Systems and methods for vehicle-based material determinations

Abstract

Systems, computer program products, and methods are described herein for vehicle based material determinations. The present disclosure provides a solution configured to receive sensor data from a sensor associated with a vehicle. The solution is configured to generate a predicted material condition of a material at least partially contained by the vehicle. The solution is configured to output the predicted material condition.

Description

SYSTEMS AND METHODS FOR VEHICLE-BASED MATERIAL DETERMINATIONSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present international application claims priority to U.S. Provisional Patent Application No. 63 / 744,713, filed January' 13, 2025, the entire content of which application is incorporated by reference in its entirety.TECHNOLOGICAL FIELD

[0002] Example embodiments of the present disclosure relate to systems and methods for vehicle-based material determinations.BACKGROUND

[0003] There are significant challenges associated with determining a material property and / or an outcome of a material test. Applicant has identified a number of deficiencies and problems associated with conventional systems for material property determinations. Through applied effort, ingenuity, and innovation, many of these identified problems 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] The following presents a simplified summary of one or more embodiments of the present disclosure, in order to provide a basic understanding of such embodiments. This summary is not an extensive overview of all contemplated embodiments and is intended to neither identify key or critical elements of all embodiments nor delineate the scope of any or all embodiments. Its sole purpose is to present some concepts of one or more embodiments of the present disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0005] Systems, methods, and computer program products are provided for vehiclebased material determinations.

[0006] Embodiments of the present disclosure address the above needs and / or achieve other advantages by providing apparatuses (e.g., a system, computer program product, and / or other devices) and methods for vehicle-based material determinations. The system embodiments may comprise a processing device and a non-transitory storage device containing instructions when executed by the processing device, to perform the steps disclosed herein. In Page 1 of 27214528758V1computer program product embodiments of the present disclosure, the computer program product comprises a non-transitory computer-readable medium comprising code causing an apparatus to perform the steps disclosed herein. Computer implemented method embodiments of the present disclosure may comprise providing a computing system comprising a computer processing device and a non-transitory7computer readable medium, where the computer readable medium comprises configured computer program instruction code, such that when said instruction code is operated by said computer processing device, said computer processing device performs certain operations to carry out the steps disclosed herein.

[0007] In some embodiments, the present disclosure provides a method including receiving sensor data from a sensor associated with a vehicle. In some embodiments, the method includes generating a predicted material condition of a material at least partially contained by the vehicle. In some embodiments, the method includes outputting the predicted material condition.

[0008] In some embodiments, in generating the predicted material condition, the method further includes determining one or more properties of the material.

[0009] In some embodiments, the one or more properties of the material includes a viscosity.

[0010] In some embodiments, the predicted material condition includes a predicted outcome of a slump test for the material.

[0011] In some embodiments, the sensor is operatively coupled to an internal portion of the vehicle that at least partially contains the material.

[0012] In some embodiments, the internal portion defines one or more extensions to which the sensor is operatively coupled.

[0013] In some embodiments, the sensor is configured to at least partially contact the material during operation.

[0014] In some embodiments, the sensor is operatively coupled to an external portion of the vehicle opposite an internal portion of the vehicle that at least partially contains the material.

[0015] In some embodiments, the sensor includes an imaging device configured to generate image data associated with the material, wherein the method further includes determining the one or more properties of the material via analyzing a captured image of the material.

[0016] In some embodiments, the method further includes determining an accumulation of the material on a housing of the sensor. In some embodiments, the method Page 2 of 27214528758vlfurther includes determining an interference on the sensor data based on the accumulation. In some embodiments, the method further includes modifying the predicted material condition and / or the one or more properties of the material to compensate for the interference.

[0017] In another aspect, a sensor device for material property determinations is provided. In some embodiments, the sensor device includes a housing configured to be operatively coupled with a vehicle. In some embodiments, the sensor device includes a sensor associated with the housing and configured to generate data associated with a material at least partially contained by the vehicle. In some embodiments, the sensor device includes a processor and a non-transitory storage device containing instructions that, when executed by the process, causes the processor to receive sensor data from the sensor associated with the vehicle. In some embodiments, the sensor device may generate a predicted material condition of the material. In some embodiments, the sensor device may output the predicted material condition.

[0018] In some embodiments, in generating the predicted material condition, executing the instructions further causes the processing device to determine one or more properties of the material.

[0019] In some embodiments, the one or more properties of the material includes a viscosity.

[0020] In some embodiments, the predicted material condition includes a predicted outcome of a slump test for the material.

[0021] In some embodiments, the sensor is operatively coupled to an internal portion of the vehicle, wherein the internal portion defines a cavity configured to at least partially contain the material.

[0022] In some embodiments, the internal portion includes one or more extensions to which the sensor is operatively coupled.

[0023] In some embodiments, the housing is configured to interact with the material.

[0024] In some embodiments, the sensor data is indicative of a shear force experienced by the housing when interacting with the material.

[0025] In some embodiments, the sensor is a piezoelectric-based sensor.

[0026] In some embodiments, the sensor includes an imaging device configured to generate image data associated with the material, wherein executing the instructions further causes the processor to determine the one or more properties of the material via analyzing a captured image of the material.

[0027] In some embodiments, executing the instructions further causes the processor to determine an accumulation of the material on the housing of the sensor. In some Page 3 of 27214528758vlembodiments, executing the instructions further causes the processor to determine an interference on the sensor data based on the accumulation. In some embodiments, executing the instructions further causes the processor to modify the predicted material condition and / or the one or more properties of the material to compensate for the interference.

[0028] In some embodiments, the sensor further includes an acoustic sensor operatively coupled to an external portion of the vehicle, wherein the acoustic sensor is configured to transmit an acoustic wave toward the material. In some embodiments, the acoustic sensor is configured to receive a response wave corresponding to a portion of the wave after interaction with the material. In some embodiments, the acoustic sensor is configured to determine the one or more properties of the material based on the received response wave.

[0029] In another aspect, a method is provided. In some embodiments, the method includes coupling a sensing assembly to an at least partially flowable material such that a response of the material to excitation influences the sensing assembly. In some embodiments, the method includes subjecting the material to excitation under a plurality of excitation conditions, the excitation conditions differing in at least one of excitation amplitude, excitation frequency, excitation waveform, excitation duration, or excitation timing. In some embodiments, the method includes measuring, using the sensing assembly, one or more response signals for each of the plurality of excitation conditions. In some embodiments, the method includes extracting, from the measured response signals, one or more response features that vary as a function of the excitation conditions. In some embodiments, the method includes determining, based on a change in at least one of the response features across the plurality of excitation conditions, one or more material conditions for the material.

