Systems and methods for dynamic building material modifications

Abstract

Methods, apparatuses, and system are described herein for dynamic building material modification. An example method includes receiving sensor data from a sensor device that is associated with a first stage of a plurality of stages that form a process for a construction project and that is associated with a first portion of a building material associated with the first stage. The method includes determining one or more characteristics associated with the first portion of the building material and modifying, via a control system, one or more parameters of the building material from which the first portion of the building material is derived based on the one or more determined characteristics.

Description

SYSTEMS AND METHODS FOR DYNAMIC BUILDING MATERIAL MODIFICATIONS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present international application claims priority to U.S. Provisional Patent Application No.63 / 605,990, filed December 4, 2023, U.S. Provisional Patent Application No. 63 / 663,989, filed June 25, 2024, U.S. Provisional Patent Application No.63 / 669,054, filed July 9, 2024, and U.S. Provisional Patent Application No.63 / 670,047, filed July 11, 2024, the entire contents of which applications are incorporated by reference in their entirety. TECHNOLOGICAL FIELD

[0002] Example embodiments of the present disclosure relate generally to building materials and, more particularly, to systems and methods for dynamically modifying building materials. BACKGROUND

[0003] Building materials, such as cementitious mixtures, are widely used in the construction of structures (e.g., foundations, substructures, superstructures, tunneling, etc.). These materials may be associated with various characteristics, attributes, parameters, etc., such as compositional properties (e.g., mix ratios, microstructure), contextual material conditions (e.g., temperature, humidity, volume), and performance metrics (e.g., compressive strength, durability). Through applied effort, ingenuity, and innovation, many of the problems associated with conventional building material determination methods and systems have been solved by developing solutions that are included in embodiments of the present disclosure, many examples of which are described in detail herein. BRIEF SUMMARY

[0004] Embodiments of the present disclosure therefore provide for methods, systems, and apparatuses for dynamic building material modification. In some embodiments, a method for dynamic building material modification may include receiving sensor data from a sensor device. In some embodiments, the sensor device may be associated with a first stage of a plurality of stages that form a process for a construction project. Further, in some embodiments, the sensor data may be associated with a first portion of a building material associated with the first stage. In some embodiments, the method may include determining one or more Page 1 of 171 13893549v1characteristics associated with the first portion of the building material. In some embodiments, the method may include modifying, via a control system, one or more parameters of the building material from which the first portion of the building material is derived based on the one or more determined characteristics.

[0005] In some embodiments, the sensor device may include an input sensor configured to generate data associated with the first portion of the building material as it enters the first stage.

[0006] In some embodiments, the control system may be configured to receive the input data from the input sensor. In some embodiments, the control system may be configured to determine a target building material characteristic based on the input data. In some embodiments, the control system may be configured to modify the building material based on a difference between the target building material characteristic and the one or more characteristics.

[0007] In some embodiments, the sensor device may be an input sensor configured to generate data associated with the first portion of the building material as it exits the first stage.

[0008] In some embodiments, the control system may be configured to receive output data from an output sensor. In some embodiments, the control system may be configured to determine a target building material characteristic based on the output data. In some embodiments, the control system may be configured to modify the building material based on a difference between the target building material characteristic and the one or more characteristics.

[0009] In some embodiments, the method may include modifying, via the control system, a second portion of the building material associated with a second stage based on the one or more characteristics of the first portion at the first stage generated by the sensor device associated with the with the building material at the first stage.

[0010] In some embodiments, the method may include modifying, via the control system, a material flow rate for the building material, based on the one or more characteristics.

[0011] In some embodiments, modifying the one or more parameters of the building material may include modifying at least one of a volume of the first portion of the building material, a moisture level of the first portion of the building material, or a temperature of the first portion of the building material.

[0012] In some embodiments, the method may further include generating a summary report based on the sensor data and the modifications of the building material. In some embodiments, the method may further include transmitting the summary report to a user device. Page 2 of 171 13893549v1

[0013] In some embodiments, modifying the one or more parameters of the building material includes modifying a quantity of the first portion of the building material.

[0014] In some embodiments, modifying the one or more parameters of the building material includes modifying a quantity of the first portion of the building material and / or modifying a quantity of a second portion of the building material.

[0015] In another embodiment, a sensor for dynamic building material modification is provided. In some embodiments, the sensor may include a support member configured to support the sensor. In some embodiments, the sensor may include a sensing device configured to generate data associated with a first portion of a building material associated with a first stage of a plurality of stages that form a process of a construction project. In some embodiments the sensor may include a communication interface configured to communicably couple the sensor with a control system. In some embodiments, the sensor may be configured to transmit the data generated by the sensing device to the control system for modifying, via the control system, one or more parameters of the building material from which the first portion of the building material is derived based on the one or more characteristics.

[0016] In some embodiments, the sensor may be configured to be attached to or disposed proximate a container associated with the first stage, wherein the support member is configured to at least partially conform to the container’s shape.

[0017] In some embodiments, the container may include a silo configured to store the building material.

[0018] In some embodiments, the sensor may be configured to be radially disposed about an interior surface of the container.

[0019] In some embodiments, the sensor may include a plurality of sensors vertically adjacent to one another.

[0020] In some embodiments, the sensor may be configured to be vertically disposed inside the container.

[0021] In some embodiments, the sensor may include a plurality of sensors horizontally adjacent one another.

[0022] In some embodiments, the sensing device may be configured to generate data indicative of at least one property of the building material include at least one of a temperature, a humidity, or a volume.

[0023] In yet another embodiment, a system for dynamic building material modification is provided. In some embodiments, the system may include a sensor configured to generate data associated with a first portion of a building material associated with a first Page 3 of 171 13893549v1stage of a plurality of stages that form a process for a construction project. In some embodiments, the system may include a control system configured to communicably couple to the sensor via a communication interface. In some embodiments, the system may include a processor. In some embodiments, the system may include a non-transitory storage device containing instructions that, when executed, cause the processor to perform a variety of steps. In some embodiments, the system may receive sensor data from the sensor. In some embodiments, the system may determine one or more characteristics associated with the first portion of the building material. In some embodiments, the system may modify, via the control system, one or more parameters of the building material from which the first portion of the building material is derived based on the one or more determined characteristics.

[0024] In some embodiments, executing the instructions further causes the processor to modify, via the control system, a second portion of the building material associated with a second stage based on the one or more characteristics of the first portion at the first stage generated by the sensor device associated with the building material at the first stage.

[0025] The above summary is provided merely for purposes of summarizing some example embodiments to provide a basic understanding of some aspects of the present disclosure. Accordingly, it will be appreciated that the above-described embodiments are merely examples and should not be construed to narrow the scope or spirit of the disclosure in any way. It will be appreciated that the scope of the present disclosure encompasses many potential embodiments in addition to those here summarized, some of which will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Having thus described embodiments of the disclosure in general terms, reference will now be made to the accompanying drawings. The components illustrated in the figures may or may not be present in certain embodiments described herein. Some embodiments may include fewer (or more) components than those shown in the figures.

[0027] Figure 1 illustrates an example system for container related determinations in accordance with an embodiment of the present disclosure;

[0028] Figure 2 illustrates a block diagram of example circuitry of a control system in accordance with an embodiment of the present disclosure;

[0029] Figure 3 illustrates an example of a plurality of stages and / or substages for modifying a building material in accordance with an embodiment of the present disclosure; Page 4 of 171 13893549v1

[0030] Figure 4 illustrates examples of sensor placement for a silo container in accordance with an embodiment of the present disclosure;

[0031] Figure 5 illustrates examples of sensor placement on a conveyor belt in accordance with an embodiment of the present disclosure;

[0032] Figure 6 illustrates a detailed view and an exploded view of an example sensor in accordance with an embodiment of the present disclosure;

[0033] Figure 7 illustrates examples of sensors that transmit waves (e.g., wave-based sensors) to detect and analyze building materials in accordance with an embodiment of the present disclosure;

[0034] Figure 8 illustrates an example of a sensor for mapping a building material (e.g., spatial mapping or otherwise) in accordance with an embodiment of the present disclosure;

[0035] Figure 9 illustrates an example sensor screen used to analyze a building material as it moves through the container in accordance with an embodiment of the present disclosure;

[0036] Figure 10 illustrates a flow chart for modifying the building material from which the first portion of the building material is derived via a control system in accordance with an embodiment of the present disclosure;

[0037] Figure 11 illustrates a flowchart for modifying, using an AI model, the building material from which the first portion of the building material is derived via a control system in accordance with an embodiment of the present disclosure;

[0038] Figure 12 illustrates an example sensor coupling to the container in accordance with an embodiment of the present disclosure;

[0039] Figure 13 illustrates an example of the containers in the plurality of stages used in the construction process for modifying the building material in accordance with an embodiment of the present disclosure;

[0040] Figure 14 illustrates examples of various container types with various types of building materials or portions of building materials in accordance with an example embodiment of the present disclosure;

[0041] Figure 15 illustrates example mixer configurations with the one or more sensors installed in a variety of locations around the mixer in accordance with an example embodiment of the present disclosure;

[0042] Figure 16 illustrates one or more stages or substages used in the construction process in accordance with an example embodiment of the present disclosure; and Page 5 of 171 13893549v1

[0043] Figure 17 illustrates the control system receiving data generated by a sensor and modifying the one or more parameters of the building material in accordance with an example embodiment of the present disclosure;

[0044] Figure 18 illustrates an example of the building material transferring from a first container to a second container in accordance with an example embodiment of the present disclosure;

[0045] Figure 19 illustrates an example sensor including one or more energy harvesting systems in accordance with an example embodiment of the present disclosure;

[0046] Figure 20 illustrates an example sensor configured to be placed within a building material within a container in accordance with an example embodiment of the present disclosure;

[0047] Figure 21 illustrates example inputs and outputs associated with the control system in accordance with an example embodiment of the present disclosure;

[0048] Figure 22 illustrates an example control process optimization associated with the control system in accordance with an example embodiment of the present disclosure;

[0049] Figure 23 illustrates an example control process optimization that determines a deviation using a reference in accordance with an example embodiment of the present disclosure;

[0050] Figure 24 illustrates an example of the control system modifying adjustments based on data associated with output materials in accordance with an example embodiment of the present disclosure;

[0051] Figure 25 illustrates an example of the control system modifying adjustments based on predicted data associated with outputs in accordance with an example embodiment of the present disclosure;

[0052] Figure 26 illustrates an example of the control system modifying adjustments based on historical trends in accordance with an example embodiment of the present disclosure; and

[0053] Figure 27 illustrates an example of the control system modifying adjustments without initial target control values in accordance with an example embodiment of the present disclosure. DETAILED DESCRIPTION

[0054] Embodiments of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, Page 6 of 171 13893549v1embodiments of the disclosure are shown. Indeed, the disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Where possible, any terms expressed in the singular form herein are meant to also include the plural form and vice versa, unless explicitly stated otherwise. Also, as used herein, the term “a” and / or “an” shall mean “one or more,” even though the phrase “one or more” is also used herein. Furthermore, when it is said herein that something is “based on” something else, it may be based on one or more other things as well. In other words, unless expressly indicated otherwise, as used herein “based on” means “based at least in part on” or “based at least partially on.” Like numbers refer to like elements throughout. Definitions

[0055] As used herein, the term “project” “construction project” or “construction process” may refer to any physical construction forming part of the built environment at any stage of its existence, including but not limited to its conception, design, construction, operation, decommissioning, and demolition. Examples of a project, construction project, or construction process include but are not limited to a bridge project, building project, tunnel project, or other commercial infrastructure or industrial project. As used herein, the term “site” may refer to any self-contained location associated with the project (e.g., a construction project or construction process), or a plurality of projects. For example, a site may refer to a jobsite, a precast factory, a batching plant, and / or the like without limitation. In some embodiments as described hereinafter, the construction process may be associated with one or more stages, substages, etc. that may include one or more sites.

[0056] As used herein, an “element” may refer to any discrete portion of a project, construction project, construction process, build, or the like, at any stage of its existence, including but not limited to its conception, design, construction, operation, decommissioning, demolition, or reuse. Examples of an element include but are not limited to a column, a slab, a façade, a wall, a beam, a floor, a subfloor, a ceiling, a foundation, a mechanical assembly, an electrical assembly, a plumbing assembly, or any other element that may be used and / or built during a construction project. Further, the element may include the use of one or more building materials to create and / or construct the element and may include the discrete building materials used in its construction. In this regard, reference to an element may include, but does not necessitate, reference to the building materials used in the element’s construction. For example, when referencing data related to an element, the data may also Page 7 of 171 13893549v1include reference to the underlying building materials used to create the element. Further, the element may include the use of one or more plants, machinery, equipment and / or tools to create and / or construct the element and may include the discrete building materials, or any portion thereof, used in its construction.

[0057] As used herein, a “material test result,” “material test record,” “crush result,” or “crush record” may include data associated with a testing of a material (e.g., an element, a building material). For instance, the testing associated with a material may include the testing of the material under mechanical tests, chemical tests, physical tests, durability tests, condition-based tests, and the like. In this way, the tests may produce data associated with the material relating to how well the material performed during the test. For example, a mechanical test may yield results relating to a compressive strength of a building material, which may be recorded in a crush record or result. It is to be understood that the materials tested to produce the material test results and records may include a variety of shapes, sizes, configurations, or the like, which allow for results that may or may not be specific to the shape, size, and configuration of the material. For example, concrete in the shape of a cube, cylinder, prism, and / or the like may be used during a crush result to gather data relating to not only the shape of the concrete, but also data relating to the performance of the concrete. Further, this may be true for other tests and other building materials, without limitation.

[0058] As used herein, “embodied carbon” may refer to the total amount of carbon dioxide associated with a building material, such as related to environmental impact. In this way, the carbon dioxide may include carbon dioxide, carbon equivalents, methane, or other materials or substances that produce greenhouse gases, associated with the building material’s lifecycle, from production to transportation and installation. The embodied carbon may include the energy consumed during processes such as mining, excavation, refining, manufacturing, delivering, installing, and the like, the building material. Further, the embodied carbon may include carbon dioxide emissions that may be tangential to or otherwise related to the building material, such as embodied carbon associated with storing the building material (e.g., carbon produced by logistics associated with the storage of the building material). In this way, the embodied carbon of a building material may include the carbon footprint of the building material that is directly and indirectly related to the building material.

[0059] As used herein, the terms “first dataset” and associated “first data entries” may be used to refer to data that, in some embodiments, is received by the systems, models, etc. of the present disclosure as an input. By way of a non-limiting example, the first dataset Page 8 of 171 13893549v1and / or first data entries may include data associated with various materials properties that are input by a user, generated by a sensor device (for example, a maturity or temperature sensor), other device, received from a database, received from a prior iteration of one or more of the models described herein, and / or the like, such as in the construction resource operations described herein.

[0060] As would be evident to one of ordinary skill in the art in light of the present disclosure, the first dataset and associated first data entries may be associated with, indicative of, or otherwise related to any of the attributes, characteristics, parameters, metrics, etc. of the construction or construction operations, systems, devices, etc. described herein without limitation. Said differently, the first dataset and associated first data entries may refer to the data structure by which data associated with the embodiments described herein is stored, regardless of data type, model used, system deployed, etc. The present disclosure further contemplates that additional datasets (e.g., a second dataset or the like) may include data entries associated with any of the same or different data types described herein with reference to the first dataset. In other words, the present disclosure contemplates that any number of different datasets of any type may be used by the embodiments herein.

[0061] As used herein, the terms “sensor,” “sensor device,” “sensing device,” “transducer,” “smart device,” and “device” may be used interchangeably and / or collectively to refer to any hardware or circuitry component configured to generate data, such as first data entries, that is associated with a building material, construction resource, contextual awareness, and / or the like without limitation. As described hereinafter, a sensor device may include any relevant circuitry, components, etc. configured to generate data that is indicative of, for example, the material properties (e.g., static material properties, compositional material properties, contextual conditions, contextual material properties, etc.) of a building material. The present disclosure contemplates that each of the techniques, models, etc. of the present disclosure may be implemented with any number of the sensor and / or sensor devices and / or transducers and / or devices described herein, alone or in any combination.

[0062] As used herein, a “positioning device” may refer to as device that has capabilities (alone or as part of a system) to make positioning determinations or characterizations. A “gateway” may refer to a network connected device (e.g., Internet connected device, for example over LTE, 5G or NB-IoT) that is configured to locally communicate with beacons (over BLE, BLE Long Range, BLE Mesh, LoRa, Sigfox, and / or the like). A “beacon” may refer to a battery powered device that can send and receive wireless signals to other beacons and / or gateways and / or other devices (over BLE, BLE Long Range, Page 9 of 171 13893549v1LTE, GPRS, 2G / 3G / 4G / 5G, NB-IoT, LoRa, Sigfox and so on). In some instances, beacons may not be directly connected to the internet. In some instances, a beacon may include a cellular interface). To this end, “global position” may refer to the position of a device with respect to a global frame of reference (e.g., a latitudinal and longitudinal location) while a “relative position” as used herein may refer to a location with respect to two or more construction objects, elements, resources, and / or the like.

[0063] As used herein, a “user interface” may be a point of human-computer interaction and communication in a device that allows a user to input information, such as commands or data, into a device, or that allows the device to output information to the user. For example, the user interface includes a graphical user interface (GUI) or an interface to input computer-executable instructions that direct a processor to carry out specific functions. The user interface typically employs certain input and output devices such as a display, mouse, keyboard, button, touchpad, touch screen, microphone, speaker, LED, light, joystick, switch, buzzer, bell, and / or other user input / output device for communicating with one or more users.

[0064] As used herein, a “user device” may be a device capable of receiving an interaction from a human, a system (e.g., the system 100), a server (e.g., which may be included in a control system 200), another device, or the like. The user device (e.g., the user device 106) may include components to allow a user to interact with the user device. In this regard, the user device may include a user interface as described above. The user device may be equipped with components including, but not limited to, a processor, a memory, a storage device, an input / output device (such as a display), a communication interface, a transceiver, and the like. The components may be connected, interconnected, operatively coupled, or the like via various buses, cables, boards (e.g., motherboards), or in other manners as appropriate. In specific examples, the user device may include a mobile phone, a laptop, a computer, a tablet, a kiosk, a terminal, a scanner, a wearable, a GPS, a three dimensional printer, a smart sensor, or the like.

[0065] As used herein, an “interaction” may refer to any communication between one or more users, one or more companies or institutions, one or more devices, nodes, clusters, or systems within a distributed computing environment, as described herein. For example, an interaction may refer to a transfer of data between devices, an accessing of stored data by one or more nodes of a computing cluster, a transmission of a requested task, a transmission of a dataset, a transmission of a representation, or the like. Further, an interaction may refer to a discrete instance of an event occurring within a continuous time interval that may evolve the Page 10 of 171 13893549v1state or attribute of a construction project, building material, building material data entity, or the like. Examples of an interaction may include, but are not limited to, an operative or user driving a nail with a hammer, a tower crane liftin precast concrete, an excavator lifting a bucket of soil, a robot painting a wall, etc.

[0066] As used herein, the terms “data,” “content,” “information,” and similar terms may be used interchangeably to refer to data capable of being transmitted, received, and / or stored in accordance with embodiments of the present disclosure. Thus, use of any such terms should not be taken to limit the spirit and scope of embodiments of the present disclosure. Further, where a computing device is described herein as receiving data from another computing device, it will be appreciated that the data may be received directly from another computing device or may be received indirectly via one or more intermediary computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and / or the like, sometimes referred to herein as a “network.” Similarly, where a computing device is described herein as sending data to another computing device, it will be appreciated that the data may be sent directly to another computing device or may be sent indirectly via one or more intermediary computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and / or the like.

[0067] It should be understood that the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as advantageous over other implementations.

[0068] As used herein, “determining” may encompass a variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, ascertaining, and / or the like. Furthermore, “determining” may also include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), and / or the like. Also, “determining” may include resolving, selecting, choosing, calculating, establishing, and / or the like. Determining may also include ascertaining that a parameter matches a predetermined criterion, including that a threshold has been met, passed, exceeded, and so on.

[0069] As used herein, “wave-based sensor” may be used to refer to any device which may generate, adjust, or control a time-varying excitation (based on an input signal) and / or sense a response to an excitation including, but not limited to, of a target material, or another material coupled (directly or indirectly) to the target material. Such a wave-based sensor may be, used to generate or otherwise make use of and sense waves, excitations, and / or oscillations (such as electromagnetic waves, electric currents and / or mechanical stresses) as Page 11 of 171 13893549v1described herein. Furthermore, “wave-based” may refer to any device, technique, sensor, etc. that employs one or more actuators to excite a host material, or a second material that is coupled to the host material. The excitation may be a time varying signal (e.g., an oscillatory signal, a wave, etc.). Wave-based devices, techniques, and sensing may also employ sensors to measure the response of the host material (directly, or indirectly through the response of the second material, or another material coupled to the host material). For the avoidance of doubt, the “wave-based” techniques described herein may encompass, without limitation, excitations, oscillations, and waves, and may further encompass any device configured to take input signals and generate, adjust, control an excitation of a field, force, or form of energy, such as via an actuator defined herein, as well as a response (e.g., material response, coupled medium response, etc.) to such excitation, oscillation, or wave.

[0070] As used herein, a “container” may include any structure, vessel, or apparatus designed to hold, store, transport, or the like, building materials. Containers may be used for material storage and may include silos, bins, tanks, hoppers, mixers, tanks (e.g., water tanks or admixture tanks), funnels or the like. Containers may include conveyors, transportation containers, drums, or the like used to move material through a construction process, for example. Additionally, the containers may be used for processing a material or a building material. In this way, the container may include mixing drums, batching plant hoppers, kilns, or the like.

[0071] As used herein, a “batch” may be a predetermined about or portion of material. Further, a batch may include one or more materials or building materials that may be used to create the batch. In this way, the batch may include particular recipes that include one or more materials used in combination to create a final material (e.g., a building material). The batch may be any volume which may include batches within containers, or the like. For instance, a batch may be a volume of material inside a transportation truck or a storage silo. Further, the batch may include material stored between one or more containers, and may refer to material created using the same recipe. Further, a batch may include an associated volume and may often exist as a batch at the material manufacturer’s factory and throughout transit. Once a particular batch is pumped, the volume(s) associated with the batch may be referred to herein as one or more “pours.” A “pour” may refer to a defined volume (e.g., at least partially enclosed via a mold, formwork, or otherwise) into which at least a portion of one or more batches of a mix formulation are provided. A “pour” as described herein may be cured with the intent of forming an element of a structure (e.g., a building element). Page 12 of 171 13893549v1

[0072] As described herein, the term “batch variability” may be used to refer to the variability in the contextual material properties, the static material properties, and / or compositional material properties of a material (e.g., as defined by an associated formulation) across batches. As would be evident to one of ordinary skill in the art, batch variability may result from the tolerances or other uncertainty of the quantities (e.g., the mixing proportion tolerances), the contextual conditions during batching, and / or also the natural variability in the properties of the raw material. As such, the embodiments of the present disclosure operate to account for batch variability in the performance of the operations described herein.

[0073] As used herein, the terms “mix,” “mixture,” “composite,” and similar terms may be used interchangeably to refer to a collection of materials (e.g., portions, constituent components, constituent elements, constituent parts, etc.) that are combined together. A mixture may be homogenous in which the composition of the constituent parts are substantially uniform throughout. Alternatively, a mixture may be heterogenous in which the composition or proportion of the constituent parts varies throughout. As described hereinafter, a mix or mixture of the present disclosure may refer to a cementitious mixture (e.g., a combination of constituent components that are combined to, following curing, form concrete) as an example building material. For example, a mix may include one or more portions of a building material. The present disclosure, however, contemplates that the device, systems, methods, techniques, etc. described with reference to cementitious mixtures may be applicable to building materials, extracted materials, or industrial materials of any type without limitation.

[0074] As used herein, the terms “material formulation” and “material design” may be used interchangeably to refer to a proportion of constituent components, parts, or elements that form a portion of a material or a material. In some embodiments, the material formulation may refer to a chemical composition of constituent components, portions, parts, or elements forming the material. As described herein, for example, a cementitious material mixture may be formed of a cementitious material (e.g., Portland cement), water, aggregates (e.g., sand gravel limestone), admixtures, and / or the like. The relative proportion of these constituent components may be defined by the material formulations described herein. As described herein, the material formulation may refer to a target set of constituent component proportions of which any particular instantiation of that material formulation should be formed. In some embodiments, material designs may refer to proportions of constituent component parts associated with one or more targets for contextual material properties. In another embodiment, material designs may also include the steps (and associated timings) Page 13 of 171 13893549v1for mixing of a proportion of constituent components or raw materials. As would be evident to one of ordinary skill in the art, any particular instantiation of a material formulation may include naturally variability in the proportions of constituent components for the same material formulation.

[0075] A mix formulation, and the batches, pours, building elements, etc. associated with the mix formulation, may further include various “material properties.” The term “material property” may refer to any physical or chemical attribute, characteristic, parameter, feature, etc. of the materials described herein. The material properties of a material may include one or more of static material properties, compositional material properties, contextual conditions, and / or contextual material properties as defined hereinafter. Although described herein with reference to an example framework for distinguishing between types or categories of material properties, for example static material properties vs. contextual material properties, the present disclosure contemplates that the devices, systems, methods, techniques, etc. of the present disclosure may be applicable to any determinable, measurable, and / or derivable attribute associated with building materials, formed of cementitious mixtures or otherwise.

[0076] As used herein, the terms “static material property” and “static property” may be used interchangeably to refer to any attribute, parameters, characteristic, state, and / or the like of a material (e.g., an example building material) that is independent of the context within which the material is used (e.g., an attribute that is context independent). By way of a non- limiting example, static material properties may include density (e.g., of water or other materials), particle size, homogeneity, fineness, specific gravity, natural variability, embodied carbon data, aggregate grading, porosity, and / or the like. Although described herein with reference to example static material properties for example cementitious mixtures, the present disclosure contemplates that static material properties may include any context independent attribute of any type for any material.

[0077] As used herein, the terms “compositional material property” and “compositional property” may be used interchangeably to refer to any attribute, parameter, characteristic, state, and / or the like indicative of the proportions by which a material (e.g., a composite material as described herein) is formed of other materials (e.g., raw materials as defined herein). A compositional material property may, for example, provide an indication of the mix formulation or compositions as defined herein at various levels of granularity. By way of example, the proportional relationship of constituent components or composition may be provided as a percentage of volume, by particle number, by mass, and / or any other relevant Page 14 of 171 13893549v1metric, relationship, etc. In some embodiments, the compositional material property may, for example, be provided as an absolute mass, mass density, or other representation. The present disclosure contemplates that information associated with the compositional material properties of a particular material may be provided by any relationship, proportionality, metrics, etc. By way of a non-limiting example, a cementitious mixture (e.g., an example building material) may include compositional material properties that are representative of the atomic composition (e.g., by chemical element percentage or the like) of the building material, the compound composition (e.g., by chemical compound percentage or the like), the molecular composition (e.g., by chemical molecule percentage or the like), by raw material composition (e.g., concrete raw materials, as defined herein, or the like).