[0030] In some embodiments, the excitation conditions and measurement of the response signals are coordinated based on one or more of (i) a measured rotational state of a container or vehicle carrying the material, (ii) a time reference provided by a clock, or (iii) a location reference derived from a positioning system.

[0031] In some embodiments, subjecting the material to excitation comprises applying a sequence of excitation amplitudes across successive excitation events, and wherein changes in one or more response features across the sequence are used to determine a material condition indicative of material yielding, slump, flow, or rheological property of the material.

[0032] 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 Page 4 of 27214528758vlany 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

[0033] Having thus described embodiments of the disclosure in general terms, reference will now be made 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.

[0034] Figure 1 illustrates an exemplary embodiment of a vehicle, in accordance with an embodiment of the disclosure;

[0035] Figure 2 illustrates an exemplary embodiment of an internal portion of the vehicle, in accordance w ith an embodiment of the disclosure;

[0036] Figure 3 illustrates an exemplary system 100 architecture wherein components of the system 100 are communicatively coupled via a network 104, in accordance with an embodiment of the disclosure;

[0037] Figure 4 illustrates an exemplary embodiment of a control system 200, in accordance w ith an embodiment of the disclosure;

[0038] Figure 5 illustrates a process flow for vehicle-based material determinations, in accordance with an embodiment of the disclosure:

[0039] Figure 6 illustrates an exemplary embodiment of a plurality of sensors 500 operatively coupled to a vehicle 400, in accordance with an embodiment of the disclosure;

[0040] Figure 7 illustrates exemplary' embodiments of configurations of the plurality' of sensors 500, in accordance with an embodiment of the disclosure;

[0041] Figure 8 illustrates an exemplary- embodiment of a piezoelectric sensor with a geometrically configured head to induce shear forces in a material, in accordance with an embodiment of the disclosure;

[0042] Figure 9 illustrates an exemplary embodiment of a printed circuit board configured to operate a sensor, in accordance with an embodiment of the disclosure;

[0043] Figure 10 illustrates a sensor including a piezoelectric sensor coupled to a printed circuit board enclosed in a housing, in accordance with an embodiment of the disclosure;

[0044] Figure 11 illustrates a sensor configured to ingest data relating to a material inside of a container via a load cell, in accordance with an embodiment of the disclosure;Page 5 of 27214528758vl

[0045] Figure 12 illustrates an embodiment of an installation of a sensor 502 on an internal extension of the container, in accordance with an embodiment of the disclosure;

[0046] Figure 13 illustrates an embodiment of an installation of a sensor 506 on an internal extension of the container, in accordance with an embodiment of the disclosure;

[0047] Figure 14 illustrates an embodiment of an installation of a sensor 510 on an internal extension of the container, in accordance with an embodiment of the disclosure;

[0048] Figure 15 illustrates an embodiment of an installation of a sensor 514 on an internal portion of the container, in accordance with an embodiment of the disclosure;

[0049] Figure 16 illustrates a sensor configured with acoustic signal sensing configurations, in accordance with an embodiment of the disclosure;

[0050] Figure 17 illustrates a plurality of sensors communicatively coupled with a data hub configured to communicate with a network, in accordance with an embodiment of the disclosure;

[0051] Figure 18 illustrates a plurality' of sensors operatively coupled to an external portion of the vehicle, in accordance with an embodiment of the disclosure;

[0052] Figure 19 illustrates a sensor coupled with a motor component of the vehicle, in accordance with an embodiment of the disclosure;

[0053] Figure 20 illustrates a plurality of sensors operatively coupled with an external portion of the vehicle configured to ingest data relating to the vehicle, in accordance with an embodiment of the disclosure;

[0054] Figure 21 illustrates a plurality of sensors operatively coupled with an external portion of the vehicle configured to ingest data relating to the vehicle’s surroundings, in accordance with an embodiment of the disclosure;

[0055] Figure 22 illustrates a sensor configured with a sensor head geometry configured to induce a shear force in the material, in accordance with an embodiment of the disclosure;

[0056] Figure 23 illustrates a sensor coupled with a printed circuit board that includes one or more electrodes, in accordance with an embodiment of the disclosure;

[0057] Figure 24 illustrates an image sensing device coupled to the container and configured to view the material in the container, in accordance with an embodiment of the disclosure;

[0058] Figure 25 illustrates an image sensing device configured to view material through a chute of the vehicle, in accordance with an embodiment of the disclosure;Page 6 of 27214528758vl

[0059] Figure 26 illustrates data from the image sensing device classifying the objects in its field of view, in accordance with an embodiment of the disclosure;

[0060] Figure 27 illustrates a sensor with a flap configured to remove accumulated material from the sensor, in accordance with an embodiment of the disclosure;

[0061] Figure 28 illustrates a sensor configured to analyze material within a gap created by the sensor, in accordance with an embodiment of the disclosure;

[0062] Figure 29 illustrates a sensor array in a ring formation configured to analyze material fl owing through the ring, in accordance with an embodiment of the disclosure;

[0063] Figure 30 illustrates a sensor configured to emit and receive light to analyze material, in accordance with an embodiment of the disclosure;

[0064] Figure 31 illustrates an amplitude-dependent impedance magnitude response, in accordance with an embodiment of the disclosure;

[0065] Figure 32 illustrates an amplitude-dependent phase transition, in accordance with an embodiment of the disclosure;

[0066] Figure 33 illustrates a real component of electromechanical impedance vs. normalized frequency, in accordance with an embodiment of the disclosure;

[0067] Figure 34 illustrates an imaginary component of electromechanical impedance vs. normalized frequency, in accordance with an embodiment of the disclosure;

[0068] Figure 35 illustrates amplitude-dependent dynamic response and yieldtransition characterization, in accordance with an embodiment of the disclosure;

[0069] Figure 36 illustrates a time domain ringdown response following excitation, in accordance with an embodiment of the disclosure;

[0070] Figure 37 illustrates a time-domain recovery and / or rebuild response, in accordance with an embodiment of the disclosure;

[0071] Figure 38 illustrates example principles by which rotation of a container and gravity combine with controlled excitation to generate shear deformation within a material adjacent to a sensing assembly, in accordance with an embodiment of the disclosure; and

[0072] Figure 39 illustrates an example embodiment in which excitation of a sensing assembly is coordinated with rotation of a container, in accordance with an embodiment of the disclosure.DETAILED DESCRIPTION

[0073] 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 7 of 27214528758vlembodiments of the disclosure are show n. 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.