[0078] As used herein, the terms “contextual material condition,” “contextual condition,” and “context” may be used interchangeably herein to refer to any imposed state or attribute that at least partially defines the instantiated context in which a material is used. The contextual condition may, for example, be associated with various characteristics, attributes, aspects, etc. of an external environment of the material and / or may be associated with characteristics, attributes, aspects, etc. of the material. With reference to an example material, contextual material conditions may be associated with temperature data, insulation data, structural data, environmental data, structural burden data, batching plant data, pump contextual condition data, truck contextual condition data, kiln contextual condition data, temporal data, spatial data and / or the like. By way of continued example, insulation data may be indicative of a formwork type, a formwork coating, the presence or absence of blankets or other coverings. Example structural data as a contextual material condition may refer to data pertaining to the geometry, physical form, structure, layout, arrangement, configuration, and / or content (e.g., rebar or the like) of a pour. As such, the structural data may be indicative of or otherwise associated with element type data, geometry or dimensional data, exposure data (e.g., surface area of concrete exposed to air, surface area of concrete exposed to other materials, such as formwork, etc.), reinforcement geometry data (e.g., data entries associated with rebar or the like), and / or data associated with the external environment of the same. Example spatial data as a contextual material condition may refer to data pertaining to the global location (e.g., latitude, longitude and altitude), or relative location of a material at a construction site or related location (e.g., location of a pour in relation to gridlines, or another pour, or location of a precast unit in a precast yard).

[0079] As used herein, the term “contextual material property” may be used to refer to any material property that is context-dependent and that may change with differing Page 15 of 171 13893549v1contextual conditions. By way of continued example with reference to a cementitious mix as the example material, the compressive strength of the cementitious mixture may increase over time in a manner that is dependent upon temperature, geometric shape, humidity, wind, and exposure and / or the like. As would be evident to one of ordinary skill in the art in light of the present disclosure, data described herein related to contextual material properties may be time dependent and may be formed of discrete, or continuous time series data. By way of a non-limiting example, contextual material properties may refer data indicative of compressive strength (e.g., 7-day strength, 28-day strength, 42-day strength, full strength profile, etc.), shrinkage, workability, tensile strength, flexural strength, stress, strain, calibration data related thereof, structural health, reactivity, flow rate, specific surface area, and / or the like. The present disclosure contemplates that the contextual material properties described herein may include any determinable, measurable, derivable, etc. metric associated with the example material based on the intended application of the devices and systems described herein. As used herein, “target contextual material properties” may therefore refer to a set of contextual material properties that are to be achieved (e.g., within applicable tolerances or the like) by the system, users, models, etc. described herein attempts to achieve for the particular mixture (e.g., as defined by mix identifier, mix classification, mix formulation, etc.).

[0080] As used herein, the term “raw material” or “input material” may be used to refer to any material described herein that is associated with only static material properties and / or portions of a building material, as defined above. By way of a non-limiting example, water, fly ash, sand, and / or the like may be raw materials in the databases and models described herein that are associated with only static material properties (e.g., density and pH, for example). Conversely, the term “composite material” may refer to a material that is identified by both static material properties and compositional properties in the databases and models described herein. The present disclosure contemplates that the provided delineation between raw materials and composite material is in reference to the way in which these materials may be stored and / or identified by the databases and models described herein. For example, a raw material may be reclassified to a composite material whenever such material is defined to have compositional material properties. In this way, a portion of a building material may be a raw material and / or a composite material. For example, a fly ash may initially exist in the databases described herein as a raw material. The fly ash, however, may be updated to include material properties other than static materials properties, such as the atomic or molecular constituent components of the fly ash. As such, the fly ash may be Page 16 of 171 13893549v1reclassified as a composite material. In other embodiments, raw material and composite material may be interpreted by their physical or chemical meanings, namely, where a raw material is a component material used to make a product (wherein the product may be a composite material), and a composite material is a combination of two or more materials with different physical or chemical properties.

[0081] Sensor devices may be used in association with “actuators” which as used herein may be used to refer to any element or circuitry component that is able to cause, generate, adjust and / or generally control any force, field or energy excitation or disturbance (including for example mechanical excitations, or electromagnetic excitations, and in particular wave-based excitations, through force or field couplings). Said differently, the present disclosure contemplates that any element configured to or is otherwise capable of creating any form of excitation (not just movement-based excitations) may be considered an “actuator.” In some embodiments, sensor, sensor device, transducer, actuator, and device may be used interchangeably to reference any of their respective meanings, in a context dependent way. In some embodiments, an example “transducer” may be intrinsically resonating in that the configuration of the transducer (e.g., by geometry or the like) produces or is otherwise associated with resonant behaviors (e.g., oscillatory resonance, wave-based resonance modes, etc.).

[0082] As used herein, the terms “contextual awareness data,” “sensor context awareness,” “self-detection data,” and “context awareness data” may be used interchangeably to refer to data that is associated with a first sensor device considering a material, associated with the material under consideration by the first sensor device, associated with a pour implicating the material under consideration by the first sensor device; and / or associated with an environment of the material under consideration by the first sensor device. In some embodiments described hereinafter, sensor context awareness data may refer to S-data, M- data, P-data, and / or E-Data. As used herein, S-data may refer to data entries that are indicative of the sensor device itself, M-data may refer to data entries that are associated with the material surrounding the sensor device (e.g., if the sensor device is embedded) or the material under consideration by the sensor (e.g., if the sensor is directed at or mounted on the material), P-data may refer to data entries that are indicative of the pour or volume in which the sensor device is located or is considering, and E-data may refer to data entries that are associated with the environment of the pour. In some embodiments, sensor context awareness data may include combinations of these data types and / or these data types for connected Page 17 of 171 13893549v1elements (wherein a connected element represents a connection between building elements (e.g., physically connected, a nearest neighbor, or within each other’s load paths etc.)).

[0083] As used herein, the terms “Compositional Arrangement”, “Production Pathway” and “Pathway” may be used to refer to any method which evolves one or a plurality of materials from state S to state S + ∆S. For example, a production pathway may include the creation of a metal alloy from its constituent metallic elements, the chemical synthesis of a polymer, the construction of a crystalline material from its constituent parts, the conversion of a pure element into the same pure element configured with another structure, the mixing of raw materials to form a composite material, the construction of a battery from its raw constituents, etc.

[0084] As used herein, the term “Production Process” and / or “Compositional Arrangement” may be used to refer to any step, intermediate step, or plurality of steps that form part of a Production Pathway, in whole or in Part, to evolve one or a plurality of materials from state S to state S + dS or S + ∆S. For example, a production process may the mixing of raw materials to create a cementitious mix.

[0085] As used herein, the term “Raw Materials” may be used to refer to any material, in the context of a production process or production pathway or compositional arrangements, which is an input to the production process or pathway. For example, the binder may be the raw material in the batching process.

[0086] As used herein, the terms “Output Material”, “Final Material”, and “Final Product” may be used interchangeably to refer to any material, in the context of a production process, compositional arrangements, or production pathway, which is an output of the production process. For example, a fresh, curing, or hardened cementitious mix such as concrete may be the output material of a batching process.

[0087] As used herein, the terms “Intermediate Material” and “Intermediate product” may be used to refer to any material, compositional arrangement or pathway, which is undergoing a process. For example, fresh concrete being mixed in the truck may be an intermediate material as it is actively undergoing the mixing process. It should be understood that fresh concrete being mixed may in some cases also be also considered an output material since the mixing process is a continuous process converting raw materials into concrete.

[0088] As used herein, the term “Production Equipment”, may be used to refer to any apparatus or machinery which executes or is used to execute a production process, in whole or in part. For example, production equipment may refer to one or a plurality of any of the following: CNC cutter, extruders, furnace, kiln, grinding machine, batching machine, mixer, Page 18 of 171 13893549v1blenders, injection molding machine, cooling systems, chemical reactors, crystallizers, filters / separators, and more.

[0089] As used herein, the terms “Process Parameter”, “Control Parameters”, “Process Contextual Conditions”, “Process Control Parameters, “Process” and “Process Parameters” may be used interchangeably to refer to any parameters or variables that may be controlled during a production process or pathway and that may influence the production process or its output. For example, this may refer to temperature, pressure, time / duration, flow rate, pH, raw material proportions, intermediate material proportions, agitation / mixing speed, heat / convection transfer, E&M fields, and / or the like present during the process and more.

[0090] As used herein, “spatial mapping” may refer to capturing, analysing, and / or reconstructing spatial distributions and / or arrangements of a material in an environment. For example, a building material may be in a container, and a spatial map may include a reconstruction of the material within the container. The spatial map may be generated using data collected from sensors to create a representation of the material. In this way, a 2D representation or a 3D representation may be constructed using the sensor data. Spatial mapping may include detecting and recreating properties such as density, volume, composition, or the like of the material. Techniques such as wave-based sensing (e.g., ultrasound, radar, lidar, electromagnetic waves) or other imaging technologies may be used to create the spatial map, such as those described herein. Overview

[0091] As now will be described more fully herein, the present disclosure presents control systems and sensor devices for container related determinations. Figure 1 illustrates an example embodiment for control systems and sensor devices for container related determinations. It will be appreciated that the system 100 is an example of an embodiment(s) and should not be construed to narrow the scope or spirit of the disclosure. The depicted system 100 may of Figure 1 may include a network 104 communicatively coupled to one or more devices or components, such as the control system 200, a user device 106, one or more sensors 102, or the like. Further, in some embodiments, the sensors 102 may include one or more sensors. For example, sensors 102a, 102b, and 102n may be any number of sensors 102 used in the system 100. Further the control system 200 may be communicatively coupled to a database 108. Page 19 of 171 13893549v1

[0092] The user device(s) 106 may represent various forms of electronic devices, including user input devices such as personal digital assistants, cellular telephones, smartphones, laptops, tablets, desktops, and / or the like, and / or edge devices such as routers, routing switches, integrated access devices (IAD), and / or the like. In some embodiments, a user may use the user device(s) 106 to transmit and / or receive information or commands to and from the system 100, the control system 200, or the like via the network 104. Any communication between the system 100 and the user device(s) 106 may be subject to an authentication protocol allowing the system 100 to maintain security by permitting only authenticated users (or processes) to access the protected areas of the system 100, which may include servers, database, applications, and / or any of the components described herein.

[0093] The network 104 may be a distributed network that is spread over different networks. This provides a single data communication network, which can be managed jointly or separately by each network. besides shared communication within the network, the distributed network often also supports distributed processing. The network 104 may be a form of digital communication network such as a telecommunications network, a local area network (LAN), a wide area network (WAN), a global area network (GAN), the Internet, or any combination of the foregoing. The network 104 may be secure and / or unsecure and may also include wireless and / or wired and / or optical interconnection technology.

[0094] The database 108 is capable of providing mass storage for the system 100. In one aspect, the database 108 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly embodied in an information carrier. The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described herein. The information carrier may be a non-transitory computer- or machine-readable storage medium, such as the memory 206, the database 108, or memory on the processor 202.

[0095] Of course, while the term “circuitry” should be understood broadly to include hardware, in some embodiments, the term “circuitry” may also include software for configuring the hardware. For example, although “circuitry” may include processing circuitry, storage media, network interfaces, input / output devices, and the like, other elements of the server 130 may provide or supplement the functionality of particular circuitry. Page 20 of 171 13893549v1

[0096] It is to be understood that the structure of the distributed computing environment and its components, connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosures described and / or claimed in this document. In one example, the system 100 may include more, fewer, or different components. In another example, some or all of the portions of the system 100 may be combined into a single portion or all of the portions of the system 100 may be separated into two or more distinct portions. Various implementations of the system 100, including the control system 200 and user device(s) 106, and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof.

[0097] In some embodiments, the present disclosure may include methods for building material optimization. In this way, the method may include sensing one or more input materials (e.g., portions of the building material) and / or the output material. Further, in some embodiments, the method may include generating data associated with any of the materials as described herein. Further, in some embodiments, the method may include determining one or more modifications of the materials based on data received by the sensors (e.g., the sensors 102). For example, the method may include determining mix proportions based on raw material and / or input material data.

[0098] In some embodiments, and as shown in block 1002 of Figure 10, the method may a process 1000 which may include receiving sensor data from a sensor device (e.g., sensor 102), wherein the sensor device is associated with a first stage of a plurality of stages that form a process for a construction project. In this way, the first stage of the construction project may include a variety of stages used in a construction project or process. For example, as shown in Figure 16, one or more stages may be included in a construction project. In this example, an excavation stage 1602, a transportation stage 1604, a manufacturing stage 1606, and a project stage 1608 may be included. It is to be understood that Figure 16 provides an example process for a construction project and the solutions as described herein may apply to a construction project or process where there are more or less stages. Further, in some embodiments, the first stage may relate to any of the stages of a construction project and the nomenclature of “first” or “second” is not to be understood as a limiting factor with regard to the order in which the stage takes place in the construction project. For example, the first stage may relate to a stage that is later in time in the project than an example second stage. In this way, the first stage may relate to any of the stages, which may include the excavation Page 21 of 171 13893549v1stage 1602, the transportation stage 1604, the manufacturing stage 1606, the project site or pour stage 1608, or the like.

[0099] In some embodiments, the data generated by the sensor(s) 102 may include data relating to processes or parameters of the production process of a stage (e.g., themanufacturing stage 1606) or a substage (e.g., the first substage 302). A non-exhaustive listof potential data generated by the sensors 102 considered by the present disclosure may include compositional arrangements (e.g., proportions of each material from which a material mix is formed), lists of steps, state machines, lists of equipment, states, state change operators, chemical reactions, and / or the like. In some embodiments, this data may be considered by the control system 200. In some embodiments, the data generated by the sensors 102 may be used to determine material compliance against a third party standard (e.g., ASTM standards, or the like).

[0100] Further, in some embodiments, the data generated by the sensor(s) 102 may include parameters relating to the portion of the building material or the building material. A non-exhaustive list of potential building material parameter data may include material contextual conditions data (e.g., durability, pumpability, workability, aesthetic finish, flexural strength, exothermicity, endothermicity, and / or the like), sensor context awareness data (e.g., sensor metadata (e.g., name of a sensor), sensor context awareness data as described in other sections of the present disclosure (e.g., location of a sensor (relative and absolute), orientation of a sensor, rf transmission of a sensor, battery consumption of a sensor, lifetime of a sensor since activation, and / or the like), and / or the like), process contextual conditions (e.g., process control parameters during a material production process (e.g., temperature, pressure, rate of mixing, and / or the like), and / or the like.

[0101] Additionally, or alternatively, in some embodiments of the present disclosure, sensor devices may be used to collect data associated with production processes as they occur. Additionally, or alternatively, data from records associated with production pathways and / or processes may be used to collect data associated with production processes. Additionally, or alternatively, sensor data and / or records data may be used to train any model herein. Additionally, or alternatively, sensor data and / or records data may be used for validation of an output of any of the models herein. Additionally, or alternatively, sensor data and / or records data may be used to improve model accuracy. Additionally, or alternatively, sensor data and / or records data may be used to collect data associated with any one or plurality of the following: material identifiers, material compositions, material properties, material states (e.g., as represented by node, edge, variable functions, vector, or otherwise), Page 22 of 171 13893549v1production process, control parameters, production process equipment. In some embodiments, any of the models herein may be able to simulate a 4D Spatio-Temporal Simulation (e.g., digital twin) of a production pathway.

[0102] In some embodiments, the prediction may be based on sensor data generated by sensors monitoring any or all of the following during the process themselves: raw materials, intermediate materials (during and in between processes), equipment involved in each process, control parameter values, and / or the like. Further, the output of a production process may be predicted based on live sensor data, versus predicting the output of a production process theoretically. Additionally, or alternatively, training data may include all production processes data objects described herein. Further, models may be trained to create associations between raw materials and processes, and output materials.

[0103] In some embodiments, the sensor data may be associated with a first portion of the building material associated with the first stage. In some embodiments, the first portion of the building material may include any portion of the building material. In this way, the first portion may include one or more materials used to create the building material. Further, the portion of the building material may include one or more constituent elements or parts of the building material. Additionally, in some embodiments, a portion may be a material constituent. In some embodiments, a portion may be a part of a material constituent. For example, a portion may be a portion of cement in a container.

[0104] In some embodiments sensors 102 may be used to gather data for training the models herein. This may include compositional properties and data, structural data, as well as material property and performance data. In some embodiments, sensors may be used to construct a labeled training dataset of material identifiers, compositional data, structural data, and properties and performance data. This may be used to train models (e.g., AI model 208) to predict properties based on composition and structure, as well as generate materials based on desired properties. Additionally, or alternatively, sensor devices may be used to construct dynamic datasets that consider contextual conditions of materials in their environments of use. Additionally, or alternatively, training datasets may continuously grow as more data is collected. Additionally, or alternatively, models may be retrained based on additional sensor data. In some embodiments, sensor devices may be embedded in, mounted on, or directed at a material to gather data associated with the material.

[0105] In some embodiments, compositional data and / or structural data may be gathered using sensors configured for X-Ray Diffraction, Electron Microscopy, Neutron Diffraction Atomic Force Microscopy, Hyperspectral imaging, FTIR spectroscopy, Raman Page 23 of 171 13893549v1Spectroscopy, LIBS Spectroscopy, Diffuse Reflectance Spectroscopy, and other high frequency wave-based sensing methods, or photonics or spectroscopic methods. In some embodiments, the methods are used to gather compositional or structural data associated with building material composition and lattice structures.

[0106] Additionally, or alternatively, sensors 102 may be used to collect data associated with the material in real-time during production, synthesis, or use of the material. Additionally, or alternatively, sensors may be used to collect data throughout the material life cycle, monitoring the raw materials, throughout the production process, all the way to the material during use. Additionally, or alternatively, live contextual conditions such as temperature, pressure, geometry, time / aging and more may be collected alongside sensing of the material itself. Additionally, or alternatively, contextual conditions may be linked to associated sensor data such as sensor time series data e.g. the temperature during a particular time, date and location, may be linked to the material sensor data collected at that time, date and location. Additionally, or alternatively, contextual condition data may be added to the training dataset / database. Additionally, or alternatively, contextual condition data may be linked to associated material composition, structure and / or property data in the training dataset. Additionally, or alternatively, contextual conditions may be extracted from APIs (e.g., weather API), records (e.g., BIM Model for geometry), human input or other sensors (e.g., thermometer, barometer). Additionally, or alternatively, contextual conditions may be determined or collected using context awareness methods. Additionally, or alternatively, contextual conditions may be determined or collected using cameras or other image-based sensing devices or methods. Additionally or alternatively, the models and / or methods herein may use context awareness data and / or methods to normalize property data with respect to contextual conditions. Additionally or alternatively, the models and / or methods herein may use context awareness data and / or methods to convert a generated data associated with one set of contextual conditions to another set of contextual conditions.

[0107] In some embodiments, material data may be gathered using wave-based sensing devices. In further embodiments wave-based sensing devices may include mechanical wave or oscillation-based devices. The devices may include 1-port and 2-port systems, configured, for example, for ultrasonic pulse velocity measurements, mechanical impedance devices, piezoelectric transducer-based devices, optomechanical sensor devices, electromechanical devices, and more. The devices may be used to measure any mechanical property including those listed herein, at one or multiple frequencies. Additionally, or alternatively, further embodiments of wave-based sensing devices may include Page 24 of 171 13893549v1electromagnetic wave or oscillation-based sensing devices. Including devices configured to sensor materials using electrical currents, magnetic fields, or electromagnetic waves (e.g., Hall effect sensors, resistivity probes and more). Additional sensor devices may include Ellipsometry, UV-Vis Spectroscopy, Photoluminescence Spectroscopy, Refractive Index Sensors, and / or the like. The sensor devices may be used to measure any electromagnetic property including those listed herein.

[0108] Additionally, building material data may be gathered using types of sensors or sensor techniques including thermal sensors (e.g., thermocouples, differential scanning calorimetry (DSC), thermal conductivity sensors, thermogravimetric analysis (TGA), and / or the like), pressure and strain sensors (e.g., pressure sensors, strain gauges, piezoelectric pressure sensors, and / or the like), and / or other sensors (e.g., pH sensors). Additionally, or alternatively, the sensor data collected by sensor devices may be processed and cleaned. In some embodiments, processing may include a Fourier transform, or derivatives or integrals of the frequency spectra obtained through the sensor.

[0109] Additionally, or alternatively, sensors may be used to collect data associated with the material in real-time during production, synthesis, or use of the material. Additionally, or alternatively, sensors may be used to collect data throughout the material life cycle, monitoring the raw materials, throughout the production process, all the way to the material during use. Additionally, or alternatively, live contextual conditions such as temperature, pressure, geometry, time / aging and more may be collected alongside sensing of the material itself. Additionally, or alternatively, contextual conditions may be linked to associated sensor data such as sensor time series data e.g. the temperature during a particular time, date and location, may be linked to the material sensor data collected at that time, date and location. Additionally, or alternatively, contextual condition data may be added to the training dataset / database. Additionally, or alternatively, contextual condition data may be linked to associated material composition, structure and / or property data in the training dataset. Additionally, or alternatively, contextual conditions may be extracted from APIs (e.g., weather API), records (e.g., BIM Model for geometry), human input or other sensors (e.g., thermometer, barometer). Additionally, or alternatively, contextual conditions may be determined or collected using context awareness methods. Additionally, or alternatively, contextual conditions may be determined or collected using cameras or other image-based sensing devices or methods.

[0110] Additionally, or alternatively, in some embodiments, sensor data may be used for model calibration or validation. Additionally, or alternatively, materials may be sensed Page 25 of 171 13893549v1using one or a plurality of sensor devices after a prediction has been made. Additionally, or alternatively, the sensor data may be compared with the output prediction. Additionally, or alternatively, model parameters or hyperparameters may be adjusted when predictions do not match sensor data.

[0111] Additionally, or alternatively, sensor data may be used for real-time material and material production monitoring and feedback. Additionally, or alternatively, a production process may be dynamically optimized based on sensor data. Additionally, or alternatively, this may include changing the contextual conditions of a production process to optimize the production process output e.g. changing temperature, pressure, or doping level, to achieve desired properties. Additionally, or alternatively, sensor data may be used to detect defects or impurities in materials from a production process, and dynamically change one or a plurality of aspects of the production process in response.

[0112] Further, in some embodiments, and as shown in block 1004 of Figure 10, the method may include determining one or more characteristics associated with the first portion of the building material. In some embodiments, the characteristics may relate to the material properties of the material, as described herein. Further, the characteristics may relate to compositional properties of the material, as described herein. Further still, the characteristics may relate to property distributions of the material, as described herein. It is to be understood that the characteristics may include characteristics relating to any aspect of the material, portion of the building material, or building material. In a specific example, the characteristics may relate to the aggregates used in the portion of the building material and may relate to size, grading, distribution, shape, surface texture, density, elastic modulus, crushing strength, durability, thermal properties, or the like.

[0113] A non-exhaustive list of potential characteristics or material properties considered by the present disclosure may include static material properties including density, homogeneity, average particle size (e.g., average aggregate particle size or aggregate grading, average binder particle size, and / or the like), fineness, specific gravity, natural variability data (e.g., data about natural variability in raw materials quality or inhomogeneity, and natural batch variability), embodied carbon data, aggregate grading, porosity, and / or the like. Further the non-exhaustive list of material properties may include contextual material properties including temperature data, insulation data (e.g., formwork type, formwork coating, blankets, and / or the like), structural data (e.g., data pertaining to geometry, physical form, structure, layout, arrangement, configuration, and / or content (e.g. rebar) of a pour, such as element type data, geometry or dimensional data, exposure data (e.g., surface area of Page 26 of 171 13893549v1concrete exposed to air, surface area of concrete exposed to other materials (e.g. formwork), and / or the like), reinforcement geometry data (e.g., rebar data, information about general surroundings, and / or the like), and / or the like)), environmental data (e.g., meteorological data (e.g., ambient temperature data, humidity data, precipitation data, wind data, storms and lightning data, and / or the like), electromagnetic radiation data, mechanical vibration and / or other mechanical disturbances, geological data (e.g., the type of soil surrounding foundations may affect its behavior), oven data, and / or the like), structural burden data (e.g., how instantiated mix experiences the following : load data, load path data, stress data, strain data, and / or the like), batching plant data (e.g., volume of batch, mixing data, measure of mixing intensity (e.g., rate of rotation), and / or the like), pump contextual condition data, kiln contextual condition data (e.g., temperature inside kiln, raw materials inside kiln, volume of materials inside kiln, and / or the like), temporal data (e.g., any information used to denote a time or timeframe in absolute terms, or relative context dependent terms (e.g., date and / or time, period of time (e.g., 5 days), period of time specifically between two dates or between two hours of the day, season, year, daytime or nighttime, stage of construction, time stamp data (e.g., associated with sensor measurements), date stamp data (e.g., associated with sensor measurements), and / or the like), and / or the like.

[0114] Further, in some embodiments, the data received may include varying data types used to transmit information to the control system 200. For example, data types of the present disclosure may include any of the discrete data and / or continuous data as described herein. In some embodiments, data sources of the present disclosure may include recorded values from measurement or sensors devices, documents (digital or handwritten), human input (e.g., via forms, surveys, filling out data fields on digital platforms, and / or the like), insights derived from the execution of any models stored on a digital platform, data and insights accessed via external organizations or services (e.g., a weather forecasting service accessed via API), and / or the like. Data formats used by embodiments of the present disclosure may include PDF, Word, PNG, JPEG, BIM (e.g., IFC), CAD (e.g., STEP), Revit (e.g., RVT), Floorplan data (e.g., as pdf or image), and / or the like. Data methods used by embodiments of the present disclosure may include extracting data and / or inferring data (e.g., extracting / inferring constraints from BIM and / or schedule information), data manipulation, data storage (e.g., in a database), data linkage, data transmission (Bluetooth, Wi-Fi, 3G, 4G, 5G (or any future G’s), Bluetooth low energy (BLE), BLE long range, LoRa, Sigfox, Mesh networks, and / or the like), and / or the like. Page 27 of 171 13893549v1

[0115] Further, in some embodiments, and as shown in block 1006 of Figure 10, the method may include modifying, via a control system (e.g., the control system 200), one or more parameters of the building material from which the first portion of the building materials is derived based on the one or more determined characteristics. In some embodiments, the parameters may include the mix recipes, the materials used during the process, the process conditions, and / or the like. For example, the parameters may include the amount or grading of particular aggregates in a particular batch. Further, in another example, the parameters may include the temperature at which a particular container is set to, which may be adjusted. Further, in another example, adjusting the parameters may include adjusting a rotation speed of a mixing paddle associated with a mixer. In this way, the control system (e.g., control system 200) may modify the one or more parameters by adjusting the processes associated with the stage, substage, and / or the like. For example, the control system 200 may modify parameters of a material by adding one or more portions (e.g., aggregates, water, admixtures) to the building material. In this way, the addition of the portions may alter the characteristics of the building material. Further, the control system 200 may adjust the processes of the stage, which may include processes relating to mixing velocity, pressure, temperature, drying, and the like. Further, in some embodiments, modifying the one or more parameters of the building material may include modifying at least one of a volume of the first portion of the building material, a moisture level of the first portion of the building material, or a temperature of the first portion of the building material.

[0116] Further, in some embodiments, modifying the one or more parameters may include creating one or more new parameters used in the process. For example, the one or more parameters may be created by the control system 200 based on data gathered from the one or more sensors 102. In this way, the control system 200 may determine that the parameters should be updated to include new values. For example, the control system 200 may generate one or more parameters associated with a mixing speed of a particular mixing spindle that may be used in the current or future batches. In another example, the control system 200 may generate parameters associated with a new batch mix recipe based on characteristics of a batch associated with a project site, wherein the new batch mix recipe includes one or more additional materials (e.g., building materials) that may or may not have been used previously. In this way, the generation of the one or more parameters may include creating new processes that the control system 200 may use to generate batches of material.

[0117] Further, in some embodiments, modifying the one or more parameters of the building material may include modifying a quantity of the first portion of the building Page 28 of 171 13893549v1material. In this way, the modifications may modify a quantity, which may include a volume, weight, or the like, of the portion of the building material. For example, the control system 200 may modify the quantity by dispensing the quantity of the first portion of the building material from a container. Further, in some embodiments, modifying the quantity of the first portion of the building material may include modifying, adjusting, creating, or altering the properties of the quantity. In some embodiments, the quantity may be the entirety of the first portion. In some embodiments, the quantity may be the entirety of the building material. In some embodiments, the quantity may be less than the first portion or may be less than the building material.

[0118] Further, in some embodiments, the control system 200 may also be capable of generating new material compositional arrangements and / or process parameters (e.g., generating parameters for the batching plant) based on the parameters provided by the user. For example, the control system 200 may create a new building material, which may be included in the modification block 1006 of Figure 10. These newly generated formulations are evaluated and included in the pool of candidate options, where appropriate. Candidate formulations may be ranked based on a customizable utility function. This may allow users to prioritize different factors, such as cost-effectiveness, sustainability, or performance, according to their specific needs. Once a suitable formulation is selected, a user may proceed and enter into a commercial agreement for the procurement of the material with a chosen supplier through the platform.

[0119] Embodiments of the present disclosure may be configured to generate material and / or design materials. In some embodiments, the models considered by the present disclosure may generates one of or a plurality of material compositions based on target objectives. In such embodiments, the models described herein may be configured to discover materials of a certain type, a certain composition, a certain structure, and / or the like.

[0120] Further, in another example, the present disclosure may provide a solution that determines mix proportions based on output material data. In this way, the output material data may include data associated with material that has passed through the construction process, or at least a portion of the construction process (e.g., through one or more stages). For example, the output material data may include a fresh mix in a batch mixer, in a truck, at the pour site, at the project site, or the like as well as a hardened mix and / or a mix at any stage in between the fresh and hardened stage (e.g., curing stage).

[0121] Further, in yet another example, the present disclosure may provide a solution that determines mix proportions based on a model (e.g., an AI model 208, ML model, or the Page 29 of 171 13893549v1like) trained to predict output material behavior based on the input material data. In this way, the model may be trained to make predictions based on material properties of the building material 101 as it passes through the construction process. For example, in some embodiments, the AI model 208 may make one or more predictions associated with the one or more parameter modifications. In this way, sensor data may be used as an input for models that predict future expected properties of materials based on historical performance data. Additionally, or alternatively, models may use multimodal data from a plurality of sensors and sensor types, either during training, or model execution. In some embodiments, property prediction and simulation may be undertaken using any models including, but not limited to, linear models and regression models, decision trees and ensemble models, support vector machines, neural networks, gaussian process regression, physico-chemical models, hybrid models, and / or the like.

[0122] Embodiments of the present disclosure may be configured to simulate materials, generate material predictions, and generate parameter modifications for the control system 200. In some embodiments, the models considered by the present disclosure may predict properties of a material in target contextual conditions. In such embodiments, the models described herein may generate predictions based on material compositional data. Additionally, or alternatively, the models described herein may generate predictions based on property data of another type (or partial property data). Further, the models described herein may generate predictions based on historical properties or compositional data of the same material or another material. In some embodiments, the models herein configured as a prediction model may generate predictions based on evolutions of material properties over time and / or space for given contextual conditions.

[0123] In some embodiments, the models considered by the present disclosure may be configured to predict material compositional data. In such embodiments, the models may generate predictions based on compositional data of another type (e.g., structure from chemical formulation). Additionally, or alternatively, the models may generate predictions based on property data (e.g., to identify material or instantiation of material). Additionally, or alternatively, the models may generate predictions based on prior compositional data or property data. Additionally, or alternatively, the models may generate predictions based on evolutions of compositional data over time and / or space.

[0124] In some embodiments, the models considered by the present disclosure may be configured to predict composition and / or properties of a material that undergoes a production process based on the production process (e.g. batching process) and / or the Page 30 of 171 13893549v1material compositional arrangement (e.g. recipe) and / or raw material data (e.g. attribute data from sensors), and / or performance data on output material (e.g. strength development of concrete pour). In such embodiments, control parameters of processes and / or process contextual conditions may be input into the model for prediction. In some embodiments, the models may be configured to predict properties of a known material. In some embodiments, the models may be configured to predict compositions of output materials. Additionally, or alternatively, the models may generate predictions based on sensor data monitoring the production process.

[0125] In some embodiments, the models considered by the present disclosure may be configured to predict attributes of production processes, based on representations of processes. In such embodiments, a model may predict, determine, and / or estimate attributes of production processes (e.g., cost, carbon, time efficiency, and / or the like) based on a representation of the production process, which may include material data about raw materials, intermediate material, output materials, the production pathway and processes themselves, control parameters, and / or equipment used.

[0126] Further, in some embodiments, the methods and / or models herein may be used to predict the properties of one or a plurality of building materials. Additionally, or alternatively, the predicted properties may be associated with a set of contextual conditions for the building materials. Additionally, or alternatively, the method may include the use of a machine learning model. Additionally, or alternatively, the machine learning model may be a neural network. Additionally, or alternatively, the neural network may be a graph neural network. Additionally, or alternatively, the model method may include the use of a physico- chemical model.

[0127] In some embodiments, and as shown in process flow 1100 of Figure 11, the model may be trained using the data received from the sensors 102 (e.g., as shown in block 1102) and based on determining the one or more characteristics of the first portion of the building material (e.g., as shown in block 1104). In some embodiments, embodiments of the present disclosure may include a plurality of types of training models. The following are broad categories of training procedures that may be, in some embodiments, used to train any of the models described herein (e.g., to modify the internal weights relative to an objective function).In some embodiments, human choice may be a training procedure used to train any of the models described herein. Further, in some embodiments, gradient descent may be a training procedure used to train any of the models described herein. Further, in some embodiments, reinforcement learning may be a training procedure used to train any of the Page 31 of 171 13893549v1models described herein. In some embodiments, reinforcement learning with human feedback (RLHF) may be a training procedure used to train any of the models described herein. In some embodiments, a genetic search may be a training procedure used to train any of the models described herein.

[0128] Further, in some embodiments, the method may include a plurality of input features. For example, in some embodiments, a large language model (LLM) may be configured to interact with a user. An example interaction may be the user inputs a prompt and the LLM ingests the prompt. In some embodiments, ingestion of input by any of the models described herein may be in a plurality of different formats including sensor data (e.g., spectroscopic data, microscopy data, and / or the like), digital records (e.g., a batching record), direct user input, and / or the like. Additionally, or alternatively, embodiments of the present disclosure may include a generative data engine configured to generate missing data in the event data is missing. In such embodiments, the generative data engine may be used in training models or execution models.

[0129] Additionally, or alternatively, embodiments of the present disclosure may employ transfer learning. In such embodiments, models may use transfer learning to train one model to become effective at another type of process (e.g., teach a specialist model to predict conductivity of crystals, to become effective at predicting conductivity of electrolytes, and / or the like). In some embodiments, a model at the control system 200 may be used in the employment of the transfer learning. For example, a common model may be trained on building material data and models at batching plants may be transferred the learning from the common model.

[0130] Further, in some embodiments, the AI model 208 may be trained with a target building material characteristic, as shown in block 1106. In this way, the target building material characteristic may be a target material the control system 200 and the AI model 208 may create. For example, the target building material characteristic may include specific mix recipes and / or test results used to create a batch recipe.

[0131] In some embodiments, the model (e.g., AI model 208) may be trained on material data which forms a training dataset. In some embodiments, the training dataset may include a target building material characteristic and / or a target material. Additionally, or alternatively, the training dataset may include material identifiers, composition data, structural data, property data, and contextual conditions data associated with the material or plurality of materials under consideration. This may include any and any combination of the embodiments for composition data, structure data, and / or property data described herein. Page 32 of 171 13893549v1Additionally, or alternatively, the training dataset may be labeled or unlabeled. Additionally, or alternatively, the training dataset may include data in different representations, formats, and from different data sources including but not limited to the representations (e.g. Voxel, Graph-based, and / or the like) and formats (pdf, png, csv, and / or the like), and data sources listed elsewhere herein. Additionally, or alternatively, some embodiments of data sources may include sensor data (e.g., XRD, electron microscopy, spectroscopy, hyperspectral imaging, wave-based sensing, thermal sensing, and / or the like) record data, data gathered from materials databases, simulated data (e.g. APIs for weather data). Additionally, or alternatively, multimodal data fusing methods may be used to combine data in different representations, formats and from different data sources. Additionally, or alternatively, in some embodiments, the training method may include feature engineering, wherein relevant features are extracted and prepared from raw data to construct relevant features for ingestion by the model. This may include fingerprints and descriptors for example, such as atomic location in the unit cell, bond angles, and other composition data or structure data, or combination of any composition and / or structure data. In some embodiments, the training method may comprise splitting the training dataset into training data and testing data. Additionally, or alternatively, the training data is split into inputs, and outputs.

[0132] In some embodiments, the models may be trained with data generated from the one or more sensors 102. For example, parameters or contextual condition data may be added to the training dataset or database. Further, contextual condition data may be linked to associated material composition, structure, and / or property data in the training dataset. Additionally, or alternatively, contextual conditions may be extracted from APIs (e.g., weather API), records (e.g., BIM Model for geometry), human input, and / or other sensors (e.g., thermometer, barometer, and / or the like). In some embodiments, contextual conditions may be determined or collected using context awareness methods. Additionally, or alternatively, contextual conditions may be determined or collected using cameras or other image-based sensing devices or methods.

[0133] In some embodiments, the models herein may be trained via data generated from simulations. These simulations may be chemical simulations, in some embodiments. In one embodiment, material simulations may be executed on a quantum processor, and / or using a quantum algorithm, and the output of those simulations may be used to train any of the models described herein, or as an input to any of the models described herein. Additionally, or alternatively, simulations and / or predictions of the models herein may be done on quantum processors and / or using quantum algorithms. Page 33 of 171 13893549v1

[0134] Embodiments of the present disclosure may use sensors 102 for training and / or data input. Further, the sensors may include any of the sensor devices as described herein. Additionally, or alternatively, embodiments of the present disclosure may be configured to digitize records and / or parse the records using AI (e.g., test records) for training data. In some embodiments, the models herein may be configured to prompt for requirements and configured to limit scope of discovery. Additionally, or alternatively, the models described herein may account for contextual conditions. In some embodiments, the models described herein may be AI models that may include physico-chemical embeddings.

[0135] In some embodiments, training of the models described herein may be based on real sensor data (e.g., spectroscopy for composition, wave-based more broadly for properties, and / or the like). Further, training of the models described herein may account for contextual conditions (e.g., use context awareness to monitor the contextual conditions of the material sensed). In some embodiments, training dataset may include multimodal data that may be sourced from material databases, sensor data, documents & records data (e.g., compliance testing for materials), and / or the like. Additionally, or alternatively, the models described herein may undergo prompt training (e.g., training to create mapping between text embedding and prompt). Embodiments of the present disclosure may include a plurality of learning techniques for training such as reinforcement learning, where an ML agent interacts with [x] to understand how changes in [x] affects the properties of the generated material and a reward is generated associated with how closely the output property matches the desired target. In some embodiments, [x] may be composition, [x] may be atomic geometry, [x] may be a proportioning of raw materials, [x] may be a production process, and / or the like.

[0136] In some embodiments, the models described herein may be configured to ingest prompts as inputs. Further, the models described herein may be configured to converse with users and generate and ask clarifying questions when required. Additionally, or alternatively, the models herein may extract relevant information from prompts, and represent this information in “material space.” Further, the models herein may extract desired characteristics, which may be turned into an objective function. In some embodiments, the models herein may use token and transformer-based architecture, where a user may input a prompt, the prompt may be tokenized, the token may be embedded into a vector space, the token space may be mapped to material space, and / or the vectors of material space may be ingested by a generative model. Further, embodiments of the present disclosure may be multimodal models. Page 34 of 171 13893549v1

[0137] In some embodiments, training datasets may include production pathways properties such as carbon emission from processes. Further, the model may learn such associations. For example, for carbon emissions the model may be trained to understand chemical reactions happening as part of the production process and may infer that carbon (or other greenhouse gases) may be emitted as byproduct of a reaction (e.g., may learn to predict how much CO2 is generated by combustion of different substances). Additionally, or alternatively, the model may be trained to understand energy consumption of a particular type of equipment and may learn how to predict or infer it for similar equipment used analogously (e.g., may convert energy use to carbon emissions). Further, these steps may be similarly performed for cost and time duration. In some embodiments, the model may sense the end material to understand yield.

[0138] In some embodiments, a LLM and / or GPT may be used to infer this information based on a prompt that describes the production process. Additionally, or alternatively, the models may be based on quantitative physico-chemical understanding of processes. Additionally, or alternatively, the models may extract equipment characteristics from specification. Further, the models herein may use hybrid physics and data-based models. In some embodiments, the model may predict total production pathway properties, by breaking down the pathway into its constituent processes, and making predictions for each production process individually. Further, the model may then sum all of the individual predictions to understand the cost, carbon, time, and / or the like of the total process.

[0139] In some embodiments, a recommendation may be generated by the AI model to modify the building material with the control system, as shown in block 1108. In this way, the AI model 208 may recommend the control system 200 to modify one or more parameters of the process to achieve the target building material characteristic.

[0140] Additionally, or alternatively, embodiments of the present disclosure may be configured to simulate materials over time with varying contextual conditions, allowing models to predict and / or simulate how materials behave under dynamic conditions (e.g., temperature cycles, mechanical vibrations, stress changes over time, and / or the like). In this way, the simulations may be used to recommend modifications of one or more parameters.

[0141] Additionally, or alternatively, embodiments of the present disclosure may include PINNs (physics infused neural networks) for material modeling. In one embodiment, physical constraints may be represented by differential equations that may be introduced into the loss function of a neural network (e.g., the model may be constrained to adhere to conservation of mass, energy, and / or charge). In some embodiments, relationships such as Page 35 of 171 13893549v1Hooke’s Law, Heat Conduction Laws, Conservation of Energy, Phase Stability Conditions, Fourier’s Law, and / or the like may be incorporated in the loss function. In such embodiments, the loss function may be defined as: Total Loss = Data Loss + ^^*Physics Loss. In this equation, Data Loss may be a measure indicative of the error between model predictions and real-world observations, Physics Loss may be a measure indicative of a degree of violation of the laws of physics (e.g., stress-strain relationships, Navier-stokes, and / or the like), and ^^ may represent the weighting of physics loss with respect to the data loss.

[0142] Additionally, or alternatively, the models herein may use predictive model to predict properties of generated material. Additionally, or alternatively, the models herein may simulate generated materials in intended use cases. In some embodiments, simulations may be computationally draining. In such embodiments, to be more computationally efficient, simulations may be performed on subsets of generated materials (if in discovery mode). Further, if a material reaches threshold of proximity to desired property (e.g., as computed through determination of objective function), then the models here may simulate the material. In some embodiments, the models herein may recalculate objective function after simulations (if the result is better or worse than expected).

[0143] Material productions (e.g., modifying or creating a building material at a batching plant, or the like) considered in the present disclosure may be configured to employ any of or any combination of data object types as described herein. A non-exhaustive list of data objects that may be used in any material production herein may include materials (e.g., raw materials, intermediate materials, output materials, final product, and / or the like), processes (e.g., may be defined as anything that makes a material evolve from state S to state S+∆S), production processes (e.g., may be defined as a process with the goal of producing a final product or target material), equipment (e.g., machinery and / or apparatuses which may execute or may be used to execute a process), control parameters (e.g., controllable external parameters of a process), and / or the like.

[0144] Additionally, or alternatively, material productions considered in the present disclosure may be configured to employ any of or any combination of models as described herein. A non-exhaustive list of models that may be used in any material production herein may include a model configured to predict composition and / or properties of a material that goes through a production process based on the production process and the raw materials (e.g., Raw Materials + Process → Output Materials), a model configured to evaluate a set of production processes (or aspects of a production process) against a set of target objectives, and output an evaluation identifier (e.g., Process + Evaluation Parameter → Process Page 36 of 171 13893549v1Evaluation and / or Processes + Target Objectives → Processes Ranking), a model configured to optimize one of or a plurality of production processes based on target objectives and material output (e.g., Process + Target Objective → Optimized Process), a model configured to generate one of or a plurality of material production processes based on a desired material output (e.g., Desired Material Output → Process), and / or the like.

[0145] In some embodiments, the models considered by the present disclosure may generate one of or a plurality of material production processes based on a desired material output. In some embodiments, objectives and / or desired attributes of the process itself may include length of the process, carbon emission of the process, cost of the process, and / or the like. Additionally, or alternatively, the models may generate one of or a plurality of material production processes based on target objectives (e.g., the process should make the material as pure as possible, minimize greenhouse gas emissions of production process, and / or the like). In some embodiments, the models here may include constraints such as constraints on raw materials, constraints on set of equipment or what production processes are possible, and / or the like. Additionally, or alternatively, embodiments of the present disclosure may transmit production process as instructions to an autonomous laboratory.

[0146] In some embodiments, the models herein may be configured to optimize one or a plurality of production processes based on target objectives and material output. As an example, such a model may have the formalism of Process + Target Objective → Optimized Process.

[0147] In some embodiments, the production process optimization models herein may include graph network representations of production processes. In such embodiments, the models may have initial material(s) state, and final material(s) state, as well as any intermediate nodes and edges. Further, the models may determine all possible paths to reach end node from starting node. Additionally, or alternatively, the models may predict properties of each path and may compare to objective function to find optimal path. Additionally, or alternatively, the model may compute action functional in path integral framework, and path which minimizes action functional may be optimal.

[0148] In some embodiments, the optimization models may use prediction models to determine properties or outputs of production processes, to then compare it against objective functions and move in search space. In such embodiments, physics-based models and data model hybrids may be used. In some embodiments, optimization algorithms may include linear programming methods, mixed-integer programming methods, genetic algorithms, Page 37 of 171 13893549v1simulated annealing, and / or the like. Additionally, or alternatively, the models may include multi-objective optimization (e.g., Pareto optimization).

[0149] In some embodiments, the model may include real time sensor based optimization. In such embodiments, raw materials, processes, control parameters, equipment, and output material may be monitored in real time. Further, output material may be monitored directly after the process, as well as in use to measure performance. In such embodiments, the production process may be optimized in a number of ways in real time, including adjusting raw materials, adjusting processes, adjusting control parameters, adjusting equipment, and / or adjusting any combination of the above.

[0150] In some embodiments, reinforcement learning and / or adaptive learning may be used to enable model to learn best optimization strategies. Additionally, or alternatively, the model may be able to explain the optimization it has generated for a particular process. Additionally, or alternatively, dynamic weighting may be used for objective functions.

[0151] In some embodiments, the models herein may be configured to generate one or a plurality of material production processes based on a desired material output. As an example, such a model may have the formalism of Desired Material Output → Process. In such embodiments, the desired output may be a material composition, target material property or performance, desired use case, and / or the like. Additionally, or alternatively, graph networks of the production process may be generated. Additionally, or alternatively, path integral formalism of the production process may be generated.

[0152] In some embodiments, entirely novel pathways may be generated. For example, this may be accomplished via the model’s fundamental quantitative understanding of physics, chemistry, chemical processes, as well as manufacturing processes and other processes. Further, novel pathways may be generated based on energy representation described herein. Additionally, or alternatively, LLM based methods may be used to generate new pathways.

[0153] In some embodiments, a list of production pathway generation algorithms may include, for example, search tree exploration (e.g., start with one or a plurality of raw materials nodes, generate edges for every process that can match the raw material(s), alongside new nodes, predicting the outputs of these production processes, recursively apply algorithm for new nodes, to generate path until satisfactory material is reached which matches desired objective and / or desired property and / or desired use case), reverse of the previous (e.g., in situations where the process starts with a desired material composition, generate processes that could produce the final material, and work backwards recursively), and / or the Page 38 of 171 13893549v1like. Additionally, or alternatively, heuristics and / or rules may be included, which may be based on physical or chemical laws, for example, to discard certain pathways to avoid explosion of branches.

[0154] In some embodiments, while generating pathways, the model may evaluate pathways as they are being constructed and discard poorly performing pathways in favor of highly rated pathways. Additionally, or alternatively, after pathways are generated, the model may be combined with optimization model to optimize generated solution. Additionally, or alternatively, production pathway generative models may work together with material and pathway prediction models, evaluation models, and optimization models, and / or material generative models. Additionally, or alternatively, generated pathways may be stored in databases and used for training of the model and / or other models. In some embodiments, any of the models described herein may be AI based.

[0155] In some embodiments, the control system 200 may modify the one or more parameters of the building material, as shown in block 1110. Further, in some embodiments, model may be trained with data generated from the sensor 102, as shown in block 1112. In this way, and in some embodiments, the AI model may be continuously trained as it receives data from the one or more sensors 102.

[0156] The model may be able to determine, in real-time or near real-time, the expected behavior of the building material 101. In this way, the model may pass that information to the control system 200 in order for the control system 200 to make any modifications to the building material 101. For example, the model may determine a lower than expected performance of a building material, and the control system 200 may react by adjusting input material quantities (e.g., binder portion, admixture portion, and / or the like), moisture levels, temperature, or the like of the process.

[0157] In some embodiments, the model may be configured to optimize material- related data, properties, performance, compositions, and / or the like. In some embodiments, the models considered by the present disclosure may optimize one of or a plurality of material compositions based on target objectives. In such embodiments, the models described herein may optimize a set and / or plurality of material compositions (e.g., optimize a set of compositional arrangements). Additionally, or alternatively, the models described herein may optimize based on target objectives (e.g. target material performance properties such as a given strength target at 28 days) in contextual conditions.

[0158] In some embodiments, the models considered by the present disclosure may optimize one of or a plurality of production processes via a control system based on target Page 39 of 171 13893549v1objectives and material outputs. In such embodiments, the models described herein may include live sensor data monitoring the process lifecycle (e.g., sensing the raw materials, sensing the production processes themselves, sensing the output products, any combination of the previous, and / or the like). Additionally, or alternatively, the models described herein may optimize contextual conditions during any part of the production process (e.g., by changing heat levels in a kiln, by changing speed of a mixer over time, and / or the like). In some embodiments, the production process may be a chemical reaction.

[0159] In some embodiments, the models herein may optimize both a material and the material’s production process, as well as contextual conditions and / or control parameters for both, simultaneously together. Further, the models herein may create a feedback loop between a material and the material’s production process where optimizing one feeds into the optimization of the other. In some embodiments, the models described herein may globally optimize both simultaneously. As will be understood by one of ordinary skill in the art in view of the present disclosure, both compositions and production processes or production parameters may have an impact on properties of materials, so by optimizing both together, they may feed into each other.

[0160] Further, in some embodiments, an example solution may use the data (e.g., data from the sensors 102) to determine material characteristics and / or properties. In other embodiments, the solution may include training a model that uses the data to predict attributes, characteristics, behavior, time evolving behavior, or the like of the output material based on the input material. In this way, the solution may edit or suggest mix modifications based on the data generated by the model. Further, in some embodiments, the solution may alter the mix proportions based on target contextual conditions. Further, in some embodiments, the solution may generate the mix design. In this regard, the solution (e.g., the system 100) may modify a given mix design.

[0161] In some embodiments, the solution may relate to a batching plant control system. In this way, the solution may determine and / or continually adjust the mix quantities on a batch by batch basis, wherein the solution may control the batch being made. In some embodiments, the control system 200 may determine batching quantities, in whole or in part, based at least on data from one or more sensors 102, which may include a wave-based sensor, a spatial mapping sensor, a container sensor, sensor data of materials over time, or the like. Additionally, or alternatively, batching quantities may also be determined, in whole or in part, based on at least data from one or more construction or concrete records data (e.g., concrete crush data, workability data, and / or the like). Additionally, or alternatively, in some Page 40 of 171 13893549v1embodiments such records data may be resultant from compliance tests (e.g., slump tests, concrete crush tests, or the like).

[0162] Further, in some embodiments, the control system (e.g., the control system 200) may be configured to receive input data from an input sensor (e.g., the sensor 102). Further, in some embodiments, the control system 200 may determine a target building material characteristic based on input data. In some embodiments, the target building material characteristic may include target properties the building material is designed to meet. For example, the target building material characteristic may include a particular crushing strength of the building material that it is designed to hit within a given time milestone (e.g., concrete is designed to meet X MPa after 28 days). Other examples of target building material characteristics may include a target workability or slump targets, shrinkage targets, temperature targets (e.g., maximum temperature during curing, or maximum temperature differentials between two points in the building material), or the like. Further, in some embodiments, proportioning may be based on target contextual conditions (e.g., pour geometry, weather conditions, temperature, etc.). In this way, the building material created in the one or more stages or one or more substages may be configured to meet the design criteria set out by the target building material. Further, the control system 200 may adjust the processes in the stages or substages to create the building material in a way that conforms or meets the requirements of the target building material. For example, requirements may include crush test requirements, strength requirements, moisture content requirements, requirements based on a third party entity (e.g., governmental entities, regulatory entities, business entities, or the like), and the like. Further, in some embodiments, the control system 200 may modify the building material based on a difference between the target building material characteristic and the one or more characteristics. In this way, the control system 200 may alter, configure, or reconfigure the one or more processes associated with the stages or substages. For example, a target building material characteristic of a particular crushing strength may require a specific amount of water to be added to the mix. The control system 200 may analyze how much water has been added, or is planned to be added, via the one or more sensors 102 and adjust the amount if necessary.

[0163] In some embodiments, the sensor device may be an input sensor configured to generate data associated with the first portion the building material as it enters the first stage. Further, in some embodiments, the sensor device may be an output sensor configured to generate data associated with the first portion of the building material as it exits the first stage. In this way, the sensor device (e.g., sensor 102) may be positioned and / or configured Page 41 of 171 13893549v1to generate data based on the material entering or exiting a stage (e.g., the first stage) of the construction process. For example, as the material enters the manufacturing stage 1606 as shown in Figure 16, the sensor 102 may generate data associated with the material properties, characteristics, or the like. Further, in another example, as the material exits the manufacturing stage 1606, the sensor 102 may generate data associated with the material properties, characteristics, or the like.