[0074] 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 hardw are 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.

[0075] 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 battery7pow ered device that can send and receive w ireless signals to other beacons and / or gateways and / or other devices (over BLE, BLE Long Range, LTE. 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.Page 8 of 27214528758vl

[0076] As described herein, a “user” may be an individual associated with an entity'. As such, in some embodiments, the user may be an individual having past relationships, current relationships or potential future relationships with an entity. In some embodiments, the user may be an employee (e.g., an associate, a project manager, an IT specialist, a manager, an administrator, an internal operations analyst, or the like) of the entity' or enterprises affiliated with the entity.

[0077] 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.

[0078] 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.

[0079] 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 9 of 27214528758vlstate 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 lifting precast concrete, an excavator lifting a bucket of soil, a robot painting a wall, etc.

[0080] 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.

[0081] As used herein, an “engine” may refer to core elements of an application, or part of an application that serves as a foundation for a larger piece of software and drives the functionality of the software. In some embodiments, an engine may be self-contained, but extemally-controllable code that encapsulates powerful logic designed to perform or execute a specific type of function. In one aspect, an engine may be underlying source code that establishes file hierarchy, input and output methods, and how a specific part of an application interacts or communicates with other software and / or hardware. The specific components of an engine may vary based on the needs of the specific application as part of the larger piece of software. In some embodiments, an engine may be configured to retrieve resources created in other applications, which may then be ported into the engine for use during specific operational aspects of the engine. An engine may be configurable to be implemented within any general purpose computing system. In doing so, the engine may be configured to execute source code embedded therein to control specific features of the general purpose computing system to execute specific computing operations, thereby transforming the general purpose system into a specific purpose computing system.

[0082] As used herein, “authentication credentials” may be any information that can be used to identify of a user. For example, a system may prompt a user to enter authentication Page 10 of 27214528758vlinformation such as a username, a password, a personal identification number (PIN), a passcode, biometric information (e.g.. iris recognition, retina scans, fingerprints, finger veins, palm veins, palm prints, digital bone anatomy / structure and positioning (distal phalanges, intermediate phalanges, proximal phalanges, and the like), an answer to a security question, a unique intrinsic user activity7, such as making a predefined motion with a user device. This authentication information may be used to authenticate the identity of the user (e.g., determine that the authentication information is associated with the account) and determine that the user has authority to access an account or system. In some embodiments, the system may be owned or operated by an entity7. In such embodiments, the entity7may employ additional computer systems, such as authentication servers, to validate and certify resources inputted by the plurality of users within the system. The system may further use its authentication servers to certify the identity of users of the system, such that other users may verify the identify of the certified users. In some embodiments, the entity7may certify the identify of the users. Furthermore, authentication information or permission may be assigned to or required from a user, application, computing node, computing cluster, or the like to access stored data within at least a portion of the system.

[0083] It should also be understood that “operatively coupled,” as used herein, means that the components may be formed integrally with each other, or may be formed separately and coupled together. Furthermore, “operatively coupled” means that the components may be formed directly7to each other, or to each other with one or more components located between the components that are operatively coupled together. Furthermore, “operatively coupled” may mean that the components are detachable from each other, or that they are permanently coupled together. Furthermore, operatively coupled components may mean that the components retain at least some freedom of movement in one or more directions or may be rotated about an axis (i.e., rotationally coupled, pivotally coupled). Furthermore, “operatively coupled” may mean that components may be physically7connected, electronically connected, and / or in fluid communication with one another.

[0084] 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.

[0085] 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 Page 11 of 27214528758vlthe 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.

[0086] 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 described 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.

[0087] As used herein, a “container” may include any vehicle, structure, vessel, or apparatus designed to hold, store, transport, or the like, building materials. Containers may¬ be used for material storage and may include drums (e.g.. a drum 402). vehicles (e.g., vehicle 400), 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.

[0088] 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 Page 12 of 27214528758vlproject, 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.

[0089] 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 facade, 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 include 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.

[0090] 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 toPage 13 of 27214528758vlnot 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

[0091] 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 and / 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 sensor 500 as described herein), 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.

[0092] 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.

[0093] 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.

[0094] 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 Page 14 of 27214528758vlcharacterizations. 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, LTE, 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.

[0095] 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.

[0096] 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.

[0097] 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 Page 15 of 27214528758vlto 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).

[0098] 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.

[0099] 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.

[0100] As used herein, "material” may refer to a construction material used during a construction process. In this regard, a material may include any type, composition, or the like of material. For example, a material may be homogenous or heterogeneous. In this regard, the material may include one or more other materials used to create the material. For example, the material may be fresh concrete. In this example, the fresh concrete may have materialsPage 16 of 27214528758vl(e.g., sub-materials) that may include water, aggregate, cementitious mix, and the like to make the fresh concrete.

[0101] 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) for 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 variability7in the proportions of constituent components for the same material formulation.

[0102] 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.

[0103] 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 Page 17 of 27214528758vlwhich the material is used (e.g., an attribute that is context independent). By way of a nonlimiting 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.

[0104] 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 metric, 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).

[0105] 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.Page 18 of 27214528758vltemporal 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).

[0106] 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 contextual 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.).Page 19 of 27214528758vl

[0107] 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 raw7materials 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 reclassified 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.

[0108] 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.).Page 20 of 27214528758vl

[0109] 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, C-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), C-data may refer to data entries that are indicative of the container 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 elements (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.)).

[0110] 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.

[0111] 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.

[0112] 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.Page 21 of 27214528758vl

[0113] 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, blenders, injection molding machine, cooling systems, chemical reactors, crystallizers, filters / separators, and more.

[0114] 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.

[0115] 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

[0116] Vehicles (e.g., concrete trucks) in the construction industry are typically equipped with a rotating drum (e.g., a container as described herein) that mixes concrete during transit. There are significant challenges in determining the behavior and state of the material inside the drum. For example, the drum is sealed and constantly rotating. The concrete quickly coats anything inside the container with a thick and abrasive layer. The trucks vibrate heavily and operate in noisy, uncontrolled environments. Further, conventional methods for analyzing material inside a container of a vehicle requires substantial modifications to the truck in order to capture sufficient data to analyze the material. ThisPage 22 of 27214528758vlcreates significant challenges due to the resources required for modification along with modifications leading to issues with operation of the vehicle. Thus, solutions that maintain operability of the vehicle while providing insight into the material in the truck are needed. As will be described herein, sensors may be used to capture data from the truck via sensors mounted internally and externally to the vehicle. In this regard, the installation of such sensors to determine material properties of the concrete must be simple so that truck operates and other individuals do not need to extensively modify the truck. In this regard, the sensors (e.g., sensors 500 as described herein) may be installed on external portions of the truck to provide easier installation without direct exposure to concrete. The external sensors may be thin, so they do not interfere with the drum's rotation. In other embodiments, sensors may be placed on internal portions of the drum to directly measure the concrete and provide high-fidelity readings of consistency, flow, material properties, and the like.

[0117] The sensors may ingest data relating to any material property as discussed herein. In this regard, the sensors and subsequent processing devices may use such ingested data to determine material properties and / or predicted outcomes of the material. Further, the data may be captured in a variety of ways, and may relate to the material, the truck, the environment, and / or other data sources, as described herein. In this way, the data captured may be used to directly or indirectly analyze the material. Further, data may be used for contextualization purposes that may provide a holistic view of the material in the context of the environment it is situated within. Thus, the data sources and data analysis as described herein provide vehicle based material determinations.

[0118] The present disclosure relates to concepts, systems, and methods associated with monitoring of materials in transit. Additionally or alternatively, the methods, systems and concepts herein may relate to sensing of materials in transit through road transport, maritime transport, airborne transport, rail transport, pipeline transport and / or other forms of transportation. Additionally or alternatively, the methods and / or systems herein may relate to monitoring building materials. In some embodiments, these building materials may include cementitious materials. In some further embodiments, this may include afresh cementitious material, such as fresh concrete and the like, being transported in a concrete truck. Additionally or alternatively, in some cases, concrete trucks may include the use of volumetric concrete mixers, wherein fresh concrete is added and mixed throughout the transportation process. Additionally or alternatively, in some embodiments, the monitoring system may include real time monitoring systems, for instance through the use of sensor systems. Additionally or alternatively, the monitoring system may be used for determinations Page 23 of 27214528758vlof properties / attributes / characteristics of the material (e.g. fresh concrete). In some embodiments, this may include determinations associated with the rheology of the fresh cementitious mix. Additionally or alternatively, this may include determinations associated with compositional properties / composition. Additionally or alternatively, the attributes of the fresh cementitious mix may be determined in whole or in part based on data gathered by the sensing system regarding the fresh cementitious mix. Additionally or alternatively, the methods / systems herein may include methods for data processing and / or analysis associated with the data collected by the sensing systems and / or any other data, including digital documentation, machine data such as truck data, and / or otherwise. Additionally or alternatively, the methods herein may include machine learning based methods for data processing or analysis. Additionally or alternatively, the sensing system disclosed herein may include the use of any wave-based sensing method described elsewhere herein. Additionally or alternatively, the sensing system herein may include the use of smart devices. Additionally or alternatively, the sensing system may include a plurality of devices and / or sensing devices. Additionally or alternatively, this plurality of devices may be able to transmit and / or receive data between each other. Additionally or alternatively, these devices may transmit data through wired or wireless communications and / or communications protocols. Additionally or alternatively, the devices herein may be installed at a plurality of locations inside the truck, and in a plurality of configurations. Additionally or alternatively, any configuration described herein associated with container sensing devices may be applicable for truck sensing devices (e.g. ring configuration circling the inside surface of the drum). In some embodiments, the systems and / or methods disclosed herein may include a control system for the truck drum. Additionally or alternatively, the control system may operate in whole or in part in response to data collected by the sensors herein. Additionally or alternatively, the control system may be configured to operate in whole or in part based on data associated with raw materials and / or mixed fresh concrete at the batching plant. This may include any data described elsewhere herein for example data associated with aggregates, cement powder and / or other materials. For example, in one embodiment, the truck control system may be configured to add additional water into the drum mixer based in whole or in part on data from the batching plant indicative of low moisture levels associated with a raw material (e.g. aggregates). By way of another example, the truck control system may be configured to increase drum rotation speed (for increased mixing), based on data from the batching plant indicative of mix inhomogeneity. Additionally or alternatively, the truck control system may be configured to operate in whole or in part based on data associated with concrete at the pour. Additionally Page 24 of 27214528758vlor alternatively, a truck control system parameter may be modified in response to data associated with concrete at the pour. Additionally or alternatively, the concrete at the pour may represent the same mix as the concrete in the truck. For example, in one embodiment, data associated with the cementitious mix at the pour may be analyzed, and it may be determined that the concrete tends to underperform its strength target (e.g. average is lower than expected or lower than target). The control system may modify quantities of water addition for future batches in transit, based on that data and / or analysis and / or determination, for example by adding less water in transit. Additionally or alternatively, the control system may include an Al system that modifies control parameters based on ingested data. Additionally or alternatively, control system methods for the truck may include and / or make use of any method and / or systems described elsewhere herein such as any of the batching plant control system based methods.

[0119] In some embodiments, the present disclosure may include methods and / or systems to determine attributes of a fresh cementitious mixture in a truck mixer / volumetric mixer / drum. Additionally or alternatively, the attributes may include any property or characteristic of the fresh concrete described herein, elsewhere herein and / or otherwise understood to be such by one skilled in the art. Additionally or alternatively, rheological properties of the material may be determined. Rheological properties may include any properties associated with deformations and flow of fresh concrete, and may include (but not be limited to) the following: Yield Stress; Plastic Viscosity; Thixotropy; Workability; Segregation Resistance; Bleeding; Cohesiveness; Flowability; Shear Stress; Setting Time; Pumpability; Slump; Consistency; Compactability; Vibration Sensitivity; Stickiness; Dilatancy; Stability; Adhesiveness; Penetrability .