[0164] Further, in some embodiments, the control system 200 may be configured to receive output data from an output sensor. In some embodiments, the control system 200 may be configured to determine a target building material characteristic based on the output data. Further, in some embodiments, the control system 200 may be configured to modify the building material based on a difference between the target building material characteristic and the one or more characteristics. In this way, the control system 200 may receive data from the sensor 102 (e.g., the output sensor) wherein the output sensor generates data associated with the material as it is output from a process or a stage. In this way, the control system 200 may determine that the building material and associated properties is different than a target building material characteristic. The control system 200 may, in some embodiments, modify the building material based on the data generated and received from the output sensor.

[0165] Further, in some embodiments, the input sensor and the output sensor may be the same sensor. In this way, the sensor may initially be an input sensor as it generates data associated with a material entering a particular volume (e.g., a container). Further, when the material exits the container, the sensor may be an output sensor as it generates data on the material exiting the container. In this way, a sensor may be both the input sensor and the output sensor. Further, in some embodiments, the sensors 102 associated with the system, stage, substage, or the like may each be considered to be both an input sensor and an output sensor.

[0166] Further, in some embodiments, the method may include modifying, via the control system 200, a second portion of the building material associated with a second stage based on the one or more characteristics of the first portion at the first stage generated by the sensor device associated with the building material at the first stage. In this way, and for example, the second stage may include a stage that is later, earlier, and / or performed simultaneously in the process with respect to the first stage. For example, if the second stage is the manufacturing stage 1606 as shown in Figure 16, the first stage may be the project or pour stage 1608. In another example, the first stage may be the excavation stage 1602. In this Page 42 of 171 13893549v1regard, the first stage, and associated sensors 102, may generate data relating to the characteristics of the building material in that stage. For example, if, at the project stage 1608, the building material is determined, via the sensors 102, to have a lower crushing strength than a target building material characteristic, the control system 200 may modify the building material at the manufacturing stage 1606. In this way, the first portion (e.g., the portion at the project stage 1608) may influence how the second portion is being created at the manufacturing stage 1606.

[0167] Further, in some embodiments, modifying the one or more parameters of the building material may include modifying a quantity of the first portion of the building material (e.g., as described herein) and / or modifying a quantity of a second portion of the building material. In some embodiments, the quantity of the second portion of the building material may include a second amount of material associated with the building material. In some embodiments, the quantity of the first portion and the quantity of the second portion may be different quantities (e.g., different collections of material). In some embodiments, the quantity of the first portion and the quantity of the second portion may be the same quantity (e.g., the same collection of material). In some embodiments, the quantity of the first portion and the quantity of the second portion may have the same amount of material or a different amount of material. In some embodiments, modifying the quantity of the second portion may be similar to modifying the quantity of the first portion. For example, modifying the parameters of the quantity of the second portion may include dispensing the quantity of the second portion. In some embodiments, the quantity of the second portion may be dispensed into a container where the quantity of the first portion was dispensed. For example, a quantity of admixtures (e.g., the first portion) may be dispensed into a mixing container and also a quantity of water (e.g., the second portion) may also be dispensed into the mixing container.

[0168] Further, in some embodiments, the method may include modifying, via the control system 200, a material flow rate for the building material, based on the one or more characteristics. In this way, the control system 200 may determine that material should be transported faster or slower depending on the data generated by the sensors 102. In another embodiment, the control system 200 may determine additives should be added in to the mix more quickly or slowly depending on the data received from the sensors 102. Further, the characteristics may be used by the control system 200 to determine how much material should be transported and determine the flow rate of the material.

[0169] In some embodiments, the present solution as described herein may also provide traceability operations that may be used to determine how a material reacts to one or Page 43 of 171 13893549v1more processes. In this way, the control system 200 may track a movement of one or more materials through the construction process. For example, the control system 200 may track a movement of a quantity of a building material 101 as it moves through the batching plant. Further, in some embodiments, the building material 101, the portions of the building material 101, constituent elements of the building material, or the like, may have one or more digital tags or IDs. In this way, the digital tags may be assigned to an amount of the building material based on mapping data from the one or more sensors 102 (e.g., a volume of material is mapped in a silo which may be assigned a digital tag). In this way, the mapped material, which may include aggregate material, may be associated with the digital ID. Further, the amount of material that moves through the process may be edited by the control system 200 based on the material properties associated with the digital ID.

[0170] In some embodiments, the control system 200 may use data found in records and documents associated with materials at different parts of the value chain to trace how the materials move throughout the value chain (e.g., tracking the movement). In some embodiments, the control system 200 may gather machine data (e.g., data from a mixer on a batching plant) to increase traceability. Further, in some embodiments, the data gathered may be combined with the sensor data collected. In this way, the sensors 102 may be used to increase the accuracy of the traceability system and / or augment it. In other embodiments, the traceability may include using the sensor data for traceability rather than just for measuring properties. Further, in some embodiments, the data may be linked between the sensor data and the material traceability data (e.g., the system may associate an ID with a particular batch of material at a particular time and / or stage of the value chain). In this way, when a sensor collects data about the particular batch, associated data may be linked to the particular ID.

[0171] In some embodiments, Figure 3 may represent a particular stage 300, such as manufacturing stage, batching plant, or the like. In some embodiments, the building material may be mined, extracted, or the like from an excavation site, where it may be transported to the first substage 302 as shown in Figure 3. Further, the transportation of the building material 101 may be analyzed by one or more sensors 102 associated with the transportation system (e.g., a truck, train, ship, plane, or the like). In some embodiments, information and data associated with the delivery may also be ingested by the system 100 for analysis. In this way, the information and data ingested may influence or otherwise control the control system 200. For example a delivery ticket and the ticket’s associated information may be ingested by the system 100. The delivery tickets may take a variety of forms, including a physical paper document, an electronic document, text file, datasheet, spreadsheet, picture file (e.g., JPEG, Page 44 of 171 13893549v1PNG), digital object file (e.g., JSON) and any other digital format used to represent textual information as would be clear to one skilled in the art. Further, the delivery ticket may include specific information relating to the delivery of the building material. For example, the delivery ticket may include, but is not limited to: Project / Site name or ID; Delivery Ticket ID; Delivery Date; Delivery Time which may further include (Time Batched, Time Arrived, Time Discharged); Mix Design name or Mix ID; Batching Plant Name; Customer Name; Order ID; Quote ID; Delivery address (site address); Delivery quantity; Concrete Mix Design description and / or composition (including but not limited to its description, density class, chloride class, exposure class, etc.); Haulier Name and ID; Vehicle registration number; Delivery number (specifying its position within an order of several deliveries); Site Contact Name and Number; Order quantity (total and cumulative); Order status (e.g. Scheduled, Delivered, Cancelled, Returned, etc.); Delivery cost / price and currency; Concrete mix design unit price and currency; Supplier system concrete mix design reference; Custom recorded comments; Concrete volume returned; Water added to the concrete on site; Quality Technician load observations and tests (including but not limited to temperature, slump observations, etc.); (E)Signature details; and Concrete mix design data.

[0172] In other embodiments, the building material may be transported to a test site that may perform tests on the building material to analyze the building material’s properties. For example, the tests may include a crush test result of the building material. The crust test results may include construction documentation that contains information associated with compliance sample crush tests. In this way, crushes may use some sample from a particular material (e.g., building material) to determine performance of the material. For example, concrete crush test results may include concrete compliance sample crush tests. The crush test results may include a variety of forms, including a physical paper document, an electronic document, text file, datasheet, spreadsheet, picture file (e.g., JPEG, PNG), digital object file (e.g., JSON), and any other digital format used to represent textual information as would be clear to one skilled in the art. Data contained in a record may include (but is not limited to): Project / Site name or ID; Mix Design name or Mix ID; Material IDs (including both site / contractor id and lab id) for every material in the material group; Crush test records i.e. the compressive strength of a given material, alongside the age of the cube as recorded by physically crushing it using a concrete crusher, hydraulic press and / or any other equipment which may be used for crushing as clear to one skilled in the art; the compressive strength is often denoted in megapascals (MPa) or pound per square inch (PSI). In some embodiments, the age of the material is often denoted in hours or days and may be recorded as the duration Page 45 of 171 13893549v1of time between the concrete is poured into its mold and the moment it is crushed. Crush records may often include the above data for many materials in a material group. Material groups are collections of materials prepared from the same batch, to be used as compliance materials against this same batch as delivered to site. Further, a concrete record may include crush tests grouping reference (to identify which samples were created from the same delivery / load); Date the sample was made; Date the sample was received by the laboratory; Date the sample was tested; Dimensions of the sample; Density of the sample; Possible slump / flow / slump flow data; Specified strength of the sample’s concrete mix design at a specific age; Possible location data (identifying the element and / or pour where the concrete mix design load was used); Lab comments / remarks; Customer comments / remarks. In some embodiments, these data may be ingested by one or more sensors 102 associated with the test site.

[0173] Further, in some embodiments, other types of mechanical tests may produce records that are relevant to the material data. For example, a material may be tested for its flexural strength or its tensile strength. Similar to the crush tests (producing the compressive strength of the material), the flexural strength may indicate the ability of the material to flex under a load or force while the tensile strength may indicate the ability of the material to respond to a specified tension. These tests may provide records, data, and the like on building materials used throughout the process as described herein. The sensors 102 may gather the mechanical test records relevant to the building material data.

[0174] In some embodiments, other construction documentation may be either collected by the sensors 102 or otherwise uploaded to the system 100 or the control system 200. In this way, the construction documentation may also include Slump Test Records (including one off measurements of slump, or photos / videos of slump tests being carried out, but also continuous measurements of slump through any of the sensor embodiments already described herein); Concrete mix design supplier / customer qualitative data (e.g. comments and / or remarks) on a specific delivery; Customer / laboratory qualitative data (e.g. comments and / or remarks) on a specific sample / strength observation; BIM Models; Mix Information; Concrete mix design EPDs (and any EPDs for the constituent raw materials themselves); reinforcement bars EPDs (e.g., for steel and / or rebar reinforcement); Construction site concrete mix design specification documentation; pre and post-pour inspection sheets; photos and videos of the construction site; quality and compliance records; forms and checklists; permits to strike; permits to tension; permits to load; other types of permits; specifications; drawings; floorplans; any other type of record mentioned elsewhere in this disclosure. In Page 46 of 171 13893549v1some embodiments, the construction documentation may be used by the control system 200 to modify the building material 101.

[0175] In some embodiments, the entire journey of the building material 101 may be recreated by the data uploaded to the system 100. For example, the amount of time the material stays in a particular container may be calculated and determined based on sensors 102 associated with the container. In some embodiments, the time between one or more containers may also be calculated based on sensor 102 data. Further, as described herein, the location of a particular material may be determined based on the data received from the one or more sensors 102. In further embodiments, the contextual conditions and / or properties and attributes within a container or in material transport systems may also be determined.

[0176] Further, the control system 200 may include digital representations of the equipment, processes associated with the equipment, control parameters of the equipment, and the like. In this way, the control system 200 may be able to configure or reconfigure the equipment associated with a particular construction process. The control system 200 may adjust these parameters in response to sensor 102 data. Further, the materials associated with the process may be determined at any granularity (e.g., binder which may include cement, cementitious mixtures, building materials as a whole, or the like). The representation of the digital representations of the equipment may be output to a user device 106, and may be displayed using graph networks, or the like.

[0177] Further, the sensors 102 may be configured for sensing building materials at various parts of the value chain. For example, as shown in Figure 16, the construction process 1600 (or value chain) may include a variety of steps, subprocesses, or stages. In this way, the stages (e.g., stages 1602-1608) may all include one or more sensors 102. In some embodiments, a stage may include the extraction of the materials (e.g., portions) used to create the building material, as shown in step 1602. The extraction stage 1602 may include one or more sensors 102 that may generate information and / or data relating to the extracted material. Further, the transportation stage 1604 may also include sensors 102 that may generate data relating to the transportation of the material. Further, in some embodiments, the manufacturing stage 1606 may include the one or more sensors 102. As an example, one of the manufacturing stages 1606 may include a batching plant, as illustrated in Figure 3. It is to be understood that the batching plant representing the manufacturing stage 1606 is only a representation and not meant to limit the application of the solutions described herein. Further, the project stage 1608 may include the building materials being installed at the project site, which may also include the one or more sensors 102. Page 47 of 171 13893549v1

[0178] Further, in some embodiments, the sensors 102 may be placed by location. For example, the sensors 102 may be placed in a stage’s containers. In this way, the containers may include raw material containers and / or mixing containers. For example, in some embodiments, the sensors 102 may be configured to be within one or more containers associated with the one or more stages of the process. In some embodiments, sensors 102 may be configured to be included within the stages in the substages. For example, as shown in Figure 4, the container 303 of the first substage 302 may include the sensors 102. In some embodiments, the sensors 102 may be inside or outside of a container 303. For example, the sensors 102 may be placed on an outflow of the first substage 302 in order to measure properties of the building material as it flows from the first substage 302 to the second substage 304.

[0179] Further, the sensors 102 may generate data based on the material type. For example, the first portion of the building material may have a different type, or be at a different stage or substage, than other portions of the building material. In this way, the sensors 102 may determine that the material (e.g., the portion of building material) is a raw and / or input material, such as an aggregate, cement, water, admixture, air, or the like. Further, in some embodiments, the sensors 102 may be able to determine an intermediate material, which may include a material being mix, such as fresh concrete being mix. The intermediate material may be in the intermediate stage until the mixture is approximately fully mixed. For example, in an instant after the mixing of the raw or input materials have started, the material may be in an intermediate material phase. When the intermediate material becomes at least approximately fully mixed, it may be an output material. Further, in some embodiments, the output material may include fresh concrete in a truck and / or concrete that is setting or curing.

[0180] Further, in some embodiments, a variety of sensing types may be included in the one or more sensors 102. For example, the sensors 102 may be configured to detect the modality of the material. For instance, point sensing devices (e.g., sensors 102) may be configured to detect and generate data relating to the temperature and / or pH of the material. Further, spatial mapping sensing devices may be configured to generate data in response to wave-based sensing (e.g., mechanical wave-based sensing) and / or thermal properties of the material.

[0181] Additionally, or alternatively, in some embodiments, the sensors 102 may be configured to include spatial mapping sensing configurations. In this way, for example, a distribution of material may be determined based on a spatial map produced by the sensor 102. For instance, the sensor 102 configured to produce spatial maps may determine a total Page 48 of 171 13893549v1number of grains in an aggregate material, determine a value of attribute for each grain, and use that value to determine a distribution of the attribute across the material. Further, in some embodiments, the spatial mapping may be based on a volume of material. Further still, the spatial mapping sensing device (e.g., the sensor 102) may generate a graphical representation of the material via the mapping data. In some embodiments, an image reconstruction may be generated that shows the material.

[0182] Further, in some embodiments, the sensors 102 may be configured to perform spatial mapping of the building materials. In this way, and in some embodiments, the sensors 102 as described herein may be configured to generate and process data associated with the building material. Further, in some embodiments, the sensors 102 may be configured to process the data in a way as to represent a spatial mapping of the building material. Further, in some embodiments, the sensors 102 may be configured to spatially map the container 303, if any, the building material is associated with. For example, the sensor 102 may be configured to spatially map a silo (e.g., container 303) and the building material within the silo. Further, in some embodiments, the sensor 102 may spatially map the building material and a material transport system. In this way, for example, building material resting and / or moving on a conveyor belt (e.g., the material transport system) may be spatially mapped by the sensor 102.

[0183] Further, in some embodiments, the sensor 102 may be able to distinguish between the building material, the container, and / or the material transport system. In this way, the sensor 102, via spatial mapping, may be able to differentiate the properties and parameters of the building material, the container, and the material transport system, along with any other associated components. For example, the sensor 102 may be able to understand that the building material includes a specific volume within a container, wherein the container is able to hold more material than what is presently in the container. In other words, the sensor 102 may, via spatial mapping, understand that a container, for instance, is able to hold more material than what is sensed in the container.

[0184] Further, in some embodiments, the sensor 102 may be configured to spatially map the building material and / or the container in a variety of forms. For instance, the sensor 102 may be configured to create a 2D or a 3D representation of the container and / or the building material. In this way, the sensor 102 may include one or more electronic components capable of performing the spatial mapping process. For example, the sensors 102 may include a processor, a memory, electronic circuitry, communication interface, and the like. Further, in some embodiments, the sensor 102 may be configured to include software or programs Page 49 of 171 13893549v1capable of using the components of the sensor (e.g., the processor, memory, electronic circuity, communication interface, etc.) to perform the spatial mapping process. In this regard, and in some embodiments, the sensors 102 may be able to perform the spatial mapping independent of one or more other devices (e.g., other sensors, the control system 200, or the like). Further, in some embodiments, the sensors 102 configured to perform spatial mapping independent of other devices may be placed throughout the value chain as described herein (e.g., the stages, substages, or the like).

[0185] Further, in some embodiments, the sensors 102 may include a sensing device configured to transmit one or more spatial mapping signals, wherein the spatial mapping signals are used to analyze a building material, a container, and / or a material transport system. In some embodiments, the sensing device may be configured to receive the spatial mapping signals. In some embodiments, the sensor 102 may include a processor configured to ingest the data associated with the spatial mapping signals. In some embodiments, the processor may be configured to perform a spatial mapping process based on the spatial mapping signals. In some embodiments, the processor, via the spatial mapping process, may be configured to generate a two-dimensional (2D) reconstruction of the building material, the container, and / or the material transport system. In some embodiments, the processor, via the spatial mapping process, may be configured to generate a three-dimensional (3D) reconstruction of the building material, the container, and / or the material transport system.

[0186] In some embodiments, the methods and / or systems herein may include methods or systems for spatial mapping of building materials (e.g. aggregate, cement, cement paste, fresh concrete, hardened concrete). Additionally or alternatively, this may include using any wave-based sensing method for determination of density and / or density distribution of the building materials. Additionally or alternatively, this may include methods or systems for spatial mapping of building material microstructure and / or spatial mapping of particle grain distribution. Additionally or alternatively the methods and / or systems herein may include determining packing density and / or attributes of the building material associated with packing density and / or spacing between building material microstructure (e.g. in the case of concrete) and / or grains of building materials (e.g. in the case of aggregates) such as air content associated properties e.g. air bubble / void presence, air bubble / void size (average, or distribution), spacing between air bubbles and / or the like, air void type. In some embodiments, air void types that may be identified using the methods and / or systems herein for example in concrete or cementitious mixes may include entrapped air voids (e.g. larger than approximately 1mm and irregularly shaped), entrained air voids (e.g. approximately Page 50 of 171 13893549v1between 10µm-1mm in size, typically bubble shaped), capillary voids (e.g. typically smaller than 10µm), voids around and / or between aggregate particles and / or around and / or in between aggregates and the cement matrix. Additionally or alternatively, these characteristics may be determined in whole or in part using mechanical wave based sensing. Additionally or alternatively, the method may include placing a mechanical wave based sensing device and / or transducer (e.g. including piezoelectric transducer) in contact or close proximity with the building material (e.g. embedded in, disposed on, or closely directed at; in some embodiments, there may be an impedance matching coating layer between the transducer and the building material), generating a mechanical wave using an electrical signal to drive the transducer, transmitting the wave into the building material, receiving the wave back using the same, another or a plurality of other mechanical transducers, converting the mechanical wave into an electrical signal representative of the wave in time-domain, measuring the signal, inputting the measurement data into one or a plurality of models, wherein the model may be a machine learning model (e.g. neural network such as a convolutional neural network), determining the target property (e.g. the density related property and / or properties associated with air content). Additionally or alternatively, the mechanical wave may include an ultrasonic wave. Additionally or alternatively, the method may include generating and receiving many waves and / or pulses, including continuously generating and receiving signals over time, and determining the target property based on the plurality of waves and / or the time-continuous signal. Additionally or alternatively, the method / system may include generating and / or receiving frequency spectrum of mechanical waves and / or determining the impedance spectrum of waves at the continuous frequency spectrum. Additionally or alternatively, the transducer may be a piezoelectric transducer. Additionally or alternatively, the system / method herein may include the use of a resonance cavity and / or frame which may contain a portion of the building material. Additionally or alternatively, the method may include transmitting the wave into the resonance cavity and making a property determination based in whole or in part on one or more resonance characteristics of the received wave, which may be associated with the resonance cavity and / or the material under consideration.

[0187] Additionally or alternatively, the method may in whole or in part determine and / or use amplitude associated attributes of the wave(s) such as the amplitude of the received waves / signals, the echo intensity of the reflected waves, the amplitude attenuation of the wave, and / or the wave envelope shape and / or amplitude, including the amplitude characteristic spectra, over a range of frequencies, to determine the one or more properties associated with air content in the building material. Page 51 of 171 13893549v1

[0188] Additionally or alternatively, the method may in whole or in part determine and / or use time-of-flight associated attributes of the wave(s) such as the time taken between the wave being transmitted and reflected back to the receiver(s) (e.g. via a reflection), including the time-of-flight characteristic spectra, over a range of frequencies, to determine the one or more properties associated with air content in the building material.

[0189] Additionally or alternatively, the method may in whole or in part determine and / or use frequency associated attributes of the wave(s) such as the Fourier transform spectral analysis and / or other frequency domain analyses of the waves including changes in the frequency spectrum / frequency components / frequency decomposition signature of the wave(s) (e.g. induced by scattering or absorption from air void boundaries), band attenuation for specific frequency bands, and / or harmonic / resonance analysis, resonance peaks, resonance peak shifts and / or the like, to determine in whole or in part the one or more properties associated with air content in the building material (e.g. spatial mapping of air voids, spacing factor, air content size, air content type, air content spatial distribution, packing density and the like).

[0190] Additionally or alternatively, the method may in whole or in part determine and / or use phase associated attributes of the wave(s) such as phase shifts between the transmitted and received / reflected wave(s) or phase unwrapping based algorithms or methods, including the phase characteristics spectra, over a range of frequencies, to determine in whole or in part the one or more properties associated with air content in the building material (e.g. spatial mapping of air voids, air content size, air content type, air content spatial distribution, spacing factor, packing density and the like).

[0191] Additionally or alternatively, the method may in whole or in part determine and / or use velocity associated attributes of the wave(s) such as group velocity or phase velocity of the transmitted and / or received wave(s) and / or group and / or phase velocity changes between the transmitted and / or received wave(s), and / or velocity variations and / or velocity mapping between spatial locations (which in some embodiments may be reconstructed based on the measured wave and target material attributes using a model), including the velocity characteristic spectra, over a range of frequencies, to spatially map the material and / or determine in whole or in part the one or more properties associated with air content in the building material (e.g. spatial mapping of air voids, spacing factor, air content size, air content type, air content spatial distribution, packing density and the like).

[0192] Additionally or alternatively, the method may in whole or in part determine and / or use waveform shape-associated attributes of the wave(s) such as distortions in Page 52 of 171 13893549v1waveform shape between the transmitted and / or received waves and / or changes in rise time and / or duration of received signal, to spatially map the material and / or determine in whole or in part the one or more properties associated with air content in the building material (e.g. spatial mapping of air voids, air content size, spacing factor, air content type, air content spatial distribution, packing density and the like).

[0193] Additionally or alternatively, the method may in whole or in part determine and / or use attenuation-associated attributes of the wave(s) such as the energy loss (e.g. due to absorption, scattering and the like) of the wave between the moment of transmission and receiving (e.g. due to scattering), for example as measured by amplitude attenuation in the wave and / or the attenuation coefficient of the material with respect to the wave, including the attenuation coefficient spectrum over a range of frequencies, to determine in whole or in part the one or more properties associated with air content in the building material (e.g. spatial mapping of air voids, air content size, air content type, spacing factor, air content spatial distribution, packing density and the like).

[0194] Additionally or alternatively, the methods herein may include signal extraction and / or processing signals such as Fourier transforms (e.g. fast Fourier transform, discrete Fourier transform, and the like), wavelet transforms (e.g. continuous wavelet transform and the like), Hilbert transform, cross-correlation based processing, machine learning models, principal component analysis, and the like. In some embodiments, the techniques listed herein may be used for frequency decomposition of the signals herein, and / or the methods herein may further include using the frequency decomposed signal, extracting a frequency or frequency range specific feature, and using this feature for the determination.

[0195] Additionally or alternatively, the methods and techniques described herein may be used to determine characteristics of the building material associated with the microstructure of the building material. Additionally or alternatively, this may include determining regions of aggregate content and / or cement matrix. Additionally or alternatively, this may include identification of aggregate types within a cementitious mix and / or within a container. Additionally or alternatively, in some embodiments, the methods and / or techniques herein may be used for spatial reconstruction of aggregates in a container. Additionally or alternatively, the methods and / or techniques herein may be used for spatial reconstruction of concrete microstructure. Additionally or alternatively, the methods and / or techniques herein may be used for determination of concrete homogeneity, in either its fresh state, hardened state, or any stage in between. Page 53 of 171 13893549v1

[0196] Additionally or alternatively, the method may in whole or in part include using the difference between any attribute of the wave as it has been transmitted and / or as it has been received to determine the associated property. Additionally or alternatively, the method may include determining S-parameters and / or T-parameters, as well as characteristics associated with wave scattering in the building material and / or using these parameters and / or characteristics, in whole or in part, to make determinations associated with the target properties (e.g. air content density, air bubble / void density, air bubble distribution, air bubble spacing, spacing factor, air bubble size, and the like…). In some embodiments, all the features of waves listed herein may be used, in whole or in part, to determine any characteristic listed herein of any building material listed herein, using any of the wave based modalities herein (e.g. they may be applicable to wave based methods other than ultrasonic mechanical wave based methods, including mechanical wave methods at other frequencies, as well as E&M wave based methods such as RF-based frequencies, terahertz based, microwave based, IR based, visible light based, UV based and / or other wave-based methods.

[0197] Additionally or alternatively, in some embodiments, any of the methods, techniques, systems, determinations and the like listed herein above, in whole or in part, may be generalized to be with different wave-based techniques and / or modalities, such as E&M wave based techniques (or mechanical waves other than ultrasound), as listed herein and / or would be otherwise clear to one skilled in the art. This may include property determination based on wave scattering in the material as well as based in whole or in part on any of the wave features (e.g. amplitude, phase and the like) mentioned herein. Additionally or alternatively, any of the methods, techniques, systems, determinations and the like listed herein above may be used to make determinations associated with any of the other characteristics and / or attributes and / or properties of building materials as listed elsewhere herein e.g. characteristics and / or attributes and / or properties of binders (e.g. spatial mapping, fineness, composition such as tricalcium silicate presence, and the like as listed elsewhere herein), aggregates (e.g. spatial mapping, grading, moisture distribution / content, aggregate type and the like as listed elsewhere herein) admixtures, water, cementitious mixes (e.g. workability, viscosity, shrinkage, strength / compressive strength / tensile strength, rate of hydration, temperature or temperature distribution, microstructure, homogeneity or inhomogeneity, composition, air void content / mapping / attributes, modulus of elasticity, and the like), cementitious pastes, fresh cementitious mixes, hardened cementitious mixes, fresh concrete, and / or hardened concretes. Page 54 of 171 13893549v1

[0198] In some embodiments, water may be spatially mapped. In this way, the water that gets poured into the mixer is firstly analyzed while inside its tank with embodiments similar to the ones inside the mixer, able to assess its exact composition and temperature. When the water gets poured into the mix, the sensors in the mixer monitoring embodiment will determine the gradient of its influence to the mix. The distribution of temperature will be monitored, as water spreads and thermal equilibrium is reached. The speed at which parameters such as viscosity and density of the mix evolve after the water pouring will be monitored as well and compared to models and previous batches. The spatial distributions of these parameters will be acquired in the form of images describing all these gradients. The first objective of this analysis is to determine the repeatability of the water adding process and determine if it always apports the same outcome to the mix. Another objective of the spatial mapping of the water impact on the mix is to link it to the output performance of the concrete, to its workability and compressive strength characteristics.