[0120] Additionally or alternatively, compositional properties may be determined. Such properties may include any data associated with the presence of material in the batch. For example, this may' include recipes (in whole or in part), water content, moisture content, water-to-cement ratio, chloride content, spectral analysis, air content (e.g. entrained air content) and the like (including as well any compositional property as described elsewhere herein). Additionally or alternatively, structural properties may be determined. Such properties may include for example batch density, density distribution, particle packing density7, aggregate density7, air void distribution, cohesion, homogeneity / inhomogeneity and the like. Additionally or alternatively, other properties of fresh concrete in the truck may be determined, such as temperature and / or temperature distributions, concrete hydration and / or early indicators of concrete setting, pH levels. Additionally or alternatively, the systems Page 25 of 27214528758vland / or methods herein may be used to predict and / or determine future behaviors or properties of the cementitious mix such as properties of the mix at the pour (e.g. compressive strength evolution). Additionally or alternatively, spatial mapping and / or motion of the fresh concrete in the truck may be sensed and analyzed to determine and / or infer other properties / attributes. Additionally or alternatively, data from multiple stages of the value chain may be used to make predictions and / or inferences associated with future behavior of a material. For example, the expected concrete strength evolution during curing of a given mix that is currently in transit may be predicted based on historical data associated with other batches of the mix, as well as real-time data associated with the current batch under consideration. Additionally or alternatively, the methods herein may be able to detect anomalies in mixes, based on data associated with the fresh mix batch in transit. For example, the methods herein may include Al based anomaly detection methods, wherein if a sensed attribute of a given batch deviates from the expected value (e.g. out by a pre-selected number of standard deviations), the system may output an anomaly with the mix. Additionally or alternatively, the methods herein may include determining, in whole or in part based on the collected data, whether the batch in transit is expected to fail or meet specifications. Additionally or alternatively, this may include ingesting specification documentation, extracting specification requirements associated with the mix, and comparing it to predictions.

[0121] In some embodiments, rheological properties of concrete may be determined and associated / correlated with a slump value determination. Additionally or alternatively, in some embodiments, the viscosity of the fresh concrete in the drum may be determined, for example through any sensing modality' described herein, and this viscosity value may be converted into an equivalent slump value. Additionally or alternatively, in some embodiments, this may be carried out using an empirical model relating the quantities e.g. Slump = f(Viscosity). Additionally or alternatively, in some embodiments, this conversion may be carried out using machine learning based techniques, which may be trained on labelled slump-viscosity data for a variety' of different mixes. Additionally or alternatively, the correlation between slump and the rheological attribute may in general be multivariate, such that the empirical model and / or the Al model may take into account a plurality of variables. This may include any parameter, property, contextual condition and the like described herein. For example, one empirical or Al model may relate the ambient temperature, viscosity, and drum geometry' data to a measure of the slump e.g. Slump = f(Viscosity, Yield Stress, temperature, geometry). Additionally or alternatively, the methods herein may include two sets of models: a first model configured to determine a rheological Page 26 of 27214528758vlproperty of the fresh concrete, which may ingest sensor data, truck machine data and the like, and output one or a plurality of rheological properties; and a second model configured to determine a slump value based on the rheological property and / or other relevant data such as contextual conditions data and / or compositional property and / or raw materials data. Additionally or alternatively, in some embodiments, a single model may be used to correlate the measured data directly with slump, either empirically and / or using Al based techniques. Additionally or alternatively, this may include correlations of for example electrical data, electrochemical data, mechanical data, E&M data and / or the like, to slump data.

[0122] As now will be described more fully herein, the present disclosure presents systems and methods for vehicle based material determinations. Figure 3 illustrates an example embodiment for control systems and sensor devices for vehicle 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 3 may include a network 104 communicatively coupled to one or more devices or components, such as a control system 200, a user device 106, one or more sensors 500, or the like. Further, in some embodiments, the sensor 500 may include one or more sensors. For example, sensors 500 may be any number of sensors 500 used in the system 100. Further the control system may be communicatively coupled to a database 108.

[0123] 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.

[0124] 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 Page 27 of 27214528758vlany 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.

[0125] 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, as shown in Figure 4.

[0126] 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 a server may provide or supplement the functionality of particular circuitry'.

[0127] 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.

[0128] In some embodiments, the present disclosure may include methods for vehicle based material determinations. 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 Page 28 of 27214528758vl(e.g., the sensors 102). For example, the method may include determining mix proportions based on raw material and / or input material data.

[0129] Further, in some embodiments, the data generated by the sensor(s) 500 may include parameters relating to the portion of the building material or fresh concrete. A non-exhaustive list of potential building material parameter data may include material contextual conditions data (e.g., viscosity, 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., cleanliness of a sensor, 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.

[0130] 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), production 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.

[0131] 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 allPage 29 of 27214528758vlproduction processes data objects described herein. Further, models may be trained to create associations between raw materials and processes, and output materials.

[0132] In some embodiments sensors 500 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., Al 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.

[0133] 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 Spectroscopy, 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.

[0134] Additionally, or alternatively, sensors 500 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 Page 30 of 27214528758vl(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.

[0135] 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 electromagnetic 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.

[0136] 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, thermogravi metric 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.

[0137] 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 matenal Page 31 of 27214528758vllife 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.

[0138] Additionally, or alternatively, in some embodiments, sensor data may be used for model calibration or validation. Additionally, or alternatively, materials may be sensed using 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.

[0139] 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.

[0140] 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 Page 32 of 27214528758vlnatural 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 concrete 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.

[0141] In some embodiments, the material properties may be changing over time in the truck. In this regard, measurements of the material properties may be taken as a timeseries. In some embodiments a consistence measurement may be used to determine the smoothness / uniformity of the mixed constituents, and the extent to w hich the overall mix can “flow”. A reasonable proxy for consistence can be the viscosity of the material in question. In some embodiments, a water content may be determined via water to total mass ratio allowing for water to binder ratios. In some embodiments, the mass and / or density of the mix in the truck may be determined via force / area measurements of the sensors 500 throughout the drum 402. In some embodiments, material constituent measurements may be taken to determine the make up of the material.Page 33 of 27214528758vl

[0142] In some embodiments, a truck-mounted material sensing system characterizes a flowable or partially flowable building material or construction material (for example, fresh concrete) in situ during mixing, transport, waiting, and discharge. The system may be installed at or near an interface to the material such as a drum wall, hatch, chute, or a serviceable insert. The system may operate while the drum (e.g., the container) is stationary or rotating and while the truck is at rest or moving.

[0143] Additionally, or alternatively, the geometry coupled to the sensor 500 (e.g., a piezoelectric sensor, as described herein) may be configured to cause normal and tangential components of stress, creating shear in the material (e.g., concrete). In this regard, impedance across the piezoelectric sensor 500 may be indicative of factors such as slump or other predicted material outcomes. Further, analysis may be performed that produces data indicative of material properties based on peaks or dampening associated with impedance of the sensors 500. Further, in some embodiments, techniques such as time relaxation may be used to determine material property data.

[0144] In some embodiments, and as discussed herein, the varying geometric shapes of the sensor head may be used to produce different forces (e.g., shear forces) in the material. In this regard, as the piezoelectric sensor is excited and moves, shear forces may be generated in the material that may be ingested as data relating to certain material properties (e.g., viscosity). In some embodiments, the sensor data may be indicative of a shear force experienced by the housing when interacting with the material. In some embodiments, particular geometries may be used to create forces in the material along with varying piezoelectric excitation methods. For example, using resonant shapes for the sensor head may enhance particular features, peaks, or the like in the captured impedance spectra data. In another example, the sensor may cause more material yielding, which may mimic a slump test and predict slump test outcomes. In this regard, the piezoelectric sensor, upon removing the geometry from the material during an excitation event, may perform an approximate slump test due to the material being released from confinement of the geometry7. Further, data may be captured that may be used to determine material properties from the approximate slump test.

[0145] In some embodiments, the sensors 500 may be modulated or configured to perform various tests or excitation events on the material. For example, a piezoelectric sensor may be configured to vary its amplitude or modulate its frequency during an excitation event. In this regard, impedance measurements at a plurality of frequencies may provide an impedance spectra and measurements at multiple amplitudes may be used to determine Page 34 of 27214528758vlmaterial yield by analyzing resonance peak shifting and other non linear effects in the spectra. Further, in some embodiments, the determined material yield may be used to determine slump outcomes for the material. In this regard, the material yield may indicate under which forces the material no longer has modulus or operate in a normal, linear regime. Further, analysis of how the spectra varies at multiple amplitudes to determine a degradation of resonance may be performed to indicate a yield point or a yield range.

[0146] In flowable and partially flowable construction material (e.g., concrete), macroscopic workability metrics including slump and slump-flow are governed primarily by resistance to shear rather than resistance to compression. Yielding in such materials corresponds to the onset of irreversible deformation under shear stress, and not to failure under purely normal or compressive loading. Although gravitational loading and mechanical contact introduce normal stresses, yielding and flow occur when those stresses are resolved into shear along internal planes within the material. Accordingly, the sensing assemblies described herein are intentionally configured to generate shear deformation at a material interface by mechanically transforming actuator motion — typically dominated by normal displacement — into tangential stress through the use of a shear-generating geometry. This transformation enables the system to probe yield-related and flow-related behavior directly, such that changes in electromechanical impedance, resonance features, damping, and amplitude-dependent nonlinearity reflect shear resistance, dissipation, and structural breakdown within the material. Without such geometry -induced shear, electromechanical excitation would primarily probe elastic contact stiffness and would not reliably correlate with yield stress, slump, or flow.

[0147] Further, in some embodiments, the geometry of the sensor head may include various configurations. In some embodiments, the geometry may be configured to take the shape of a “pocket” or the like. In this regard, the geometry may hold the material without preventing the material from passing through the geometry. For example, the geometry may take the shape of a “bowl” or the like wherein the material may sit within indefinitely. In this regard, the sensor head may, upon excitation, move the entirety of the material within the geometry. In this regard, geometry configured to hold the entirety of the material may be used to determine one or more material properties and / or one or more predicted material outcomes.

[0148] In some embodiments, the geometry7of the sensor head may include a geometry with a plurality of holes, or the like. In this regard, the sensor head or geometry may allow material to pass through it. For example, a plurality of holes may be incorporated Page 35 of 27214528758vlinto the geometry that allow the material to pass through at a specific rate. In some embodiments, the plurality’ of holes may be configured to allow a certain viscosity of material to pass through at a certain rate. In other embodiments, the plurality of holes may each be the same or different sizes, depending on how much concrete should be allowed to pass through. In some embodiments, as the sensor is excited, the sensor head with the plurality' of holes may move a portion of the concrete, with some of the concrete flowing through the holes while the sensor head moves. In this regard, geometry configured to allow a portion of the material to pass through (e.g., via a plurality of holes) may be used to determine one or more material properties and / or one or more predicted material outcomes.

[0149] Further, standardized slump and flow-based measurements are used to characterize the consistence of concrete during its pre-curing stage. The consistence of concrete is reasonably approximated by the properties of a Bingham plastic (i.e., a viscoplastic fluid that behaves as a solid under low stress, and a liquid under high stress). For a Bingham plastic, all information needed to characterize any measure of consistence can be derived from the yield stress and viscosity’ of the material. In general, the system 100 as described herein may be able to predict the slump or flow that would be measured for a given sample of concrete extracted from a delivery' truck at an arbitrary time, $t$, on its journey from a batching plant to a construction site. This problem is therefore one of determining the yield stress and viscosity of the concrete in the delivery truck as a function of $t$; derived from measurements that the system is able to take from the truck during transit (and perhaps some additional metadata obtained prior to the delivery'). As described herein, the system may convert yield stress and viscosity' into a predicted slump, slump rate, flow and flow rate test result at any given time.

[0150] For example, characterizing yield stress and viscosity may include using the spatio-temporal evolution of the surface of the concrete to extract yield stress and viscosity. In this regard, the system may calculate surface normal vector field as function of surface / spatial position and time, use normal vector rate change to calculate strain field, use normal vector rate change to calculate velocity' field, use velocity' field to calculate deformation rate, use deformation rate to calculate shear rate, use stress field and shear rate field to solve for the Bingham constitutive relation at local regions on the concrete surface, and require enough degrees of freedom to solve Bingham relation for yield stress and viscosity (2 degrees of freedom in linear function). In this regard, the system may recover sufficient degrees of freedom spatially (obtaining a local, spatially distributed set of average values; assuming approximately equal consistence over local scale size) and recover Page 36 of 27214528758vlsufficient degrees of freedom temporally (obtaining a temporal change in measured values at local regions; assuming approximately equal consistence over local temporal windows).Sensor Placement

[0151] As shown in process flow 300 of Figure 5, the system 100 is configured to ingest data from one or more sensors 500 and determine material properties and predicted material conditions of a material. As mentioned herein, the material may be any material used during a process (e.g., a construction process). By way of non-limiting example, and in some embodiments, the material may include concrete. In some embodiments, as shown in block 302, the system may receive sensor data from a sensor associated with a vehicle. The vehicle may include a variety of vehicles used to transport the material. For example, and as will be discussed in greater detail below, the vehicle (e.g., a truck 10 as shown in Figure 1 and / or the vehicle 400 as shown in Figure 6) may include a concrete truck used for the transportation of the material.