[0199] In some embodiments, admixtures may be spatially mapped. In this way, the admixtures that get poured into the mixer are firstly analyzed while inside their tank with embodiments similar to the ones inside the mixer, able to assess their exact composition and temperature. When they get poured into the mix, the sensors in the mixer monitoring embodiment will determine the gradient of their influence to the mix. The distribution of temperature will be monitored, as admixtures spreads and thermal equilibrium is reached. The speed at which parameters such as viscosity and density of the mix evolve after the admixtures pouring will be monitored as well and compared to models and previous batches. The spatial distributions of these parameters will be acquired in the form of images describing all these gradients. The first objective of this analysis is to determine the repeatability of the admixtures adding process and determine if it always apports the same outcome to the mix. Another objective of the spatial mapping of the admixtures impact on the mix is to link it to the output performance of the concrete, to its workability and compressive strength characteristics.

[0200] In some embodiments, fresh concrete may be spatially mapped. In some cases the embodiment that monitors fresh concrete in mixers, as described herein, may be modified to be able to diversify its measurements in the space of the mixer with the goal of generating a spatial map of the mix evolution as it occurs in the mixer. The embodiment will then have a shape that covers larger areas with different geometries, like bars or rings. Flow sensors at the water and admixtures intakes will be added to have the information of how much and when the ingredients get added to the mix. In some embodiments, the gradient evolution of Page 55 of 171 13893549v1the monitored parameters will be linked to the ingredients mixing timing and characteristics to assess if the procedure is repeatable, what affects it and what can improve its outcome. Additional Sensor Embodiments

[0201] In some embodiments the methods and / or systems may include methods or systems to measure and determine physical characteristics and changes therein across materials in different environments including but not limited to different facilities and machinery involved in construction material manufacturing chains (e.g. concrete batching plants).

[0202] In some embodiments sensor devices can be placed in and / or around containers and be configured in a plurality of ways including but not limited circular, oblong, or other shaped arrays completely or partially encircling exteriors, interior, segments, and / or inside and outside of openings.

[0203] Additionally or alternatively devices could be arranged in strips mounted along the sides or suspended within a container. Additionally or alternatively in some embodiments devices could be designed to be placed freely among materials such that they are inserted into and pass through a system in the same functional pattern and following the same path as the constituent parts (e.g. a device of approximately the same proportions as a given cement mix additive is added to a batch of additive material and moves through the manufacturing chain with it). Additionally or alternatively in some embodiments the device could broadcast a signal constantly as it moves through the system and / or be extracted at a given point having locally recorded data.

[0204] Additionally or alternatively, in some embodiments devices could be arranged to interact with materials in air (e.g. moving from a container to a transport system) or in uncontained environments (e.g. moving along a conveyer belt). Additionally or alternatively, in some embodiments sensing devices could be paired with other device arrays in some embodiments including but not limited to tools for sorting or adjusting materials as they move along the manufacturing line (e.g. hitting unsuitable additive rocks out of the air as they fall from a silo to a transport unit based on high speed sensor analysis and modelling). Additionally or alternatively, in some embodiments data from the sensors could be input into a plurality of models and / or combined with user input to have the system dynamically adjust the behavior of the devices involved.

[0205] Additionally or alternatively, in some embodiments the system could use a plurality of methods for moisture detection or analysis of particulate air contact or Page 56 of 171 13893549v1temperature to determine physical characteristics of materials (e.g. additives in concrete batch mixing), containers, and / or other equipment.

[0206] Additionally or alternatively in some embodiments devices could emit and / or receive electromagnetic radiation in specific patterns to determine physical characteristics including but not limited spatial positioning, density, temperature, and / or strain.

[0207] Additionally or alternatively in some embodiments devices could be arranged to move in specific patterns to gather larger sets of data and / or track the change in characteristics over time. Additionally or alternatively, the system may include methods to input the data gathered by the system into a plurality of models including but not limited to mapping material movement and / or modelling damage to material by interacting with equipment and / or efficiency of material transit in an environment, to analyze material characteristics and / or behaviors and / or to improve functionality of built materials and / or improve processes involved in the manufacturing chain.

[0208] Additionally or alternatively, user input or data output from a plurality of models including those unrelated to the system may be used to adjust the systems behavior (e.g. change device or material positions in space, change device activation patterns, change materials, and / or change models being applied).

[0209] Additionally or alternatively embodiments could have sensors that include optical sensing including high speed imaging, laser image tracking, fluorescence intensity modelling. Additionally or alternatively, in some embodiments devices placed in different parts of the manufacturing chain including inside of concrete mixers (e.g. on site mixers, concrete mixing trucks) in fixed and / or mobile configurations could use seeded particles and / or independent devices with or without additional sensor equipment to track movement and material characteristics around a mixer. Additionally or alternatively, in some embodiments, the methods herein may include imaging techniques configured to continuously monitor the movement of a fresh cementitious mix in the mixer being mixed. Additionally or alternatively, the imaging techniques may include hyperspectral imaging, cameras, LIDAR based methods, RADAR based methods, other E&M wave based methods including scattering methods, mechanical wave based sensing methods including ultrasonic based methods and the like. Additionally or alternatively, in some embodiments, the data associated with the movement of the fresh cementitious mix may be used to determine, in whole or in part, an attribute and / or property of the fresh mix, including data indicative of workability, viscosity, flow, thermal distribution (e.g. using IR imaging based methods) and the like. Additionally or alternatively, determining the properties listed herein may include Page 57 of 171 13893549v1inputting the sensor data associated with the movement of the fresh concrete into a model, wherein this model may in some embodiments be a machine learning model. Additionally or alternatively, the machine learning model may be trained on a training dataset including labelled data associated with the movement of the fresh concrete over time (e.g. continuous time series data or feed), and associated expected properties such as viscosity, fluid flow and the like. Additionally or alternatively the model may include a physico-chemical model which uses physical laws, equations, and / or parameters to determine the property based on the sensor data. Additionally or alternatively, the models herein may include use of Navier- Stokes equations, Bernoulli equations and the like. Additionally or alternatively, the methods herein may include generating a simulation of fresh mix movement and / or movement patterns and / or movement data inside the mixer, wherein the simulation may include a spatial geometrical reconstruction of the mixer (including container and / or mixing paddles), a simulation of mixer rotational velocity and / or speed, and / or simulation of fresh material attributes and / or compositional properties. Additionally or alternatively, the simulation may include simulating different mix behavior for the same mixer. Additionally or alternatively, the machine learning models herein may be trained on simulation data. Additionally or alternatively, the property determination may be made, in whole or in part, based on the simulation. Additionally or alternatively, in some embodiments data collected from devices could be input into a plurality of models to model and / or analyze characteristics including fluid flow, moisture content, fluid flow, electrical impedance, temperature, slump, viscosity and / or other characteristics.

[0210] Additionally or alternatively data from this could be input into a plurality of models or systems and used to dynamically adjust behaviors, materials, material choices, equipment, workflows, and / or designs (e.g. mix designs in concrete) further up the chain. Additionally or alternatively in some embodiments devices could be included in mixers wherein the materials flow through a contained shape (e.g. a cone, ring, cylinder, or rectangular prism) with sensors positioned to observe physical properties and behaviors of the material as it passes through. Additionally or alternatively in some embodiments an electrical impedance based system of emission and / or reception and / or an (ultra)sonic pulse velocity based system could be placed in the contained shape and applied to material as it passes through. Additionally or alternatively in some embodiments different equipment could include elements designed to tilt, press, or vibrate with dynamically adjusted parameters and / or coupled with sensors that could observe the effects of the motion on the behavior of the materials (e.g. panels to tilt and cameras to observe slumping, moving panels to press on Page 58 of 171 13893549v1either side of material as it moves through a contained space and analyze resistance). Additionally or alternatively, data from these sensors could be input into a plurality of models to determine physical characteristics and / or their evolution over time. Additionally or alternatively, in some embodiments data input at different points along the manufacturing chain including but not limited to weight, material types, temperatures, and / or time series data could be input into a plurality of models to analyze and determine physical characteristics and behavior of the materials across the manufacturing process. Additionally or alternatively, in some embodiments devices could be used to generate and / or receive laser pulses and / or other electromagnetic radiation and record data about how that radiation interacts with materials including but not limited to liquid concrete or cementitious materials (e.g. laser induced fluorescence of particles, optical tracking of doped particles, and / or speckle imaging based on reflection scattering. Additionally or alternatively in some embodiments data generated by the system could be used with a plurality of models to analyze physical characteristics and interact with automatic systems for dynamic adjustment in order to change the physical characteristics of the material being sensed and / or adjust the creation process for future iterations.

[0211] In some embodiments, models of the present disclosure may include a materials marketplace as a feature. In such embodiments, one or more models may be executed on a centralized platform which may provide a comprehensive database of material producers, their production facilities, the raw materials available at each location, and the formulations produced. Users may define their exact material requirements, and the system may search its database to identify suitable formulations that meet these specifications, utilizing one or more of the models described herein (e.g. for accurate matching to requirements).

[0212] In some embodiments, the composition of binder (e.g., Portland cement) may be analyzed by the sensors 102 to determine properties. In this way, the binder may be proportioned (e.g., in the mixer) accordingly. In some embodiments, the sensors 102 may sense binders that are mixed to identify a combined powder (e.g., portion) of each material (e.g., binder). In some embodiments, the binders are sensed separately. In some embodiments, the sensors 102 may sense the dry binder mix.

[0213] In some embodiments, the system may use known processing times to determine which material is which at different points and times in the batching process. In this way, the system may determine how long it takes for material to go from a discharge Page 59 of 171 13893549v1from a container, for example, to a certain point being sensed on a material transport system. In this way, the system will know which material is being sensed.

[0214] In some embodiments, the system may modify quantities of admixtures added to the batch based on sensor 102 measurement performed on any of the raw materials. In this way, the sensor data may determine that binder stored in a container is particular reactive, for example, and reduce the amount of admixture (e.g., in the case where the admixture is accelerating admixture).

[0215] In some embodiments, low cost sensors 102 in an array may use Bluetooth beacons to localize the sensor. In some embodiments, sensors may be placed in a container, perform 3D mapping via a network, and move through the container. General Attributes of Materials

[0216] As used herein, a “material” or a “building material” may include any material, component, constituent, element, or the like used to create a material. In this way, materials may be used (e.g., combined) to create a material, which may include a building material. For example, constituent components, elements, composites, substances, or the like may be used either singularly or in combination to form a building material. Additionally, or alternatively, the constituent components or elements may include the first portion (or any portion) of the material (e.g., the building material). It is to be understood that the disclosure as provided herein may relate to materials other than building materials and may be applied to other types of industries, processes, projects, or the like. For example, a grain may be a constituent component, element, or portion of a final material. In this way, the processes as described herein may apply to an industry, process, or project relating to the modification of grain as a portion of the final material. In another example, the material may include materials used in creating concrete, which may include binders, aggregates, water, air, admixtures, cement, cementitious mix, or the like.

[0217] In some embodiments, one or more building materials may behave differently in different contextual conditions - its contextual material properties may be different (e.g. Strength, Durability, Shrinkage, Strain, Workability, will evolve differently in environments with different temperatures). For example, a building material (e.g. concrete) poured into a cube of sides 10cm, may cure very differently when compared to the same material poured into a cubical slab of sides 3m e.g. strength gain curves may be significantly different. Page 60 of 171 13893549v1

[0218] In some embodiments, a building material property or parameter may be defined at a certain time and at a certain location within the instantiated use-case of the material in question. For example, a building material property or parameter might include the compressive and flexural strength of the material at a specific location on the surface of one of its faces, and at a specific time. It may also be the case that a building material property may change with both time and spatial position within an instantiated material. In general, the models herein may output building material properties that belong to a certain position and / or time, or that are spatially distributed in the instantiated material as a field of values throughout its body and / or surface, or that are temporally distributed (as a time series). In the case where values are not spatially or temporally dependent (such as the average workability of a batched cementitious material), the models herein may account for the building material property as a “bulk” value (independent of spatial position) and / or a “constant” value (independent of time).

[0219] In some embodiments, the materials and / or building materials may include binders, aggregates, water, admixtures, or the like. For example, the building material 101 may include materials, such as constituent components or elements, used to create or combine together to create concrete, cement, or an otherwise cementitious mixture.

[0220] In some embodiments, the sensors 102 may be used to sense one or more material properties and / or attributes of a material (e.g., a portion of a material and / or a building material). Further, the sensors 102 may sense a distribution of the material properties and / or attributes for a collection or volume of material (e.g., a distribution of rounded aggregates, angular aggregates, sub-angular aggregates, flaky aggregates, elongated aggregates, irregular aggregates, or the like). Further, in some embodiments, for an attribute and / or property, the sensors 102 may sense (e.g., spatially map) distributions of quantities for the collection of material or volume of material sensed. For example, this may include a moisture distribution for a volume of cement. In other embodiments, the distributions may be used to determine or generate an average distribution for the material in a container and / or any sub-volume in the container. Further, in some embodiments, an average distribution, for example, may be used to determine batching quantities and / or proportions.

[0221] In some embodiments, the specific material properties of the building material or a portion of the building material may be determined. For example, an aggregate may have material properties relating to the size, grading, size distribution or the like. Fine aggregates may be approximately 0.1 millimeters (mm) to approximately 4.75 mm in size, while coarse aggregates may be approximately 4.75 mm to approximately 37.5 mm. Further, the shape Page 61 of 171 13893549v1and / or shape distribution may be determined. For example, the aggregates may have shapes such as a rounded aggregate, angular aggregate, sub-angular aggregate (e.g., partly rounded, partly angular), flaky aggregate (e.g., thin and flat), elongated aggregate (e.g., long), flaky and elongated (e.g., long, thin and flat), or irregular. Further, the surface texture of the aggregate may be determined, which may include glassy, smooth, granular, rough, honeycombed, or porous. Further, in some embodiments, other properties of the aggregates may be determined, such as the density (e.g., specific gravity, bulk density, packing density), porosity and water absorption, elastic modulus, crushing strength, durability, chemical reactivity, thermal properties, presence of impurities, deleterious materials, organic impurities (e.g., silt, clay, salts, etc.), and the composition, which may include the mineralogical composition, compositional properties, atomic composition, molecular composition, etc. For example, the mineralogical composition may include quartz, feldspar, clay minerals (e.g., montmorillonite and / or carbonate minerals, limestone, dolomite).

[0222] Further, in some embodiments, attributes associated with the viscosity of the material (e.g., concrete, cementitious mix, or the like) include workability, consistency, slump, pumpability, air content (e.g., the air entrained in the mix or the presence of air bubbles in the concrete), and temperature or temperature distribution. Temperature attributes may also include measurements of heat generated from the start of the concrete hydration reaction, where the binder reacts with water. Additional attributes include density, cement content, water-to-cement ratio, and early indicators of concrete setting and hydration, such as temperature or electrochemical and pH-type sensing. Attributes associated with homogeneity, dispersion, and mixing include homogeneity (including its evolution as raw materials are initially added and mixed, transitioning from very inhomogeneous to progressively more homogeneous, which could be tracked), segregation resistance (how much aggregates segregate), bleeding (how much water segregates or rises to the surface), pH level, and chloride content.

[0223] In some embodiments, composition includes chemical, mineral, and other aspects that influence the parameters, properties, and performance of materials like cement and concrete. Chemical composition includes calcium oxide (CaO) for strength development, reactivity, and thermal behavior; silicon dioxide (SiO₂), which contributes to strength through the formation of calcium silicate hydrates; aluminum oxide (Al₂O₃) and iron oxide (Fe₂O₃) for strength, reactivity, and heat of hydration; and magnesium oxide (MgO), which affects expansion stability and durability. Chloride content influences corrosion and durability, with higher levels leading to potential structural issues. Mineral composition includes compounds Page 62 of 171 13893549v1like tricalcium silicate (C3S) for early strength gain, reactivity, heat of hydration, and curing time; dicalcium silicate (C2S) for long-term strength, reactivity, heat of hydration, durability, and curing time; tricalcium aluminate (C3A) for setting time, reactivity, heat of hydration, sulfate attacks, curing time; and tetracalcium aluminoferrite (C4AF), which improves workability and reduces heat during hydration. Other composition factors include alkali content, such as sodium oxides (Na2O) and potassium oxides (K2O), which may cause alkali- silica reactivity. Sulfate content, such as gypsum (CaSO4), affects setting time and, if excessive, may delay setting and reduce durability. Free lime content (CaO) impacts durability and expansion, with excess potentially causing cracking. The presence of supplementary cementitious materials and / or pozzolanic material (e.g., fly ash or silica fume) can significantly alter final properties of the material.

[0224] In some embodiments, concrete properties derived from composition include chemical reactivity (e.g., rate of hydration and setting time), strength development over time, workability (e.g., viscosity, slump, pumpability), and long-term durability (e.g., resistance to cracks, corrosion, shrinkage, and creep); thermal behavior, influenced by the heat of hydration; and structural factors like microstructure, porosity.

[0225] In some embodiments, fineness and / or particle size distribution may affect reactivity, strength, workability, thermal behavior, microstructure or the like. In some embodiments, finer particles lead to faster strength gain but higher heat, and larger particles lead to slow setting but reducing early strength. Particle shape and texture impact workability, hydration rate, and density, with rounded particles enhancing workability and reducing voids, while angular or rough particles increase strength by creating tighter interlocking structures. Density and density distribution, including specific gravity, are also key considerations in determining overall performance.

[0226] Further, in some embodiments, properties associated with admixtures may include: workability enhancement; water reduction; air-entrainment capability; setting time control; compatibility with cement; durability enhancement; rheology modification; strength enhancement; shrinkage reduction; corrosion inhibition; reduction of permeability; resistance to alkali-silica reaction (asr); freeze-thaw resistance; sustainability improvement; workability retention; heat of hydration modification; self-healing capability; dispersion capability; pH.

[0227] Further, in some embodiments, properties associated with water may include Composition - in particular detecting presence of impurities such as chloride content; sulfate content; total dissolved solids (TDS); organic content; suspended solids; biological contaminants; and / or other attributes such as pH & alkalinity; hardness; temperature. Page 63 of 171 13893549v1

[0228] Further, in some embodiments, properties associated with placed concrete, concrete pours, and / or cementitious mix pours may include: workability; consistency and flowability; air content; temperature of fresh concrete; bleeding rate and water separation; initial and final setting time; compressive strength; flexural strength; tensile strength; elastic modulus; impact strength; anchorage or pull-out strength; dynamic modulus of elasticity; shear strength; permeability to water or gas; chloride ingress resistance; chloride ion permeability; carbonation resistance; freeze-thaw durability; sulfate resistance; alkali-silica reactivity; electrical resistivity; chemical resistance; resistance to marine exposure; density (unit weight); thermal conductivity; drying shrinkage; autogenous shrinkage; resistance to cyclic wetting and drying; capillary absorption of water; homogeneity and quality; specific heat capacity; thermal expansion and contraction coefficients; abrasion resistance; surface hardness; water absorption capacity; resistance to scaling; creep; relaxation; fire resistance and spalling behavior; resistance to radiation; fatigue resistance; crack propagation and fracture toughness; self-healing capability; acoustic insulation properties; electrical conductivity; hydration heat profile; pore structure and pore size distribution; sustainability indicators.

[0229] In some embodiments, all measurement data may be stored in a database, which is formed of both historic and live data and encompasses, but is not limited to, all data types described above. The sources of this data can be categorized as recorded values from measurement devices, documents (digital or handwritten), human input (e.g., via forms, surveys, or filling out data fields on the digital platform), insights derived from the execution of any models stored on the digital platform, and data and insights accessed via external organizations or services (e.g., a weather forecasting service accessed via API).

[0230] In some embodiments, specific examples may include the following, which may include sensor data such as wave-based sensor data, which encompasses any data collected by electromagnetic and mechanical wave-based devices. Further specific examples include data related to temperature, strength, humidity, conductivity, tilt, strain, acoustic signals, electromagnetic properties, piezoelectric responses, and pressure. Additional examples include optical data, spectroscopy, images and videos captured by cameras, LIDAR, infrared data, and data from mobile phones or tablets. This sensor data can be live or real-time data or historical data recorded at an earlier time or date. Sensors can be placed throughout the full concrete value chain, including locations such as the kiln, quarry or raw materials extraction, batching plant, transit or truck, pump, and pour or site. Specific examples include sensors in mixers at batching plants to assess homogeneity and cameras Page 64 of 171 13893549v1capturing image data of concrete flow into the drum to extract flow rate and correlate it to workability using computer vision and post-processing. Other data types include maturity data, enhanced maturity data, mix fingerprinting data (any output produced by mix fingerprinting), and machine data such as machine data collected from batching plants for raw materials recipes at batching.

[0231] Further, in some embodiments, documents may be digital or handwritten, and may include specifications, recorded crush test results, BIM model data, batching records, schedules, building codes, EPDs. In some embodiments, human input may be any specific data type. Further, in some embodiments, insights derived from execution of any models stored on the system may include any output from the one or more models. Further, data and insights accessed via external organizations and / or services may include weather forecasting services.

[0232] In some embodiments, the devices that capture the physical measurements described herein may include any type of device mentioned in any section, such as thermal sensors, wave-based sensors, including piezoelectric sensors, spectroscopy sensors, or any combination of these. The following list is representative and non-exhaustive: wave-based sensors, mechanical wave-based sensors, piezoelectric EMI devices, acoustic devices, electromagnetic wave-based sensors, electrochemical devices, magnetochemical devices, electromagnetic wave impedance sensing devices, refractive index sensing devices, ground- penetrating radar (GPR) sensing devices, terahertz frequency sensing devices, NMR and / or microwave spectroscopy devices, LIBS spectroscopy devices, FTIR spectroscopy devices, X-ray diffraction devices, hyperspectral imaging devices, LIDAR devices, RADAR devices, temperature sensors, strain gauges, and barometers.

[0233] Further, in some embodiments the sensors 102 may generate feedback loops between the real, in-situ behavior of mixes, and the mix design process. In essence, given comprehensive historical data about the material behavior / attributes of mixes in the field, alongside any relevant mix recipe information (though this is not a requirement), it is possible to design mixes which will behave within chosen bounds on target contextual material properties, and within chosen bounds on contextual conditions (e.g., time of year, location etc.). Further, historical data collected by sensors may be used to create a large database of mixes and their associated material behavior / attributes. Further, in some embodiments, examples of sensors 102 may be as follows: Sensors at the quarry include spectroscopy sensors to monitor the composition of raw elements sourced, enabling models to produce better estimates of the variability of raw elements. Sensors at the kiln include temperature Page 65 of 171 13893549v1sensors to monitor the temperature inside the kiln, allowing models to optimize the kiln temperature profile. Sensors at the batching plant include relative humidity sensors to measure the moisture content in a batch of concrete, which enables optimization based on a target moisture content. For example, models may minimize workability given a specific target contextual material property.

[0234] Further, in some embodiments, the sensors may be placed in a truck. The mix may be monitored while it is in the truck. One implementation involves piezoelectric sensors in the truck. Piezoelectric materials respond to mechanical pressure and convert it into electrical signals. The pressure placed on the sensor by the material in a rotating drum varies in proportion to the material's viscosity. This relationship allows the sensors to characterize the viscosity and, consequently, the workability of the material. The signals measured via the piezoelectric sensor would be stored in a database alongside mix recipe data (compositional properties). This data may be normalized across various types of implementations using self- detection, ensuring that all available training data can be made comparable to a single standardized set of contextual conditions. This is one possible approach. Models may be trained on this standardized data to understand the relationship between compositional properties and workability. Models may also be trained on unstandardized data. Given a target workability and a specific optimization target (e.g., minimizing the time taken to cure), models could predict the workability of a given mix and use that prediction to iteratively generate mixes whose utilities would be calculated at each step until reaching an optimal point that satisfies the target workability requirements.

[0235] Further, in some embodiments, sensors 102 may be placed in a pump. In this way, the strain gauge in the pump may be used to determine the viscosity and workability of the material. This could create a feedback loop across the pump and truck to achieve a fuller characterization of the evolution of workability under different contextual conditions. In general, the feedback loop would allow models to represent the evolution of the material properties of a mix across the entire creation and delivery process, providing the most complete understanding of the material.

[0236] Further, in some embodiments, the sensors 102 may be placed in a pour. In this way, a sensor device may be embedded inside a concrete pour to characterize the compressive strength of the concrete, potentially in conjunction with piezoelectric devices. As the concrete cures, its LIBS spectrum may shift, and this shift can be correlated with the gain in concrete strength during hydration. Similarly, the EMI spectrum will also shift during curing and may be used to characterize strength and other contextual material properties of Page 66 of 171 13893549v1the concrete. Additionally, LIBS spectroscopy may provide direct data about the compositional properties of the mix, with the measured LIBS spectra serving as direct measures of the material composition. This compositional data can be linked with associated EMI spectra, and self-detection algorithms may improve comparability across all such data. Models may be trained on this data to understand the correlation between strength and LIBS- piezo data, as well as between compositional properties and LIBS spectra. Given a target strength and a specific utility function (e.g., minimizing curing time), models could generate, adjust, or recommend mixes based on the sensor data in the database and the contextual conditions of the material being designed.

[0237] Further, in some embodiments, an electromagnetic wave-based sensing device may be used to measure the refractive index at the boundary of a material to characterize the curing rate. This can assist in optimizing for a curing schedule target, ensuring that the pour does not exceed scheduling requirements, or alerting the user if it is predicted to exceed the schedule in the future. A mechanical wave-based sensing device may be used for electromechanical impedance measurements or acoustic measurements to characterize curing strength and other contextual properties of the concrete. It may also be used to tomographically map out the concrete. An electromagnetic wave-based FTIR spectroscopy sensing device may be used to map out the molecular structure of a material, including the shape of bonds, with this data stored in a database and used to train models to understand the chemical behavior of the material. An electrochemical wave-based sensing device may be fully or partially immersed in the concrete to capture the AC electrical impedance spectrum of the material and characterize its water content. Two examples of how such measurements can be used include optimizing for compressive strength given a target workability (as water content is often correlated with workability, with more water increasing workability) and measuring compositional material properties (e.g., identifying that a mix has x% water at batching).