[0152] Figure 6 illustrates a non-limiting example of a vehicle used to perform the methods and procedures as described herein. For example, the vehicle 400 of Figure 6 may be used to transport the material. In this regard, the vehicle 400 may be a concrete truck that is configured to transport concrete (e.g., the material) to a destination. Further, the vehicle 400 may include various components used for the transport of concrete, which may include components that are configured to maintain the workability and freshness of the concrete during transportation. In some embodiments, the vehicle may be equipped with a container, vessel, drum, or the like configured to hold the material. For example, as shown in Figure 6, the container 402 may be a drum configured to at least partially contain the material. In this regard, the container 402 may, as described above, rotate during transportation to maintain the concrete. For example, the vehicle 400 may be equipped with a drum configured to rotate so the concrete is continuously moving to keep the concrete mixed and prevent it from hardening, separating, settling, and the like before it reaches the destination.

[0153] In some embodiments, the sensors 500 may be placed on a body separate and distinct from the vehicle 400. For example, the sensors 500 may be placed on another vehicle, a structure, the ground, or the like. In this regard, the sensors 500 on a separate body may be activated (e.g., triggered to ingest material data) when the vehicle 400 is within a specified proximity', at a predetermined time, via a user (e.g., truck driver) activating the sensor 500, or the like.Page 37 of 27214528758vl

[0154] In some embodiments, sensor devices may be used to monitor the fresh cementitious mix in transit and / or its contextual conditions. Examples of sensor types that may be used may include any wave-based sensor described elsewhere herein. Sensor types may also include mechanical sensors such as load cells, accelerometers, gyroscopes, inclinometers, pressure sensors, sound based sensors (e.g. microphones, ultrasonic sensors) and the like; thermal sensors such as thermocouples, digital thermometers, infrared based sensors; electrical sensors such as electrochemical impedance sensors, capacitive sensors, conductivity sensors, resistance sensors and the like; electromagnetic sensors such as RF based sensors, magnetic flow sensors and the like; spectroscopy based sensors such as those described elsewhere herein; chemical sensors such as pH sensors. The sensors described herein may be used to sense one, and / or a plurality, and / or any combination of the fresh concrete properties described herein, including whilst the concrete is being mixed in the truck drum. Additionally or alternatively, these sensors may be installed at various locations on or in the truck, including inside the drum.

[0155] Without loss of generality, impedance based measurements (e.g. electromechanical impedance spectroscopy, electrochemical impedance spectroscopy, E&M wave impedance spectroscopy and the like), may be used to determine properties such as rheological properties of the concrete. Additionally or alternatively, data associated with wave-based sensing devices may be used for fresh mix property characterization. Additionally or alternatively, in some embodiments, S-parameters and / or T-parameters may be used to determine properties of the fresh mix in the drum. Additionally or alternatively, such properties may be determined using measured resonance peaks in the impedance spectra, as well as shifts in the resonance peaks of the measured impedance spectra. Additionally or alternatively, this may include generating a wave / oscillation using a wavebased actuator / sensing device, sensing the reflected and / or transmitted and / or scattered wave, analyzing received data to determine data associated with S- and / or T-parameter(s), and determining a property7such as a rheological property' of the cementitious mix using the in whole or in part based on the data associated with the S- and / or T-parameter(s). This may include determining S- and / or T- parameters for actuated waves generated inside the rotating drum, and into the fresh concrete by mechanical wave-based sensing devices for example. Additionally or alternatively, for any wave-based embodiment, a plurality of pulse types may be driven into the cementitious mix, such as sine waves, square waves, multi-sine pulses and the like. Additionally or alternatively, wave based sensing devices may be installed on the inside surface of the rotating drum, and transmit waves into the concrete, and receive them Page 38 of 27214528758vlusing receivers to generate tomographs by analyzing wave scattering at different angles in the truck (as the truck rotates). This may be executed using electromechanical based techniques, electrochemical impedance spectroscopy based tomography techniques, as well as electromagnetic wave impedance spectroscopy based techniques. Additionally or alternatively, properties such as rheological properties and / or image reconstruction / tomographs may be determined using Al models that may ingest sensor data and output the desired property or tomograph. Additionally or alternatively, these Al models may be trained on simulations and / or simulated data. Additionally or alternatively, simulations may include a simulations of the behavior of a fresh cementitious mix with known properties (e.g. known viscosity) inside of a rotating truck drum. Additionally or alternatively, simulations may include simulating the behavior of mixes in different geometries of drums, simulating the behavior of different mix recipes, simulating the behavior of mixes with varying water content and / or water to cement ratios, simulating varying contextual conditions e.g. temperature, vary ing rotational speeds, varying applied torque and / or any other such parameter. Additionally or alternatively, in some embodiments, the simulation may be a physics-based simulation. Additionally or alternatively, the simulation may include simulating the target sensor device, wherein the sensor device generates simulated data associated with the fresh mix. Additionally or alternatively, the simulated data may be fed to the Al model during training, which may learn to correlate any of the simulated parameters with the desired property outcome as well as the simulated sensor data with the desired property(ies) or parameter(s). Additionally or alternatively, in the same way, simulation based methods may also be used to train a machine learning model to generate tomographs.

[0156] In some embodiments, the sensors 500 may be placed on a variety of places throughout and among the vehicle 400. In some embodiments, the sensors 500 may be operatively coupled to the vehicle 400. For example, as shown in Figure 6 the sensors 500 may be placed on any portion of the vehicle 400 via a variety of attachment or coupling methods. In some embodiments, the coupling methods of the sensors 500 may include magnetic attachments to the drum, magnetic bases of the sensor, adhesive, floating sensors in the material, coupling with the internal portion (e.g., wall, extensions, blades, internal hatch wall, and the like), attachment method that rotates with the drum, attachment method that says fixed in relation to the drum, in contact with the concrete, directed at the concrete, indirectly sensing the concrete, through holes in the sides of the drum, through holes in the blades. Further, in some embodiments, cables may be used to suspend the sensor and cause Page 39 of 27214528758vlthe sensor to stay in place. In this regard, the cables may have some flexibility used to measure strain as the drum rotates and concrete drags against the cables and / or sensor. A device in the middle cable may have 3 strain gauges in it, each attached to a different cable, measuring stress experienced by the cable itself. In some embodiments, the sensor PCB board may be in the middle and sensing components may be along the cables.