[0238] Further, in some embodiments, the sensors 102 may be placed throughout the full value chain. In some embodiments, the value chain may include a value chain associated with a concrete value chain. In some embodiments, and as mentioned previously, Figure 16 may represent a value chain of a construction process, which may include a concrete value chain. For example, the value chain may include a kiln, wherein the sensors 102 may sense the raw cement. Further, a quarry and / or raw material extraction site (e.g., similar to extraction stage 1602) may sense raw aggregates, portions of the building material, or other constituent elements of the building material. Further, in some embodiments, a batching plant Page 67 of 171 13893549v1(e.g., similar to manufacturing stage 1606) may include one or more sensors 102 to sense all the raw materials, similar to the batching plant as shown in Figures 3 and 13. Further, a transit system or truck (e.g., similar to transportation stage 1604) may include sensors 102 to detect fresh concrete. Further, a pump or other material transport system may include sensors 102 to sense fresh concrete. Further, the pour site or project site (e.g., similar to project stage 1608) may sense concrete that, over time, transforms from fresh to hardened concrete. Control System

[0239] In some embodiments, the control system 200 and the user device(s) 106 may have a client-server relationship in which the user device(s) 106 are remote devices that request and receive service from a centralized server, i.e., the control system 200. In some embodiments, the control system 200 and the user device(s) 106 may have a peer-to-peer relationship in which the control system 200 and the user device(s) 106 are considered equal and all have the same abilities to use the resources available on the network 104. Instead of having a central server which would act as the shared drive, each device that is connected to the network 104 would act as a server for the files stored on it.

[0240] The control system 200 may include a server, which may represent various forms of servers, such as web servers, database servers, file servers, or the like, various forms of digital computing devices, such as laptops, desktops, video records, audio / video players, radios, workstations, or the like, or any other auxiliary network devices, such as wearable devices, Internet-of-thins devices, mainframes, or the like, or any combination of the aforementioned.

[0241] Figure 2 illustrates an exemplary component level structure of the control system 200, in accordance with an embodiment of the disclosure. In some embodiments, and as shown in Figure 2, the control system 200 may include a memory 206, a processor 202, a communication interface 204, and / or an artificial intelligence (AI) model 208. Each of the components 202, 204, 206, and 208 may be operatively coupled to one another using various buses and may be mounted on a common motherboard or in other manners as appropriate. As described herein, the processor 202 may include a number of subsystems to execute the portions of processes described herein. Each subsystem may be a self-contained component of a larger system (e.g., control system 200) and capable of being configured to execute specialized processes as part of the larger system. Page 68 of 171 13893549v1

[0242] The processor 202 may process instructions, such as instructions of an application that may perform the functions disclosed herein. These instructions may be stored in the memory 206 (e.g., non-transitory storage device), or on the database 108, for execution within a server, the control system 200, or the like, using any subsystems described herein. It is to be understood that the control system 200 may use, as appropriate, multiple processors, along with multiple memories, and / or I / O devices, to execute the processes and methods described herein.

[0243] The memory 206 may store information within the system 100, the server, the control system 200, or the like. in some embodiment, the memory 206 may be a volatile memory unit or unties, such as volatile random access memory (RAM) having a cache area for the temporary storage of information, such as a command, a current operating state of the system 100, an intended operating state of the system 100, instructions related to various methods and / or functionalities described herein, and / or the like. the memory 206 may store, recall, receive, transmit, and / or access various files and / or information used by the system 100 during operation.

[0244] The communication interface 204 may be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and / or transmit data, including media content in the form of video or image files, one or more audio tracks or the like. In this regard, the communication interface 204 may include, for example, an antenna (or multiple antennas) and supporting hardware and / or software for enabling communications with a wireless communication network. Additionally, or alternatively, the communication interface may include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). In some environments, the communication interface may alternatively or also support wired communication. As such, for example, the communication interface may include a communication modem and / or other hardware / software for supporting communication via cable, digital subscriber line (DSL), universal serial bus (USB) or other mechanisms.

[0245] The communication interface 204 may provide for communications under various modes or protocols, such as the Internet Protocol (IP) suite (commonly known as TCP / IP). Protocols in the IP suite define end-to-end data handling methods for everything from packetizing, addressing and routing, to receiving. Broken down into layers, the IP suite includes the link layer, containing communication methods for data that remains within a single network segment (link); the Internet layer, providing internetworking between Page 69 of 171 13893549v1independent networks; the transport layer, handling host-to-host communication; and the application layer, providing process-to-process data exchange for applications. Each layer contains a stack of protocols used for communications. In addition, the communication interface 204 may provide for communications under various telecommunications standards (e.g., 2G, 3G, 4G, 5G, and / or the like) using their respective layered protocol stacks. These communications may occur through a transceiver, such as radio-frequency transceiver. In addition, short-range communication may occur, such as using a Bluetooth, Wi-Fi, or other such transceiver (not shown).

[0246] In some embodiments, the control system 200 may deploy one or more machine learning (ML) models to perform the operations described herein. To this end, the control system 200 may include a machine learning (ML) module comprising circuitry configured to ingest data, such as a multivariate N-dimensional space of time-series data where N is the number of different measurement types (e.g., data of different types) and, via a various ML and / or artificial intelligence techniques described hereafter, and output location determinations, interaction determination, state determination, and / or the like. The ML module may leverage the processor 202 to perform its associated operations and may, for example store any results in the memory 206 and / or databases 108.

[0247] In some embodiments, the control system models (e.g., a control system 200 or one or more control systems 200), methods and / or logics described herein may use parameters and / or contextual condition data to apply these kinds of normalization to understand the underlying behavior of a material for a target contextual condition, material characteristic, material parameter, or the like. Additionally or alternatively, these kinds of procedures may be done for example during control system model training. Additionally or alternatively, in some embodiments, the models herein may be trained based on simulations (e.g. simulating concrete’s real thermal evolution), or based on physico-chemical models. Additionally or alternatively, in some further embodiments, contextual condition data may be generated and / or collected from sensors (including any of the sensor devices described herein), records (e.g. digital records), batching plant specifications (e.g. specifying silo dimensions or other), machine data which may be integrated or automatically collected by the control system (e.g. recorded mixing rate for the mixer). Additionally or alternatively, the context awareness methods herein may include adjusting predicted output data associated with a material (e.g. predicted target performance and / or properties and / or behavior) based on contextual condition data. In some embodiments, the models herein may output contextual material properties that belong to a certain position and / or time, or that are spatially Page 70 of 171 13893549v1distributed in the instantiated material as a field of values throughout its body and / or surface, or that are temporally distributed (as a time series). In the case where values are not spatially or temporally dependent (such as the average workability of a batched cementitious material), the models herein may account for the contextual material property as a “bulk” value (independent of spatial position) and / or a “constant” value (independent of time).

[0248] In some embodiments, the control system 200 may be configured to control or otherwise influence operations of the project, construction project, construction process, or the like. In particular embodiments, the process as shown in Figure 3 may relate to a batching process at a batching plant. A batching plant may include processes used to produce concrete by combining various raw materials (e.g., the portions of the building material) in specified proportions. In some embodiments, the portions of the building materials may include cement, aggregates (e.g., sand, gravel, etc.), water, admixtures, fresh concrete, gases such as air and / or carbonation, and the like. Further, in some embodiments, the materials may include the portions of the building materials (e.g., the first portion, the second portion, and so on) or any other constituent or discrete elements or components used to create a building material.

[0249] It is to be understood that Figure 3 is for example purposes only and not intended to limit the scope, spirit, or functionalities of the solutions as described herein. For example, Figure 3 shows a portion of a construction process, wherein the entire construction process may also include the solutions as described herein. In this way, the solutions, methods, processes, systems, and the like as described herein may be applied to the other portions of the construction process. In other embodiments, the solutions as described herein may be applied to industries other than the construction industry, wherein the solutions, explanations, and descriptions provided herein may be tailored to other industries, as well. For example, the solutions described herein may be applied to other industries wherein materials may be commonly stored in containers e.g., the storage of grains and / or other powdered materials such as granulated or liquid fertilizers, animal feed, in the food and agriculture industries. Additionally or alternatively, examples may include storage of chemicals in the chemical industry such as acids, bases, solvents, and polymers in drums, tanks, or Intermediate Bulk Containers. Additionally or alternatively, examples may include storage of tablets or capsule raw material in the pharmaceutical industries such as granules and powders stored in bins or hoppers. Additionally or alternatively, in some embodiments, the solutions herein may apply to powdered materials stored in containers across industries. This may include the following examples and the like in the foods industry: flour; sugar; spices like paprika or cinnamon; milk powder; cocoa powder; salt. Additionally or Page 71 of 171 13893549v1alternatively, this may include the following examples and the like in the pharmaceuticals industry: active pharmaceutical ingredients (APIs); excipients like lactose or microcrystalline cellulose; granulated medicines. Additionally or alternatively, this may include the following examples and the like in the chemical industry: fertilizer granules; detergent powders; plastic granules (pellets); carbon black powder. Additionally or alternatively, this may include the following examples and the like in the metals industry: iron ore pellets; coal dust; limestone powder. Additionally or alternatively, this may include the following examples and the like in the cosmetics industry: talcum powder; mica powder; powdered dyes; kaolin clay; zinc oxide powder; silica microspheres. Additionally or alternatively, this may include the following examples and the like in the agriculture industry: animal feed granules; pesticide powders; soil amendments such as lime or gypsum; granular fertilizers such as urea or phosphate; seed coatings; powdered animal supplements such as calcium carbonate.

[0250] Additionally or alternatively, this may include the following examples and the like in the energy industry: Charcoal granules; lithium powders; sulfur granules; graphite powder. In some embodiments, the sensing solutions described herein may be applied to the above list of industries and materials. Additionally or alternatively, this may include applying the sensing solutions herein to materials such as the material listed above, stored in containers across other industries. Additionally or alternatively, the material portion determination methods described herein may be applied to other material processes across other industries wherein one or a plurality of raw materials may be mixed, combined, or otherwise placed together in predetermined portions or proportions. For example, this may include the pharmaceuticals industry wherein APIs may be mixed with binders, fillers and lubricants. Additionally or alternatively, for example, this may include fertilizer production, wherein nitrogen, phosphorus, and potassium compounds may be blended to create fertilizers (e.g. NPK fertilizers). Additionally or alternatively, for example, this may include steel manufacturing, wherein iron ore, carbon (coke), and limestone may be combined. Additionally or alternatively, for example, this may include battery manufacturing, wherein lithium, cobalt, and electrolyte compounds may be combined. Additionally or alternatively, for example this may include adhesive production, wherein epoxy resins and hardeners may be mixed to ensure desired curing behavior.

[0251] In some embodiments, the batching plant process as shown in Figure 3 may include a variety of subprocesses that may be performed on the building material and / or the portions of the building material. In some embodiments, the control system 200 may modify the building material as it moves through the process as shown in Figure 3. For example, the Page 72 of 171 13893549v1control system 200 may control one or more stages of the construction process. In some embodiments, the stages may include one or more substages of a construction process. For example, substages may relate to the sub-processes carried out within a particular stage of the construction process. For example, Figure 3 illustrates a batching plant stage of a construction process, wherein one or more substages may be included within the stage. In some embodiments, the substages may include a first substage 302, a second substage 304, a third substage 306, a fourth substage 308, a fifth substage 310, a sixth substage 312, a seventh substage 314, an eighth substage 316, a ninth substage 318, or the like. In some embodiments, each substage may be configured to modify or influence a building material moving through the construction process. For example, the building material may begin the process at the first substage 302 and ultimately arrive at the ninth substage 318, as shown in Figure 3. It is to be understood that Figure 3 may be a portion of a larger construction project and the construction project may include other various stages. In this way, the construction project may include a building material extraction site, a loading site, a transportation site, a project site, a testing site, or the like.

[0252] In some embodiments, an example embodiment illustrating the material flow from a batching plant stage to a pour stage is provided. In some embodiments, the raw materials may be prepped which may include aggregates, cement, water, and admixtures. In some embodiments, the aggregates may go from a truck to a storage container, then to a hopper used to weigh a required amount for the batch, then to a conveyor below and / or pump, then to a mixer. In some embodiments, the cement may go from a truck to a storage container (e.g., silo), to a hopper used to weigh the cement for the batch, to a pump / conveyor belt, to a mixer. In some embodiments, the water may be pumped from a water tank using a flow meter to determine the correct amount of water to add to the mixer. In some embodiments the admixtures may go to a mixer using a flow meter to determine the correct amount of admixtures, or to a hopper used to weigh the required amount for a batch, then to the mixer. In some embodiments, the materials added to the mixer may follow a typical order such as aggregates, cement, water, admixtures, however this is not limiting. Further, in some embodiments, the water and / or admixtures may be dynamically added. In some embodiments, the mixer may ensure the materials become mixed. In some embodiments, the fresh concrete goes from the mixer to a truck drum, where the truck delivers the concrete to the site and the concrete goes from the truck to pour through a pump.

[0253] In some embodiments, an amount of raw materials in a batch may be determined by volume or weight. For example, the amount of binder, aggregate, water, Page 73 of 171 13893549v1admixtures may be determined by volume or weight. Further, any ratio / proportion of any of the raw materials may be used (e.g., the water to binder ratio or the water to cement ratio). In some embodiments, the sequencing and timing of when the control system 200 adds each material to the mixer may include using control parameters and / or contextual conditions. For example, the mixing parameters used by the control system 200 may include the rate of mixing, rotation speed of the mixer, and / or the duration of mixing. Further, the contextual conditions may include conditions in a container, for example, and may include temperature and humidity. Further, the material flow rate may be used in some embodiments to determine how the flow control gates are controlled (e.g., opened or closed).

[0254] Further, with reference to Figure 3, the first substage 302 may include cement silos, which may be used for long-term storage of binders such as cement or fly ash, protecting it from moisture or contamination. The second substage 304 may include one or more hoppers, which may be funnel-shaped containers for temporary storage, weighing, or controlling a discharge of the building material. The third substage 306 may include a conveyor belt (or other material transport systems e.g., pneumatic systems, pumps, pipes, and the like) used to transport weighed aggregates and other materials to the next substage. Further, in some embodiments, the substages herein may include one or more discharge systems used to get materials into or out of a container, which may include hatches and / or values. The fourth substage 308 may include a water tank used to store and measure water for precise addition during mixing. The fifth substage 310 may include a mixing unit (batch mixer) used to combine all ingredients into a uniform concrete mix, ensuring proper hydration and blending. The sixth substage 312 may include a conveyor belt used to transport the material to another substage. The seventh substage 314 may include admixture tanks and pumps used to store and does liquid or powdered chemical admixtures to modify concrete properties. The eighth substage 316 may include aggregate storage containers that may store and segregate coarse and fine aggregates, providing quick access for batching and also an aggregate weigh hopper used to precisely measure materials to meet mix designs. The ninth substage 318 may include a truck or transport system to deliver the building material to another site or location. Further, in some embodiments, the stages and / or substages as described herein may include one or more mixers, which may include a batching plant mixer and / or a truck drum mixer. Further, in some embodiments, the stages and / or substages described herein may include one or more weighing systems.

[0255] The control system 200 may be used to automate and monitor batching processes, ensuring accuracy, efficiency, and consistency. Further, in some embodiments, Page 74 of 171 13893549v1sensors 102 may be associated with the jobsite and / or pour site (e.g., the project stage 1608 as shown in Figure 16), which may also generate data that may be used in the control system 200. For example, the data collected / generated by sensors 102 at the pour site may be used to make decisions about portions and / or proportions of material going into a batch.

[0256] In some embodiments, a building material 101 may include any material used in a construction or building process to construct or create an element, building, structure, or the like. For example, a building material may include concrete, concrete ingredients / aggregates, a cementitious mix, admixtures, wood, steel, glass, MEP (mechanical, electrical, plumbing) assemblies, rebar, etc. In some embodiments, a building material may be a homogenous material that includes the same uniform material throughout. In other embodiments, a building material may include composite materials that include one or more materials. For example, a building material may include reinforced concrete, wherein the reinforced concrete includes concrete and rebar.

[0257] For example, a building material may include a binder. Examples of binders and / or binder types for which any of the systems and / or methods herein may be applicable includes the following examples and the like: Portland cement; fly ash; ground granulated blast furnace slag (GGBFS); silica fume; metakaolin; limestone powder; natural pozzolans; calcined clay; high-alumina cement; magnesium phosphate cement; gypsum; hydrated lime; alkali-activated binders; geopolymer binders; rice husk ash; volcanic ash. The sensor solutions herein (including any sensor solution described herein for cement sensing) may be used to sense any one of those binder types. Additionally or alternatively, they may also be used to spatially map a volume of binder e.g. stored in a container. The methods and solutions associated with the control system described herein may be applied to any binder type including the ones listed herein, and may include methods, systems and / or solutions to adjust, modify, or otherwise any parameter associated with any of these binders, including their proportion in a batch, and any contextual conditions within which they may be stored, batched, mixed, or otherwise handled.

[0258] Further, in some embodiments, an aggregate may include a variety of materials. Examples of aggregates may include natural aggregates; crushed stone; gravel; sand; recycled concrete aggregate (RCA); lightweight aggregates; expanded clay aggregate; expanded shale aggregate; expanded slate aggregate; pumice; perlite; vermiculite; slag aggregate; steel slag; synthetic aggregates; glass aggregate; polymer aggregate; recycled plastic aggregate; crushed brick; volcanic rock (basalt); limestone; granite; marble; quartzite; scoria; diabase; chalk; flint; crushed shells; coconut shell aggregate; palm kernel shell Page 75 of 171 13893549v1aggregate; crushed ceramics; geopolymer aggregate; metallurgical by-product aggregate; copper slag; iron ore tailings; recycled rubber aggregate; coal bottom ash; crushed porcelain; sintered fly ash aggregate; natural fiber aggregate.

[0259] Further, in some embodiments, an admixture may include a variety of materials. Examples of admixtures may include air-entraining admixtures; water-reducing admixtures; superplasticizers; mid-range water reducers; accelerating admixtures; retarding admixtures; shrinkage-reducing admixtures; corrosion-inhibiting admixtures; waterproofing admixtures; permeability-reducing admixtures; viscosity-modifying admixtures; bonding agents; air-detraining agents; alkali-silica reaction (ASR) inhibitors; carbonation inhibitors; freeze-thaw resistance admixtures; lightweight concrete admixtures; heavyweight concrete admixtures; coloring agents; pigments; anti-washout admixtures; hydration-control admixtures; set-controlling admixtures; pozzolanic admixtures; silica fume; fly ash; ground granulated blast furnace slag (GGBFS); metakaolin; recycled polymer admixtures; recycled tire fiber admixtures; fiber-reinforcing admixtures; steel fiber admixtures; synthetic fiber admixtures; glass fiber admixtures; microbial self-healing agents; biopolymer admixtures

[0260] Further, in some embodiments, a material is deemed to possess one or more of a set of “static material properties”, “compositional properties”, “contextual conditions” and “contextual material properties.” In some embodiments, these may be defined as Static Material Properties (e.g., an attribute or state of a material that is deemed to be independent of any context within which the material is used (e.g. the density of water, the average size of sand particulates, etc.)); Compositional Properties (e.g., a measure of the proportions by which a material is formed of other materials (e.g. 300kg of fly ash, 30% water, 20% Tricalcium Cilicate etc.)); Contextual Conditions (e.g., an imposed state or attribute that partially or wholly defines the instantiated context in which a material is used (e.g. time, temperature, geometry, atmospheric humidity, location, altitude etc.), wherein a contextual condition can be an aspect of the material itself or of the environment); Contextual Material Property (e.g., a material property that is context-dependent, and will (generally) change with differing contextual conditions (such as the compressive strength of a cementitious mix — which increases over time, and in a manner that is dependent upon temperature, geometric shape etc.). Some other non-exhaustive examples of contextual material properties are: durability, pumpability, workability, aesthetic finish, flexural strength, exothermicity, endothermicity etc.

[0261] With respect to the above categories of data, materials may be included in one of two categories: Raw materials: A raw material is one that possesses only static material Page 76 of 171 13893549v1properties in a database (e.g. water, fly ash, sand; where each might possess only a density and pH in the database). Composite material: A composite material is one that is identified by both static material properties and compositional properties in the database (e.g., a mortar formed of 50% cement and 50% sand and having a density of 3000 kg per cubic metre, etc.).

[0262] With respect to the above categories of material, it should be clear that a “cementitious mix” is a composite material that: is formed of raw materials in various proportions, will always possesses a set of static material properties, may or may not possess some set of contextual material properties (depending on the contextual conditions of its use in some instance). It should be clear that both raw materials and composite materials can be instantiated in a given use-case and may thus have contextual conditions or material properties associated with them. It should also be clear that, in one definition, the “identity” of a material may be distinct from any context in which it is used, and that a material’s “identity” may be equivalent to a material’s total set of context-independent properties (i.e. its compositional and static material properties). For example, under this definition of identity, if a cementitious mix (Mix A) were poured into: 1. A cube, and placed inside a controlled temperature bath within a laboratory, or 2. A large floor slab on the top floor of a skyscraper (in open air), one would expect the contextual material properties (such as compressive strength over time, slump, etc.) to differ in each of these contexts, regardless of the fact that both are instances of the same composite material.

[0263] The above however, only represents one definition of identity. The mix fingerprinting section of this invention explores both this, but also other definitions of mix identity (some which may depend on contextual conditions). In some embodiments, there exists a subset of contextual conditions that have a noticeable impact on the contextual material properties of a mix and will have a noticeable impact in most use-cases / contextual conditions. It is a feature of one or more models that they are able to learn or discover what these relevant contextual conditions might be, and that they will assign an appropriate embedding weight to each, whilst essentially discarding non-relevant contextual conditions, or assigning much smaller embedding weights to them based on historical data. On the other hand, the system may also choose to ‘infuse’ the models with pre-existing, empirical knowledge of these relevant contextual conditions: for example, through the insertion of physico-chemical equations. Doing so may lessen the burden on the models to learn on their own and may allow them to start with a baseline set of empirical knowledge from which to work. This need not be a static data grouping. The models may, based on the data they ingest, discover that a contextual condition previously believed to be highly relevant is, in fact, less Page 77 of 171 13893549v1relevant than expected (altering its weights accordingly). The models may also discover that a contextual condition (of which it was not previously aware) actually has a significant impact on the material behavior of concrete. In this sense, the models may be used to discover unknown relevant contextual conditions. Some relevant contextual conditions which typically may influence a mix or any mix’s raw and / or intermediate materials’ contextual material properties are listed in section B.1.1.2 below. That section includes other data types which may also be of relevance (non-exhaustive). These may include Sensor Contextual Conditions (aka Context Awareness Data, Self-Detection Data, and / or Sensor Context Data): As defined in Self-Detection section; Supply Chain and Resource Availability Data: Data relating to availability of resources across the supply chain and other relevant supply chain information; Source / Supplier Data: Information relating to the sourcing of materials which compose the mix & information relating to the supplier of such materials; Dependency Data: Data representing the dependencies between entities e.g. temporal dependency between two pours.

[0264] The list below includes certain examples of relevant data grouped by data types. This list is representative and non-exhaustive. The categories are also non-rigid (e.g. temperature data may be a contextual condition or a contextual material property depending on the context), but representative. For example, these may include density, homogeneity, average particle size, average aggregate particle size or aggregate grading, average binder particle size or fineness, specific gravity, natural variability data, data about natural variability in raw materials quality or inhomogeneity and natural batch variability, embodied carbon data, aggregate grading, and porosity.

[0265] Further, material contextual conditions may include: temperature data, insulation data, formwork type, formwork coating, blankets, structural data, including data pertaining to geometry, physical form, structure, layout, arrangement, configuration, and content (e.g., rebar) of a pour, element type data, geometry or dimensional data, exposure data (e.g., surface area of concrete exposed to air or to other materials such as formwork), reinforcement geometry data (e.g., rebar data), information about general surroundings, environmental data, meteorological data, ambient temperature data, humidity data, precipitation data, other atmospheric effects, wind data, storms and lightning data, electromagnetic radiation data, mechanical vibration and other mechanical disturbances, geological data (e.g., the type of soil surrounding foundations that may affect its behavior), oven data (e.g., ovens used to influence curing in precast settings), structural burden data such as load data, load path data, stress data, and strain data, batching plant data such as Page 78 of 171 13893549v1volume of batch and mixing data (e.g., measure of mixing intensity or rate of rotation), pump contextual condition data, truck contextual condition data, volume of load, truck rotation data (e.g., rotational velocity), kiln contextual condition data such as temperature inside the kiln, raw materials inside the kiln, volume of materials inside the kiln (e.g., of raw materials, desired output materials, or waste materials), temporal data, including any information used to denote a time or timeframe (e.g., date, time, period of time, season, year, daytime / nighttime, stage of construction), and timestamp or date stamp data (e.g., associated with sensor measurements), are all critical data points. This timing data is often associated with specific processes, such as estimating the time when one or more pours must occur to maintain deadlines, inferred from a schedule. Processes generally include defined start and end times.

[0266] In some embodiments, sensor contextual conditions may include sensor metadata includes the name of a sensor (e.g., as provided by a user), sensor context awareness data as described in other sections of the invention (e.g., the location of a sensor, both relative and absolute, which may be determined using mesh network time of flight to position sensors relative to each other), orientation of a sensor, RF transmission of a sensor, battery consumption of a sensor, and the lifetime of a sensor since activation. These data points may evolve over time, which is generally the case for any data listed herein.

[0267] In some embodiments, the data herein may generally be time dependent, and may be formed of discrete, or continuous time series data. The data herein may be qualitative, quantitative and / or measured or predicted. The data may also be location dependent, particularly for strength, slump and shrinkage. For example: Compressive strength data, including 7-day strength, 28-day strength, 42-day strength, and the full strength profile from time zero to the present, shrinkage data, workability or slump data, tensile strength data, flexural strength data, stress data, strain data, calibration data (e.g., maturity calibration data), structural health data (particularly long-term structural health data that could be monitored by sensors over extended periods), reactivity (e.g., cement reactivity), flow rate data (e.g., for concrete through a pump), and specific surface area are critical data points for assessing and optimizing material performance and behavior in various applications.

[0268] Compositional Properties in some embodiments may be used interchangeably with “Composition.” These may take the form of mix recipes / composition / properties, at any level of granularity. From the proportions of water, aggregate and cement, down to a compositional breakdown of the mix by atomic element, and anything in between (e.g. composition by main materials). They could include any of the following examples below Page 79 of 171 13893549v1(non-exhaustive). The composition may be listed as a percentage of volume, by particle number, by mass or any other relevant metric. It may also not need to be listed as a percentage at all (for example, the composition may be listed by absolute mass, or by mass density). Any conceivable way to convey compositional information may be used. Mix A by atomic composition (illustrative, and not comprehensive of any element which could compose a mix): Carbon (C) at X%, calcium (Ca) at X%, oxygen (O) at X%, silicon (Si) at X%, aluminum (Al) at X%, iron (Fe) at X%, hydrogen (H) at X%, sulfur (S) at X%, sodium (Na) at X%, magnesium (Mg) at X%, nitrogen (N) at X%, potassium (K) at X%, titanium (Ti) at X%, and manganese (Mn) at X%, etc. Mix B by compound composition includes tricalcium silicate (3CaO ⋅ SiO2) at Y1%, also known as C3S, dicalcium silicate (2CaO ⋅ SiO2) at Y2%, also known as C2S, tricalcium aluminate (3CaO ⋅ Al2O3) at Y3%, also known as C3A, tetra- calcium aluminoferrite (4CaO ⋅ Al2O3Fe2O3) at Y4%, also known as C4AF, and water (H2O) at Y5%. Mix C by molecular composition includes calcium oxide (CaO) at Z1%, represented as C in cement chemist notation; silicon dioxide (SiO2) at Z2%, represented as S in cement chemist notation; aluminum oxide (Al2O3) at Z3%, represented as A in cement chemist notation; iron oxide (Fe2O3) at Z4%, represented as F in cement chemist notation; water (H2O) at Z5%, represented as H in cement chemist notation; sulfate / sulfur trioxide (SO3) at Z6%, represented as S in cement chemist notation; magnesium oxide (MgO) at Z7%, represented as M in cement chemist notation; sodium oxide (Na2O) at Z8%, represented as N in cement chemist notation; manganese oxide (MnO) at Z9%; potassium oxide (K2O) at Z10%, represented as K in cement chemist notation; phosphorus pentoxide (P2O5) at Z11%, represented as P in cement chemist notation; titanium dioxide (TiO2) at Z12%, represented as T in cement chemist notation; and carbon dioxide (CO2) at Z13%. Mix D by raw materials composition includes Portland cement at W1%, water at W2%, aggregates at W3%, admixtures at W4%, and fibers at W5%.

[0269] Compositional properties may also evolve over time, meaning the recipes described above are actually time-evolving. Data about this time evolution may, in some embodiments, be referred to as post-batching compositional adjustment data, such as adding water during transit. This data may reflect known or predictable user behavior that can be accounted for or may be generated as part of an output mix design. In such cases, the mix recipe, mix composition, or mix design would be time-evolving, with step-by-step instructions provided to produce a given recipe rather than simply listing static compositional properties. Compositional properties may also be captured at specific snapshots in time, such Page 80 of 171 13893549v1as compositional properties at batching, compositional properties at a certain point in time during transit, or compositional properties during the pour. Supply chain and resource availability data include supply chain entity data, such as a stakeholder map of entities involved in the construction project with their associated roles, and material availability data, which encompasses raw materials, rebar, and other materials at both local and global levels. Fleet data, including the number of trucks available for concrete delivery, their locations, and estimated times of arrival (ETAs), is also included. Additional data pertains to forms availability, fixtures availability, backprops availability, price lists, and material costs. Source and supplier data capture material sourcing information, detailing how, where, and by whom a material was gathered, processed, or manufactured, as well as quantitative supplier data, such as organization size or reputation score. Dependency and linkage data represent dependencies between entities, such as temporal dependencies between two pours, or linkages between entities, such as a map linking cube test results to strength time series data from sensors. These dependencies and linkages may be generated using the models described in the linkage portion of this invention.

[0270] In some embodiments, the sensors 102 may be used to sense any of the foregoing or any of the properties as described herein or any distribution of them for a collection and / or volume of material (e.g., distribution of rounded aggregates, angular aggregates, sub-angular aggregates, flaky aggregates, elongated aggregates, and irregular aggregates in a volume / collection of aggregates).

[0271] Further, as shown in Figure 3, the control system 200 may control each of the substages (e.g., substages 302-218) associated with the stage of the construction process. In this way, the control system 200 may, via the communication interface 204, transmit instructions or operations to the substages 302-318 as shown in Figure 3 in order to modify the building material as it moves through the process. In some embodiments, the control system 200 may determine modifications of the building material based on sensor data received from the one or more sensors 102. The sensors may be configured to provide data relating to the building material as it moves through the process, as it is stored, as it is modified, or the like. For example, the sensors 102 relating to the first substage 302 may provide the control system 200 with information about storage conditions of the building material.

[0272] As used herein, a “building material data entity” may include data associated with a building material. The data entity may be a portion of data related to the building material (e.g., the building material 101) that is analyzed and / or modified throughout a Page 81 of 171 13893549v1particular construction process. The building material data entity may be data recorded, ingested, tracked, analyzed, or the like based upon the building material as it flows through the process. For example, a building material data entity may be data associated with a building material as it begins the construction process at the first stage of the construction process. In this example, the building material data entity may include the data received from the one or more sensors 102 configured to measure material properties of the building material 101. Further, the building material data entity may include sensor data from the one or more additional stages of the construction process. Further still, the building material data entity may be tagged, linked, aggregated, processed, cleaned, or the like to distinguish the data associated with the building material at each stage. Additionally, or alternatively, the building material data entity may include the measured properties for the portions of the building material. In this way, the building material data entity may include material properties for one or more portions of the building material that are combined to create the building material. For example, the building material data entity may relate to the one or more portions (e.g., aggregate, water, cementitious mixture, etc.) of the building material.

[0273] In some embodiments, the building material data may include a data object which may be a material identifier that may include a material ID, a material name, and / or a material type. By way of example, a C40 mix may be defined as a concrete mix that may reach a minimum of 40 MPa of compressive strength by 28 days, if cured as a standard cube (or cylinder) in standard conditions (in a temperature-controlled water bath at a fixed temperature). A C60 has an identical definition but must reach a minimum of 60 MPa instead. Therefore, material identifiers (e.g., names) hold information regarding the particular mixes the models described herein are operating upon. Additionally, or alternatively, material identifiers considered by the present disclosure may include a non-exhaustive list of material types including pure materials, composites, and / or material mixes (e.g., a cementitious material, water, aggregates, admixtures, plasticizers, fibers, and / or the like).

[0274] In some embodiments, the operation of the system 100 may include data linkages, determinations, predictions, recommendations, or generating content. In this regard, the building material data entity may be further processed by the system 100 to analyze the building material data entity further. For example, the determinations generated based on the building material data entity may include comparing it against a rule (e.g., a crush rule), performing statistical calculations, performing carbon calculations, or the like.

[0275] Further, the system 100 may determine which operational step is appropriate to provide a deep analysis on the building material data entity. In this regard, a request for a Page 82 of 171 13893549v1particular output may be received that may indicate that a particular operation should be performed. For example, the system 100 may determine that a particular building material 101 is too dry, and more water should be added to the mix. In this way, the system 100 may control the control system 200 to activate a valve or other system to allow water to enter the building material 101 as it is being created. Further, the control system 200 may analyze the amount of water (e.g., by weight or by volume) as it is being added to the building material in order to track, record, and analyze the moisture content of the building material.

[0276] In some embodiments, the plurality of data records may include real-time data, or near real-time data, collected from multiple stages of the building material’s lifecycle. In this way, near real-time may include microseconds, seconds, minutes, hours, days, weeks, months, years, decades, or centuries, or any appropriate timeframe. For example, this may allow for end-to-end tracking of the embodied carbon, quality control metrics, or volumetric information passed along to various project stakeholders. The system 100 may facilitate automating the collection, linkage, processing, analysis, and the like of the data at every stage, and may incorporate real-time sensor data to further process and enhance the data analysis. The real-time aspect of the system 100 may enhance the overall efficiency and accuracy of control system (e.g., the control system 200) configurations, as described herein. By integrating real-time data, which may originate from a variety of sources as described herein, the system 100 may provide instantaneous (or near-instantaneous at any order of magnitude of time delay) updates on material conditions, performance, environmental factors, and the like. In this way, the control system 200 may be configured or reconfigured to adjust for the real-time conditions of the building material 101.

[0277] For instance, and by way of non-limiting example, data ingested from sensors embedded in a particular concrete mix may monitor temperature, humidity, or curing time of the concrete on-site that will provide data used in determining whether adjustments to the process are needed. In this way, the real-time data may also feed directly into the system to provide continuous tracking of the building material as it moves throughout the construction project.

[0278] Additionally, or alternatively, the system may process and analyze the ingested data in real-time simultaneously along with historical records, which may offer up- to-date insights on quality and status metrics related to the building material. For example, real-time quality analysis may ensure the building materials meet required compliance specifications prior to installation, which reduces delays associated with faulty material installations. In this way, the building material data entity may be generated based on the Page 83 of 171 13893549v1comparison of the datasets, which may include differences from an actual and expected performance of the building material data entity. For example, the data records used for compliance specifications may be received by the system 100, the control system 200, a user device 106, or the like via the network 104.

[0279] In some embodiments, the processes and / or operations carried out by the control system 200 may include receiving a material in an input material state, performing a process, and outputting the material in an output material state. In this way, the input material state may be a state in which the material exists prior to a process or operation being performed on it. The process may include configuring the material in a particular way to produce a particular result. The output material state may be the state in which the material is in after the process has been performed. For example, discharging a material from a storage container to a hopper may initially be in an input material state in the storage container and, after the process of discharging the material, the material may transform into the output material state of being in the hopper. In an additional example, the input material state may include the material in the hopper, the process may include discharging the material to the mixer, and the output material state may be the material in the mixer. Further, a process may include weighing a material, wherein the input material state is the material (e.g., aggregates, cement, or the like) prior to being weighed and the output material state is the control system 200 knowing the weight of the material. Mixing may also be another process, wherein the input material state is the material prior to being mixed, and the output material state is the mixed material. In this way, the mixing may be performed in a variety of containers (e.g., containers 303) including the batching plant mixer in substage 310 and the truck mixer shown in substage 318 or in stage 1604 of Figure 16.

[0280] Further, in some embodiments, the control system 200 may alter the batch in the batching plant stage (e.g., batching stage 1606) based on output from another stage (e.g., the pour stage 1608). For example, as shown in Figure 17, one or more sensors 102 may be installed in the pour stage 1608. In some embodiments, the sensors 102 may generate data relating to the material properties of the building material 101 as it is being installed and / or tested. These sensors 102 may transmit the data to the control system 200. In some embodiments, the control system 200 may then alter the batching recipe at the batching plant. For example, as shown in block 1702 of flow 1700, the control system 200 may determine one or more characteristics associated with the first portion of the building material. In this way, the first portion of the building material 101 may be the portion at the pour site 1608. As shown in block 1704, the control system 200 may modify one or more parameters of the Page 84 of 171 13893549v1building material 101. In this way, for example, the control system 200 may alter the batching plant mix recipe of the current batch of mix.

[0281] In some embodiments, the control system 200 may control one or more parameters associated with the stage and / or substage. In some embodiments, for example, the control system may control the mixing substage which may include the speed of rotation of the mixer (e.g., the speed of the mixer paddles or of a truck drum rotation). Further, in some embodiments, the control system 200 may determine between dry batching (e.g., adding water later in the process) and wet batching. Further, in some embodiments, the control system 200 may determine the proper timing for when to batch (e.g., logistics of when materials are batched). In this way, the control system 200 may account for batching orders, trucks arriving, availability of materials, and the like.

[0282] As mentioned previously, and in some embodiments, the control system 200 may include one or more feedback loops. In some embodiments, the loops may include analyzing raw and / or input material data to determine batching properties. Further, in some embodiments, the raw and / or input material data along with the output material data to determine batching. Further still, in some embodiments, the raw and / or input material data and output material data and a prediction from raw material to the output material may be used to determine batching. Additionally, or alternatively, in some embodiments, data associated with the material may be generated throughout the construction process (e.g., as the material is being mixed) which can be used to predict an output material as well as determine batching properties. In this way, determining the batching properties for any of the foregoing may include adjusting the portions of materials (e.g., raw or input materials), or the processes performed on the materials (e.g., mixing time, temperature, etc.) to adjust the final building material produced at the batching plant.

[0283] Further, in some embodiments, and as shown in Figure 21, the control system 200 may include a generalized model illustrating the inputs to the control system 200 and the outputs and / or parameter adjustment determinations. In some embodiments, the models may be multi-modal models that may be configured to take textual, image, and / or other documentation formats as inputs, as well as compositional information, material properties, and real time information captured by classical, quantum, and / or hybrid sensors (e.g., of chemical spectra at varying frequencies, processed using Fourier methods and other methods herein). Sensor data may include geometrical or topological data associated with material structure (e.g., molecular structure, unit cells, and / or quantum structures). Said data may be captured through spectroscopy techniques and sensor devices (e.g., Raman spectroscopy and Page 85 of 171 13893549v1X-Ray crystallography-based methods). Additionally, and / or alternatively, training data may include chemistry and materials engineering, quantum chemistry, and engineering documentation including textbooks and others. This may infuse existing physico-chemical understanding into the models herein.

[0284] Further, in some embodiments, embodiments of the present disclosure may include a plurality of types of execution models. For example, a type of execution model considered by the present disclosure may be an AI model configured as a hybrid AI + physico chemical model. One type of model may be a hybrid physico-chemical and AI model. In such a model, knowledge of the laws of nature may be embedded in the algorithms and used in conjunction with AI algorithms, allowing the model to physically and chemically reason about the systems it is treating and therefore provide explainability for the solutions it reaches, whilst also learning from the provided data using deep learning approaches. Scientific principles may thus be used to augment these deep learning models, which may in turn, use the historical data they gather to refine their understanding. This combination may also be described as the model being infused and / or embedded with physico-chemical knowledge, which may be particularly useful for treating complex problems especially with ill-labelled or incomplete data.

[0285] Further, in some embodiments, the physico-chemical and empirical AI components may work together to provide a solution that may be based on a multi-modal internal representation (in which some aspects of the internal solver are based on physical and / or chemical principles, and other aspects on learned representations from empirical data). One instantiation of such a model may be a Convolutional Neural Network (CNN), which may be used as a means to construct an ensemble model, such that the weights of the convolutional networks bias the outputs of several sets of physical or chemical equations, each of which contributing to various aspects of the final model output. The weights of the convolutional neural networks might be learned over the set of empirical data available, whereas the physico-chemical equations that are being weighted by the neural network may have been derived from pre-existing theory. For example, one such physico-chemical component might encode a functional representation of how insulation affects heat transfer in a material, such that modifications to insulation will have a heavily weighted influence on outputs from those functions. Such an internal representation may allow for an accurate prediction or identification of the compositional material properties of a mix which may have been covered with a blanket and would allow for an automated physico-chemical explanation of those results. The inclusion of physical or chemical understanding may also allow for a Page 86 of 171 13893549v1model to be more robust against missing or partially informative data. For example, a CNN which may also have an internal functional representation of the mechanics of acoustic wave propagation within concrete, may be able to take as input an incomplete or noisy set of acoustic wave measurements from a mechanical wave-based sensor within a particular mix, and output a sufficiently accurate prediction, evaluation or recommendation. In contrast, a traditional CNN model may have difficulty learning useful embeddings that are representative of the system with incomplete data.

[0286] In some embodiments, the models may include vector machine physical / chemical models, tree-based methods, decision trees, bagging trees, random forests, gradient boosted trees, Bayesian probability theory, artificial neural networks, convolutional networks, recurrent neural networks, transformers, generative adversarial networks, diffusion system, clustering algorithms, k-means clustering, hierarchical clustering, density based spatial clustering, spectral clustering, affinity propagation, gaussian mixture models, embedding / dimensionality reduction methods, principal component analysis (PCA), independent component analysis (ICA), multi-dimensional compression methods, Bayesian networks, causal graphs, ensemble models, and / or the like.

[0287] Further, in some embodiments, more time efficient and / or lower complexity models to work in real-time (e.g., during live control system optimization of cementitious mix batching based on data from sensors sensing raw materials). Additionally, or alternatively, larger, more complex models may be used for less time sensitive tasks that may provide more accurate outputs (e.g. longer term optimization of building material parameters based on for example data from the pour). Further, outputs of the larger models may be used to validate outputs of smaller models thereby allowing smaller models to learn and / or be trained on larger model’s outputs.

[0288] In some embodiments, models may be able to identify errors in data and / or mismatches in data and / or uncertainty in data and clean the data and / or resolve the issues (e.g., composition that doesn’t match a particular property). Additionally, or alternatively, data cleaning may be performed in the training dataset. In some embodiments, training data may be stored in a graph database.

[0289] In some embodiments, the models may include orchestrator models and / or multi-version distributed models. Given the multitude of models described herein, the methods may include an orchestration system which may be able to orchestrate across the various models herein. This may orchestrate the use of different models based on different information. In a first instance, this system may have a notion of the use case for which the Page 87 of 171 13893549v1method herein is used and may be able to route the input data to the correct model. In general, it may even be able to orchestrate a sequence and / or parallel combination of use of each model. This system may allow the method to act as a distributed system of models that may fulfil different purposes and may be used multiple times in parallel or in sequence.

[0290] Further, in some embodiments, for each one of the models described herein it may be possible to have different versions of the model, which may be trained in different ways to create intentional biases and / or specialize the models towards outperforming in a particular field. For example, one version of a model may be trained on a large portion of incomplete data (which may allow the model to become robust towards incomplete data) whereas another may be trained on predominantly complete data, which may be more accurate than the models trained on partial data and may be used whenever additional data is available. Another example may be a model trained predominantly on mixes formed of SCMs (supplementary cementing materials), and may therefore be particularly effective at predicting, evaluating, and / or generating SCM-based material designs. This latter example may be generalized as training models to effectively work in different volumes or parts of material space (e.g., model for C80 and above, model for materials below C50, and / or the like). In some embodiments, this may be described as an adaptive model. One instantiation of the distributed model system may be a distributed system of GANs (generative adversarial network), where a system of distributed GANs, configured with different biases, may act as an ensemble which may be intentionally skewed towards certain types of mixes over others.

[0291] The models herein may represent production pathways, and associated materials, equipment and control parameters in a number of ways. In some embodiments, a production pathway may be represented as a set of production processes that may occur sequentially, in parallel or as both. For example, the production pathways may include production processes through one or more stages and / or one or more substages. In some embodiments, the representation of one or a plurality of production pathways that may either be ingested, output, or otherwise interacted with by the models herein may include any one of or combination of: a set of material identifiers M = {m1, m2, …, mN}; the supply availability of each material, which may be in some embodiments represented as a function, additionally or alternatively, a function of the relevant material and of time (e.g., SA(t, mi)); a set of processes P = {P1, P2, …, PM}, which may represent the state change, transformation, or mapping between raw materials and final materials (and optionally intermediate materials); a set of process parameters, process contextual conditions, or control parameters, C = {c1, c2, …, cl}, which may define the contextual conditions of the process; Page 88 of 171 13893549v1a set of equipment E = {E1, E2, …, EN}, which may either execute or may be used to execute a production process in whole or in part. Additionally, or alternatively, a production pathway may include, one of or a plurality of raw materials being ingested, optionally based on supply chain availability, into a set of processes, the set of processes transforming raw materials into one of or a plurality of final products, where every individual process in the production pathway may include associated equipment to execute the process, and a set of process contextual conditions which may act as control variables during the process. Additionally, or alternatively, a production pathway may have attributes associated with it (e.g., associated carbon or greenhouse gas emissions impact, monetary cost, energy consumption, time efficiency of pathway (including total estimated process cycle time), material yield and / or waste, environmental impact (including water usage, air pollution, waste generation and more), product quality (including through measures of defect rate or failure rates), process difficulty (e.g., a measure of how difficult a process may be to achieve, in the case where a model may generate a novel pathway), and / or the like). Production pathway attributes may, in some embodiments, be features predicted by the models described herein, or may be included in the representation or description of the pathway.

[0292] For example, the control system 200 may receive one or more material-related inputs 2102. The material related inputs 2102 may include raw material characteristics and / or parameters and output material characteristics and / or parameters. For example, the raw material characteristics and / or parameters may include data from sensors 102, records, the material source, the material supplier, temperature, humidity, volume, or the like. Further, the output material characteristics and / or parameters may include sensor data, record data, pour geometry, ambient weather conditions, and / or the like. In some embodiments, data generated from the output material may include test data and / or other records data from the output material. For example, the data may include data from the following tests: slump test; compaction factor test; flow table test; air content test (pressure method); air content test (volumetric method); temperature test; density (unit weight) test; bleeding test; setting time test (vicat needle test); compressive strength test; flexural strength test; split tensile strength test; shrinkage test; creep test; permeability test; chloride ion penetration test; rapid chloride permeability test (rcpt); carbonation test; freeze-thaw resistance test; sulfate attack test; alkali-silica reaction (asr) test; modulus of elasticity test; water absorption test; abrasion resistance test; fire resistance test; ultrasonic pulse velocity test; rebound hammer test; pull- off test; pull-out test; impact resistance test; dynamic modulus of elasticity test; resistivity test; sorptivity test; thermal conductivity test; drying shrinkage test; wetting and drying test. Page 89 of 171 13893549v1Further, the data may also include any data associated with the following properties of the output material: workability; consistency and flowability; air content; temperature of fresh concrete; density (unit weight); bleeding rate and water separation; initial and final setting time; compressive strength; flexural strength; tensile strength; shrinkage (volume reduction); long-term deformation under load; permeability to water or gas; chloride ingress resistance; chloride ion permeability; carbonation resistance; freeze-thaw durability; sulfate resistance; alkali-silica reactivity; elastic modulus; water absorption capacity; abrasion resistance; resistance to high temperatures; homogeneity and quality; surface hardness and compressive strength estimate; bond strength; anchorage or pull-out strength; impact strength; dynamic modulus of elasticity; electrical resistivity (corrosion resistance); capillary absorption of water; thermal conductivity; drying shrinkage; resistance to cyclic wetting and drying.

[0293] Further, the control system 200 may receive initial target control values 2104, which may include initial mix compositions and / or recipes, initial control system 200 parameters, initial mix sequencing and / or timing, or the like. Further, in some embodiments, the control system 200 may receive constraints and objectives 2106, which may include input constraints, performance targets and output constraints, optimization objectives, or the like. In some embodiments, the input constraints may include a control variable range and / or a material availability, for example. Further, the performance targets and output constraints may relate to the specification of the building material (e.g., final building material). Further, the optimization objectives may include one or more goals to minimize energy consumption, minimize waste, minimize variation from recipe, minimize carbon, or the like.

[0294] As used herein, a constraint may include any limitation on any data objects described herein, quantitative thresholds, and / or qualitative attributes. A non-exhaustive list of quantitative constraints may include tensile strength ≥ 500 MPa, weight ≤ 5 kg, elastic modulus: 200 GPa ≤ E ≤ 250 GPa, fatigue life ≥ 10⁷ cycles, maximum deformation ≤ 1 mm, shear strength ≥ 50 MPa, operating temperature: -100°C ≤ T ≤ 400°C, thermal expansion coefficient ≤ 10 x 10⁻⁶ / °C, corrosion rate ≤ 0.1 mm / year, impact resistance ≥ 10 J, processing temperature ≤ 1000°C, part thickness ≥ 0.5 mm, machining tolerance ±0.01 mm, production cycle time ≤ 30 min / unit, electrical resistivity ≤ 10⁻⁶ ohm·m, dielectric strength ≥ 100 MV / m, thermal conductivity ≥ 50 W / m·K, embodied carbon ≤ 10 kg CO₂-eq / kg, recyclability ≥ 70%, water usage ≤ 100 L / kg, material cost ≤ $50 / kg; manufacturing cost ≤ $200 / unit, surface roughness ≤ 0.8 µm, packaging weight ≤ 25 kg, shock load resistance ≥ 10g, and / or the like. A non-exhaustive list of qualitative constraints may include material must be chemically resistant to acids, material must be non-toxic and comply with environmental regulations, Page 90 of 171 13893549v1material must have high surface finish quality, material must be visually defect-free, material must be compatible with existing manufacturing processes, and / or the like.

[0295] In some embodiments, the control system 200 may generate outputs and / or execute parameters, as shown in block 2108. In this way, the control system 200 may perform process adjustments on the batch composition and / or raw material quantity, adjust the mixing sequencing and timing, adjust the control system parameters and / or contextual conditions, or the like.

[0296] In some embodiments, the models described herein may include one or more output features including output confidence intervals alongside material properties. Additionally, or alternatively, the models described herein may be configured for ‘Live’ prediction adjustment and refinement and / or tuning, where the model may be predicted based on data gathered by sensors.

[0297] In some embodiments, any of the models herein may be able to predict the uncertainty in a given material’s properties in reality, as well as variability in batches, production quality, purity, and / or the like. Additionally, or alternatively, training data from sensors may provide accurate characterizations of this variability such that the models may use empirical methods to generate expected uncertainty or variability based on real world data. In some embodiments, training dataset for machine learning models may be based on physical simulations (e.g., finite element models). Further, in some embodiments, the models may be capable of performing arbitrary local adjustments (e.g., executing parameter adjustments 2108) to a given input space, x1, x2,…,xn,, in order to achieve some desired output(s) provided by the function y=fx1, x2,…,xn,, where the function, f, denotes a model of arbitrary non-linearity and discreteness, operating on inputs of arbitrary continuity or discreteness.

[0298] In some embodiments, the output of the models described herein may be multi- dimensional tensors of point values, where those point values may define the evaluation, adjustment or recommendation of a single variable. However, in some embodiments, all models may be placed within an additional probabilistic modelling procedure, which characterizes the probability density function over all possible outcomes for a point value, where that probability density may be taken over some subset of the totality of the data on which the model was trained, or some ensemble of subsets of the totality of data, or some other representative dataset.

[0299] In general, the use of probabilistic models over a set of point values may allow for conversion of any point value into one or more range values associated with that model Page 91 of 171 13893549v1output. For example, the point value for a predicted temperature may be 24.3C, but the probabilistic uncertainty on that output may provide the range of values (23.8C, 25.2C), where the first and last temperature in the range denotes the lower and upper bound on the uncertainty to within a 99.9% confidence level, respectively.

[0300] Further, in some embodiments, and as shown in Figure 22, a general optimization model is illustrated. In some embodiments, the optimization model may be used to optimize the process for building material production, or the like. In this way, the optimization model may optimize the process by which the building material is made. For example, the control system 200 may receive the same or similar inputs as shown in Figure 21 (e.g., the material-related inputs 2102, the initial target control values 2104, and the constraints and objectives 2106), while outputting and / or executing the parameter adjustment 2108. In some embodiments, the control process optimization 2202 may include the control system defining objective functions and constraints, as shown in block 2204, and / or using an optimization loop to search for optimized values, as shown in block 2206. For example, the control system 200 may define objective functions and constraints based on the constraints and objectives 2106, which may include optimizing processes / parameters / characteristics directly related to the batching process (e.g., making the batching process less energy intensive by lowering the temperature). Further, running the optimization loop (e.g., block 2206) may include searching for optimized values based on the outputs and / or parameter adjustment executions, as shown in block 2108.

[0301] In some embodiments, the models as described herein may be optimized based on one or more objective functions and / or utility functions. A non-exhaustive list of objective functions considered by the present disclosure may include objective functions directed to defining a desired output from the models herein, objective functions directed to being used internally during training of the models, and / or the like. Example objection functions may include loss function, cross-entropy loss, mean squared error (MSE), hinge loss, log- likelihood, reconstruction loss, reconstruction loss, Q-learning objective, and / or the like. Objective functions of the present disclosure may be directed to and / or may include L1 regularization, L2 regularization, parameters (e.g., model attributes that change during optimization or learning), hyperparameters, weights, biases, regularization parameters, constraint parameters, multi-objective functions (e.g., weights (that determine importance of each objective terms), pareto-based, evolutionary algorithm methods, and / or the like), and / or the like. Page 92 of 171 13893549v1

[0302] Further, in some embodiments, utility functions may be similar to objective functions but may be used in cases where a user’s preferences may be defined over multiple possible outcomes into one function. A non-exhaustive list of utility functions considered by the present disclosure may include cumulative reward function, expected utility, payoff function, linear utility, multiplicative utility, exponential utility, logarithmic utility, weighted sum utility, risk-aware utility (to penalize risk or uncertainty in material behavior), piecewise utility, pareto utility, and / or the like.

[0303] Further, in some embodiments, Potential objectives considered by the embodiments of the present disclosure may include minimize cost, maximize performance, minimize embodied carbon, maximize or minimize any property (optionally up to a threshold), maximize strength-to-weight ratio, minimize material cost, maximize durability, maximize recyclability, minimize environmental impact, maximize fatigue resistance, maximize thermal conductivity, maximize corrosion resistance, minimize weight, maximize impact resistance, minimize manufacturing complexity, minimize production time, maximize availability of raw materials, maximize energy efficiency in production, maximize ease of processing, minimize maintenance requirements, maximize lifecycle sustainability, minimize embodied carbon in material, minimize carbon footprint, minimize waste generation during production, maximize use of renewable materials, minimize water usage during production, maximize potential for reuse or repurposing, maximize resistance to wear and tear, maximize UV resistance, maximize fire resistance, maximize electrical conductivity, minimize electrical resistivity, maximize dielectric strength, maximize optical clarity, maximize light absorption for energy applications, maximize electromagnetic shielding effectiveness, minimize electromagnetic interference, maximize chemical resistance, minimize material degradation due to chemical exposure, maximize ease of machining and forming, maximize compatibility with additive manufacturing, maximize resistance to weathering, maximize surface finish quality, maximize thermal insulation, maximize heat dissipation, maximize resistance to damage during shipping, and / or the like. Any combination of the aforementioned objectives may be represented in a multi-objective function.

[0304] Further, in some embodiments, and as shown in Figure 23, the control process optimization 2202 may include one or more additional steps. For example, the control system 200 may sense material data 2302, detect a deviation 2304, and / or adjust the deviation 2306. In some embodiments, the control process optimization 2202 may determinate a deviation using a reference 2308. For example, the deviation may use a baseline as the reference, which Page 93 of 171 13893549v1may be any predetermined value or range of values of any characteristic or parameter of a material. In some embodiments, the reference may evolve over time. For example, the reference (e.g., baseline) may be determined on a target recipe and / or a mix composition, it may be an average or expected parameter value, it may be determined based on historical data, it may be changed based on seasons and / or weather, it may be calculated using a model, it may be static or time varying, it may be determined based on learned correlations between performance targets for output material and measured material data, it may be set as a predetermine rule inside the control system 200 logic, or the like. In some embodiments, based on correlations between control parameters and output material performance the model may learn how control parameters impact output performance (e.g., be a predictive model). In some embodiments, the adjustment may be directly adjusted so it matches the baseline and / or reference. In some embodiments, the adjust may be determined by a predetermined rule (e.g., if ‘x’ threshold is reached, adjust by ‘y’).

[0305] In some embodiments, and as shown in Figure 24, the adjustments to the parameters may be based on data associated with output materials (e.g., final product building materials). In this way, the model may ingest output material data 2402 from one or more sensors 102. For example, the control system 200 may sense the output material 2404 which may include the building material after it has left a stage, a substage, or has taken a final form (e.g., in the project site stage in a pour). Further, in some embodiments, the control system 200 may detect a deviation from a target performance 2406. Further, in some embodiments, the control system 200 may adjust the control process to compensate for the output deviation 2408. In this way, the model (e.g., AI model) may determine which control parameters can be used to compensate for the deviation. In some embodiments, the models may be continually updated, where updating is done as a continuous process of training as new data arrives from any set of data sources (e.g. sensor data from across the value chain). For example, an optimization loop may be used to adjust the parameters until a satisfactory solution is reached. In some embodiments, the parameter may be adjusted, as shown in block 2410. In some embodiments, a prediction model 2412 may be used to predict material performance with the adjusted parameter 2414. In some embodiments, these may be compared against target performance, input constraints and objectives 2416. In some embodiments, the optimization loop may be continuous until the adjust parameters result in output material performance that satisfies target performance attributes or parameters. Additionally, or alternatively, the model may adjust parameters based on learned correlations between parameters and output performance (e.g., infer appropriate adjustments using Page 94 of 171 13893549v1correlation analysis). Additionally, or alternatively, the model may run predictive scenario analyses where the model predicts output performance based on adjustments to the parameters and chooses the closes, most appropriate scenario.

[0306] In specific embodiments, optimizing the construction process via the control system 200 may take a variety of forms. For example, optimization make take place on a batch-to-batch basis, wherein the dosages and amounts of materials are tuned and determined based on what material is next to be added. Further, in another example, the control system 200 may use the condition of the input materials to optimize the recipe of the current batch towards a target output. Further, in another example, the control system 200 may use the characteristics of the output material to optimize the recipe towards a target output for future batches. Further, in another example, the control system 200 may use a combination of the input and output to optimize the recipe. Further, in another example, the control system 200 may use averages, ensembles, distributions (e.g., spatial, temporal, or otherwise) across one or more containers to make decisions. Further, in another example, the control system 200 may make changes to conditions of stored raw materials (e.g., humidity or temperature). Further, in another example, the control system 200 may trace raw materials all the way to output and isolate characteristics of the material (e.g., all the way from containers to the pour stage). In this way, the traced data of the material may be used to train the models of the system.

[0307] Further, in some embodiments, one or more AI models (e.g., AI model(s) 208) may communicate with one another in real-time to make predictive decisions, such as forecasting models or risk identification in the construction process. In some embodiments, the real-time analysis may include analyzing data up to a first time, which may include data ingested and process up until an initial point, which may also serve as baseline data. Further, in some embodiments, data may be collected up until a second time, which may be a point in time after the first time. In this way, the data associated with the first time and the data associated with the second time may be compared, analyzed, processed, and the like. It is to be understood that the real-time analysis may also include numerous points in time and should not be limited to the first time and the second time. Said differently, the operations described herein may be performed iteratively such that the first and / or second time may refer to any time at which the operations of the systems described herein occur.

[0308] In some embodiments, the AI model may be included in the control system 200. Further, in some embodiments, the AI model may use data from sensors 102 across the value chain (e.g., stages and / or substages) to optimize mix recipes at the batch level. Further, Page 95 of 171 13893549v1in some embodiments, the control system 200 may control what goes into the batch (e.g., controlling different operations at the batching plant). These actions may be influenced by the AI model. In some embodiments, for example, discharging material may be performed by the AI model via weight or volume of the materials. In some embodiments, the AI model may communicate with the control system 200 and be a separate entity. In some embodiments, the AI model may predict expected behavior of the final product based on sensor data of the raw materials.

[0309] Further, in some embodiments, the AI model may include prediction models, recommendation models, optimization models, generation models, or the like. Additionally, or alternatively, the models may be trained on any data from any of the sensors 102 as described herein. Further, in some embodiments, and as shown in Figure 25, adjustments to the process may be based on predicted data about outputs from inputs. In some embodiments, the model may ingest input and / or output material data 2502 from one or more sensors 102. For example, the control system 200 may sense the raw materials 2504 using the one or more sensors 102. In some embodiments, the control system 200 may include a predictive model 2506. In some embodiments, the predictive model may determine an output material performance 2508 and / or compare it to target performance 2510. Further, in some embodiments, the predictive model 2506 may perform an optimization loop, as described above, wherein the parameters are adjusted until a satisfactory solution is reached.

[0310] Further, in some embodiments, and as shown in Figure 26, the control system 200 may determine long term trends in data over time 2604. In this way, the control system 200 may receive dynamical input data 2602 from one or more sensors 102 over a period of time. In some embodiments, a correlation between output performance deviation and one or more parameters may be determined (e.g., block 2606). In this way, for example, the parameters that may be determined to affect the correlation may include weather and seasons, equipment performance and / or degradation, average raw material parameters, average output material parameters, raw material supplier variability, project specific trends (e.g., material tends to underperform at a specific jobsite), average raw material variability, average output material variability, or the like. In some embodiments, the control system 200 may determine an expected deviation in output material performance cause by a parameter 2608. Further, in some embodiments, the control system 200 may determine parameter adjustments to compensate for the deviation 2610.

[0311] Further, in some embodiments and as shown in Figure 27, the control system 200 may receive the material related inputs 2102 and the constraints and objectives 2106 and Page 96 of 171 13893549v1output and / or execute parameter adjustments 2108. In some embodiments, the control system 200 may determine one or more constraints and objectives to be more important (e.g., a list of equipment) in order to generate a batching process.

[0312] Further, in some embodiments, the predictive models herein may include predictive models that can predict output material performance and / or parameters and / or characteristics based on a number of predictors. In some embodiments, the models may be used in the control system 200 to predict output material attributes / performance based on input data. Additionally, or alternatively, the input data may be one or any combination of the one or more mix compositions or recipes; one or more raw material data (e.g., raw material property, attribute or any data associated with raw material); one or more control system parameters and / or raw material contextual conditions; one or more mixing and / or batching sequences and / or timings; one or more output or intermediate material contextual conditions.

[0313] In another embodiment, the control system 200 production operations and / or pathway may be represented as a graph network. In a further embodiment nodes may represent materials or material states, and edges may represent production processes. Additionally, or alternatively, a pathway may be represented by a path along nodes and edges. Additionally, or alternatively, nodes may encode material composition data and / or material property data as node attributes. Additionally, or alternatively, edges may encode control parameter data and / or equipment data as edge attributes. Additionally, or alternatively, equipment data and control parameter data may be represented as additional nodes on the graph. Additionally, or alternatively, equipment or control parameter nodes may be connected to the process that is applicable to them. Additionally, or alternatively, intermediary nodes may be used to connect equipment or control parameter nodes to process edges. Additionally, or alternatively, the starting node or plurality of nodes on a graph network may represent the raw material(s), the end node or plurality of nodes may represent the final product, and any intermediate nodes may represent intermediate materials.

[0314] In an alternative further embodiment, nodes may represent materials (raw, intermediate or final), processes, equipment or parameters, and edges may represent the transitions or relationships between these nodes. Additionally, or alternatively, this may be represented as a directed acyclic graph. Additionally or alternatively, directed edges may represent relationships between nodes for example an edge from a material to a process may represent that the material is being input into the process; an edge from a process to a material may represent that the material is being output from the process; an edge from a process to Page 97 of 171 13893549v1an equipment may represent that the process requires the equipment; an edge from a control parameter to a process may represent that the control parameter is required by the process.

[0315] Additionally, or alternatively, the graph-based representation may be space and / or time dependent. Additionally, or alternatively process time dependence may include time dependent process contextual conditions. Additionally, or alternatively, time dependence may be represented through intermediate nodes through a process edge which breaks the graph into discrete time slices. Additionally, or alternatively, edges may be dynamic and time-evolving. Additionally, or alternatively, time dependence may be encoded through time dependent functions associated with edges and / or nodes, for example: the node itself may be a time dependent function, the edge may be a time dependent function, and / or node or edge attributes may be time dependent functions. Additionally, or alternatively, control parameters may be space and / or time evolving functions (e.g., temperature over time).

[0316] Additionally, or alternatively the graphs described herein may be any of the following examples Directed Acyclic Graph (DAG); Bipartite Graph; Multigraph; Hypergraph; Flow Network; Labeled Graph; Weighted Graph; Dependency Graph; Petri Net; Knowledge Graph; State Machine Graph; Process Network Graph.

[0317] In another embodiment, the control system may internally represent production processes and / or pathways as a set of functions and operators. Additionally, or alternatively, the function may be material state functions. Additionally, or alternatively, the functions may be represented as vectors in a vector space. Additionally, or alternatively, operators may be represented as matrices or tensors. Additionally, or alternatively, an input material undergoing a process may be represented as an input function being transformed by an operator. Additionally, or alternatively, an input material undergoing a process may be represented as a vector being transformed by a matrix. Additionally, or alternatively, control parameters may be represented as transformation matrices and / or operators, which may transform process matrices and / or operators, wherein the matrix multiplication of both may represent the full process transformation. Additionally, or alternatively, these matrices may be spatially or temporally dependent.

[0318] In some embodiments, the condition or properties of materials throughout a production process, across time and space may be represented and / or encoded through state variables and / or state variable functions s(x,t). These may be discrete functions of space and time or may be continuous. Additionally, or alternatively, state variables may be a function of the discrete production processes along a production pathway. Additionally, or alternatively, state variable functions may be vectors of a plurality of functions. Additionally Page 98 of 171 13893549v1or alternatively, each vector component state variable function may represent a specific material property or contextual conditions that may vary spatio-temporally across process steps si(x,t), for example: Temperature T(x,t), Pressure P(x,t), Stress (t,x); Strain (t,x). Additionally or alternatively, process control variables or control parameters may also be represented as functions of space and time u(x,t). Additionally or alternatively, this may also be represented by a vector of control parameter functions ui(x,t) for each parameter type, for example: applied temperature or heat flux uT(x,t), applied mechanical force or pressure uF(x,t), applied electric field or current uE(x,t), applied tool velocity uV(x,t) (e.g., of a CNC cutter, laser or extruder). Additionally or alternatively, equipment may be represented as transfer functions G, wherein an input material is inputted into a piece of equipment, which changes the state of the input material to an output product or intermediate material, based on control parameters as well soutput(x,t) = G[sinput(x,t), u(x,t)]. Additionally, or alternatively, in some embodiments, state variable evolution may be modeled using AI methods. Additionally, or alternatively, in some embodiments, state variable evolution may be modeled using physico-chemical equations, such as dynamics equations, empirical laws, differential equations including partial differential equations and ordinary differential equations and / or others.

[0319] In some further embodiments, materials in a production pathway may include raw materials, intermediate materials and output materials. Additionally, or alternatively, materials may be represented in the production methods, models, and systems herein as material identifiers. Additionally, or alternatively, the material identifier may include any associated characteristic of the material such as composition data, properties and any other such entities described elsewhere herein. Additionally, or alternatively, a material identifier may be indicative of the state of the material in the production pathway. Additionally, or alternatively, the state of the material may be represented by a time or space dependent state variable. Additionally or alternatively, the state of the material may be represented as a spatial or temporal state function e.g. MS(x,t). Additionally or alternatively, the state of a material throughout the pathway may be represented by one, a plurality, or a set of material characteristics, which may be functions of space, time, both, or step in the production process e.g., properties, composition, contextual conditions, which may include for example: Material Type T(t,x); Atomic Composition A(t,x); Temperature T(t,x); Pressure P(t,x); Stress σ(t,x); Strain ^(t,x); Material density ρ(t,x); Concentration of a chemical species i Ci(t,x) and other such characteristics. In some embodiments. In some embodiments, state variables may be represented as vectors in material space. Additionally, or alternatively, these vectors may Page 99 of 171 13893549v1be time evolving and / or spatially dependent. Additionally, or alternatively, the state of a material may be represented by a state machine, wherein each state in the state machine is representative of the material’s evolution through the pathway.

[0320] In some embodiment, the models herein may use path integral representations for modeling, evaluating, optimizing, and / or generating optimal production pathways. Additionally, or alternatively, the path integral may encode production pathway properties such as cost, energy, and time. Additionally, or alternatively, the path integral may encode this information through an action functional. Additionally, or alternatively, each possible path may have an associated weighting according to a probability distribution over all paths. Additionally, or alternatively, the path integral may sum over all possibilities. Additionally, or alternatively, the optimal pathways may be defined as those that minimize the action functional. Additionally, or alternatively, this representation may be used by manufacturing process optimization and / or generation models for searching the space of possible production pathways and determining optimal candidates based on desired targets. Additionally, or alternatively, pathways may be represented by graph networks. Additionally, or alternatively, path integrals may be used to evaluate pathways of nodes and / or edges on graph networks. Additionally, or alternatively, the action functional may be computed for each path on the graph. Additionally, or alternatively, optimal paths are node and edge paths which minimize the action functional.

[0321] In some embodiments, graph neural networks may model and predict production process outputs. In such embodiments, entities may be included such as nodes being material states, edges being production processes, and / or the like. Additionally, or alternatively, the model problem statement may include predict full information for the final node of the graph (e.g., final material state), based on known information about all previous nodes and edges. This may include predicting composition, and properties, optionally in certain contextual conditions.

[0322] In some embodiments, features of the graph neural networks may include predicting the final node of the graph on incomplete data (e.g., not all attributes of every node and edge must be present, may have some incompleteness, nodes and edges may be completely missing, but the model may generate an output), confidence interval, likelihood, or uncertainty in the output (e.g., uncertainty increases as less information regarding prior nodes and edges exist), and / or specialized models may be effective at dealing with missing nodes or edges or incomplete information. Page 100 of 171 13893549v1

[0323] In some embodiments, the control system models herein may be trained on data associated with any predictor herein and / or any other data types, and associated output material performance data. Additionally, or alternatively, the models may include statistical models. Additionally, or alternatively, the models may be machine learning networks such as neural networks. Additionally, or alternatively, the models, via training, may determine statistical correlations or data indicative of statistical correlations between the predictor parameters and the one or more output material performances / attributes / parameters. Additionally, or alternatively, the output material may be a hardened cementitious mix (e.g., concrete), a fresh cementitious mix (e.g., concrete) and / or a cementitious mix in between those states (e.g., an intermediary mix).

[0324] For example, the models (e.g., AI model 208) associated with the control system 200 may ingest any data and output a recommendation and / or parameter adjustment. In this way, the data that may be inputted into the AI model 208 for prediction may include data generated from the one or more sensors 102, data from the process of a stage, data from the process of a substage, data from equipment, data from a third party, data from one or more conditions, or the like. In this way, the data used by the AI model 208 may then be used to determine a prediction of how the material will perform under a set of circumstances. For example, the AI model 208 may predict how a material will perform on a jobsite wherein the jobsite experiences a higher humidity than where the material is produced (e.g., at the batching plant). In this way, the AI model 208 may receive data associated with the weather conditions from a third party and use that data to adjust the batching recipe for a material destined for the jobsite. Further, in some embodiments, the AI model 208 may use any and all data produced, generated, or received to make the predictions. Further, in some embodiments, the combinations of the data used may incorporate any and all data as described herein.

[0325] Further, in some embodiments, the control system 200 may adjust the mixing sequence. For example, sensors 102 in the mixer will gather and generate data regarding the difference ingredients interacting with each other as they are being added. For example, if aggregate is the first in the mixer and cement is being added, and then water, the sensors 102 may provide data about how the materials interact with each other in real-time, as well as the impact of the sequence of mixing on the behavior of the material. The models (e.g., the AI model) may be trained on this data to understand how the dynamic sequence in which raw materials are added and mixed together impacts the output material. In this way, the data may Page 101 of 171 13893549v1be used as considerations by the control system 200 to determine how to sequence and mix materials.

[0326] Further, in some embodiments, and as shown in block 1008 of Figure 10, a summary report may be generated based on the sensor data and the modifications of the building material. Further, in some embodiments and as shown in block 1010 of Figure 10, the summary report may be transmitted to a user device. For example, a summary report may be generated to include the material properties of the building material 101 or any portions of the building material 101. In this way, the report may be generated that includes information and data relating to the building material data entity. In this way, the report may be a communication or file (e.g., an email communication, a PDF report, or the like). The report may incorporate the one or more computed values, anomalies (if any), status alerts (e.g., pass, fail, warning, etc.) associated with the building material data entity. Further, the report may be generated for a single pour or a multiple pour in embodiments where concrete is the building material. In some embodiments, the report may include an operation and maintenance manual that may be generated automatically upon the system receiving data associated with the building material data entity. Further, the report may include a project handover report which may summarize all compliance and quality status associated with the project.

[0327] Further, in some embodiments, the system 100 may transform the data and / or building material data entity by cleaning and structuring the data. In some embodiments, the system 100 may translate the data to make the data understandable in regard to the schema of the system 100. For example, the material properties of the building material 101 or the portions of the building material 101 may be processed via a server, the control system 200, the processor 202, or the like, in order to transform and translate the material properties prior to further processing. In some embodiments, the data may be processed (e.g., pre-processed) by a processor associated with the one or more sensors 102.

[0328] In some embodiments, the building material 101 may take a variety of forms. In this way, the building material 101 may include raw materials and / or input materials. In some embodiments, a first portion of the building material 101 may be used. In this way, the first portion of the building material 101 may be a discrete component of the building material 101 that is used to create the building material 101. In some embodiments, the building material 101 may include at least one portion (e.g., the first portion) to make up the building material 101. In this way, the first portion, along with potentially one or more other portions, may be combined in a way as to create the building material 101. For example, the portions Page 102 of 171 13893549v1may include raw materials and / or input materials such as binder (e.g., cement, Portland cement, fly ash, GGBS, etc.), aggregates, water, admixtures, or the like.

[0329] In some embodiments, the building material 101 created by combining the one or more portions (e.g., the first portion) of the building material 101 may create output materials. The output materials may, in some embodiments, be output from a particular stage, and may change throughout the process. In some embodiments, the output materials may include building materials 101 such as cementitious mixtures, concrete, or the like.

[0330] In some embodiments, the sensors 102 may measure properties of the building material 101 and the portions of the building material 101 as it moves through the process. The sensors 102 may transmit information regarding the status or properties of the building material based on which stage the building material 101 is in. In some embodiments, the data captured by the sensors 102 may take a variety of forms. For example, the properties of the building material 101 ingested and / or generated by the sensors 102 may be static material properties, contextual material properties, or the like. In another example, the sensors 102 may determine compositional material properties such as the composition or the structure (e.g., microstructure) of the building material 101. In another example, the sensors 102 may determine contextual conditions such as pressure in the container 303, temperature in the container 303, humidity in the container 303, or the like. The information gathered by the sensors 102 may be transmitted and used by the control system 200 to make determinations about a performance of the building material 101.

[0331] Further, in some embodiments, the control system 200 may alter mix recipes and / or determine how much of a material should be in each batch. In this way, the control system 200 may execute the altered mix recipe in the batching plant, for example. In some embodiments, the control system 200 may determine and / or predict data indicative of cement reactivity based on sensor data relating to cement powder. Further, in some embodiments, determining and / or altering the batching quantities may be based on sensor data and / or a model determination of reactivity. In this way, sensor data may include spatial mapping data associated with the cement powder. Further, the spatial mapping data may be used to determine data associated with the compositional properties of the cement in the mapped space. ...

Claims

CLAIMS:

1. A method for dynamic building material modification, the method comprising: receiving sensor data from a sensor device, wherein: the sensor device is associated with a first stage of a plurality of stages that form a process for a construction project, and the sensor data is associated with a first portion of a building material associated with the first stage; determining one or more characteristics associated with the first portion of the building material; and modifying, via a control system, one or more parameters of the building material from which the first portion of the building material is derived based on the one or more determined characteristics.

2. The method of Claim 1, wherein the sensor device is an input sensor configured to generate data associated with the first portion of the building material as it enters the first stage.

3. The method of Claim 2, wherein the control system is configured to: receive the input data from the input sensor; determine a target building material characteristic based on the input data; and modify the building material based on a difference between the target building material characteristic and the one or more characteristics.

4. The method of Claim 1, wherein the sensor device is an input sensor configured to generate data associated with the first portion of the building material as it exits the first stage or before it exits the first stage.

5. The method of Claim 1, wherein the control system is configured to: receive output data from an output sensor; determine a target building material characteristic based on the output data; and modify the building material based on a difference between the target building material characteristic and the one or more characteristics. Page 167 of 171 13893549v16. The method of Claim 1, further comprising modifying, via the control system, a second portion of the building material associated with a second stage based on the one or more characteristics of the first portion at the first stage based on data generated by the sensor device associated with the building material at the first stage.

7. The method of Claim 1, further comprising, via the control system, modifying a material flow rate for the building material, based on the one or more characteristics.

8. The method of Claim 1, wherein modifying the one or more parameters of the building material comprises modifying at least one of a: volume of the first portion of the building material; moisture level of the first portion of the building material; or temperature of the first portion of the building material.

9. The method of Claim 1, further comprising: generating a summary report based on the sensor data and the modifications of the building material; and transmitting the summary report to a user device.

10. The method of Claim 4, wherein modifying the one or more parameters of the building material comprises modifying a quantity of the first portion of the building material.

11. The method of Claim 4, wherein modifying the one or more parameters of the building material comprises: modifying a quantity of the first portion of the building material; and / or modifying a quantity of a second portion of the building material.

12. A sensor for dynamic building material modification, the sensor comprising: a support member configured to support the sensor, a sensing device configured to generate data associated with a first portion of a building material associated with a first stage of a plurality of stages that form a process of a construction project, and Page 168 of 171 13893549v1a communication interface configured to communicably couple the sensor with a control system, wherein the sensor is configured to transmit the data generated by the sensing device to the control system for modifying, via the control system, one or more parameters of the building material from which the first portion of the building material is derived based on one or more characteristics.

13. The sensor of Claim 12, wherein the sensor is configured to be attached to or disposed proximate a container associated with the first stage, wherein the support member is configured to at least partially conform to the container’s shape.

14. The sensor of Claim 13, wherein the container comprises a silo configured to store the building material.

15. The sensor of Claim 13, wherein the sensor is configured to be radially disposed about an interior surface of the container.

16. The sensor of Claim 15, wherein the sensor comprises a plurality of sensors vertically adjacent one another.

17. The sensor of Claim 15, wherein the sensor is configured to be vertically disposed inside the container.

18. The sensor of Claim 17, wherein the sensor comprises a plurality of sensors horizontally adjacent one another.

19. The sensor of Claim 12, wherein the sensor is configured to be attached to or disposed proximate a conveyor configured to transport the building material.

20. The sensor of Claim 12, wherein the sensing device is configured to generate data indicative of at least one property of the building material including at least one of: a temperature; a humidity; or a volume. Page 169 of 171 13893549v121. A system for dynamic building material modification, the system comprising: a sensor configured to generate data associated with a first portion of a building material associated with a first stage of a plurality of stages that form a process for a construction project; a control system configured to communicably couple to the sensor via a communication interface; a processor; a non-transitory storage device containing instructions that, when executed, cause the processor to: receive sensor data from the sensor; determine one or more characteristics associated with the first portion of the building material; and modify, via the control system, one or more parameters of the building material from which the first portion of the building material is derived based on the one or more determined characteristics.

22. The system of Claim 21, wherein executing the instructions further causes the processor to modify, via the control system, a second portion of the building material associated with a second stage based on the one or more characteristics of the first portion at the first stage based on data generated by the sensor device associated with the building material at the first stage. Page 170 of 171 13893549v1

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