[0157] In some embodiments, torque sensors may be used to determine the torque used to rotate the drum. Rheological properties such as the slump or workability of the cementitious mix may in whole or in part be determined based on these measurements. Additionally or alternatively, in some embodiments a higher required torque may be indicative of higher viscosity and conversely, a lower required torque may be indicative of lower viscosity. Additionally or alternatively, the sensors herein may include hydraulic pressure sensors, and the rheological property determination may be done using hydraulic pressure measurements associated with pressure applied to rotate the drum (e.g. as proxy for the torque). Additionally or alternatively, the determination may be done in whole or in part based on the energy / power consumption required to rotate the drum. Other parameters which may be used to make the determination and / or may influence the result of the determination and as a consequence may be accounted for may include the ambient temperature, humidity, concrete batch volume, the batch weight (which may for example be determined using weight measuring system and / or sensors in the truck), rotational speed of the drum, drum orientation / angle / slope, drum geometry. In some embodiments, data associated with raw materials and / or composition of the mix may be used to determine, in whole or in part, fresh concrete properties in the truck, including rheological properties, such as aggregate size, shape, and texture, aggregate distribution, binder type (e.g. portland cement, GGBS, fly-ash), admixtures (e.g. plasticizers, air-entraining admixtures), air content and the like. Additionally or alternatively, mix homogeneity may also be a factor influencing the required torque, but that are not necessarily directly related to the rheological properties under consideration. Further sensing methods may be used to account for this. For example, imaging based methods, such as installed imaging sensors (e g. cameras) inside the drum, may be used, with associating image / feed processing to detect homogeneity and / or bleeding / segregation of the cementitious mix and its raw materials. Data extracted from documentation may also be used in the methods herein. For example, data associated with presence of admixtures, or aggregate types / shapes / sizes / grading, may be used to adjust the measured torque / rotational energy associated data, as the mix’s compositional properties may alter the required torque without impacting for example its viscosity. In some embodiments, the rheological properties Page 40 of 27214528758vlmay be determined in whole or in part from the measured data using a model. Additionally or alternatively, this may be a multi-modal or multivariate model. Additionally or alternatively, this may be a machine learning model. Additionally or alternatively, this may include a model relating any of the torque or torque-associated parameters listed herein (e.g. power, hydraulic pressure and the like), to any rheological property' herein, which may include viscosity, workability, pumpability, slump, yield stress and the like. Some example models that may be used include bingham plastic models which may be used to determine plastic viscosity and / or yield stress of the fresh concrete based on shear stress (which may be determined in whole or in part using torque measurements) and shear rate (which may be determined in whole or in part using rotational speed). Other models which may be used include Herschel-Bulkley Models, Modified Reiner-Riwlin Equation, as well as empirical torque-slump relations for example of the form T=a+bS, where T represents torque, and S slump, empirical machine learning models such as neural networks, energy / power based models and the like. The sensing system herein may include installing torque sensors on the drive shaft and / or motor powering the drum, or sensors attached on the drum to measure rotational speed. Additionally or alternatively, this may include installing hydraulic pressure sensors in the hydraulic lines to measure resistance, power sensors positioned on the motor to monitor power output, and / or the like. Additionally or alternatively, load cells may be installed in the drum to measure the weight of the concrete batch. Additionally or alternatively, some of the data described herein may be accessed as machine data from the truck (e.g. energy consumption of drum, batch weight and the like).

[0158] Without loss of generality, the methods and / or techniques listed herein may be used for other sensing modalities that may be non-E&M based, or may use E&M based techniques in combination with one or a plurality of other techniques / modalities.

[0159] In some embodiments, the sensing system herein may be used to measure and track concrete properties, including rheology, and / or generate tomographic images and / or mappings and / or reconstructions of the fresh mix and / or parameters / attributes associated with the mix. In some embodiments, this may be done using E&M based sensing systems. Additionally or alternatively, the E&M based sensing system may include one or a plurality of E&M wave-based transmitters and / or receivers, configured to generate E&M pulses into the concrete and sense reflected and / or transmitted signals. Additionally or alternatively, in some embodiments, the device may be magnetically attached (e.g. using magnetic clamp) onto the inside surface of the drum, and / or may be attached using a vacuum-based suction system (this may apply to any of the sensing device embodiments herein). Additionally or Page 41 of 27214528758vlalternatively, the device may be configured to function given the rotating of the drum. Additionally or alternatively, the device may scan the inside of the drum, including the fresh mix, from a number of different angles periodically over time. Additionally or alternatively, the system / method may include data processing and / or machine learning based methods to process and / or interpret the data and / or relate it to the desired quantity.

[0160] In some embodiments, the system herein may be configured to generate tomographic images of the fresh mix in the drum. Additionally or alternatively, this may include generating electromagnetic pulses at regular or irregular intervals into the drum and reconstructing images of the drum based on measured reflected pulses. Attributes of the fresh concrete attributes and / or presence of its raw materials (e.g. inhomogeneities in raw materials) may be determined based on fluctuations in electromagnetic properties such as permittivity, conductivity, and / or reflectivity of the fresh concrete, which may be determined. This may be used to determine presence and / or proportions and / or quantity and / or distributions of aggregates in the mix as well as other raw materials or inhomogeneities. Additionally or alternatively, the methods herein may be used to determine data associated with the dielectric constant of the mix, which may be associated or related to the water content in the mixture. This may be used to monitor the evolution of the water-to-cement ratio, moisture content and / or water content in the mixture, throughout its journey in the truck. This may be used in conjunction with machine data from the truck control system to track water additions as well as the effect of the water addition on the rheological properties and / or other properties of the mix. Additionally or alternatively, the E&M based sensing system may generate a plurality of images and / or mappings over time. Additionally or alternatively, these data may be processed and / or analyzed to track the evolution of fresh concrete attributes such as flowability, viscosity, hydration state, aggregate distribution and the like. In some embodiments, the generated pulses may include radio-frequency and / or microwave pulses. Additionally or alternatively, in some embodiments, the system may include a pulse generator, and may be configured to generate pulses of a plurality of frequencies. Additionally or alternatively, the pulse generator may be integrated into a ruggedized system for robustness. Additionally or alternatively, the sensing system may include antennas such as RF antennas, which may be conformal antennas and / or waveguide probes. Additionally or alternatively, in some embodiments, this may include an antenna array. This system may be configured for beam steering. Additionally or alternatively, the system herein may determine time-of-flight, amplitude and phase shifts of transmitted and reflected E&M pulses, and determine / infer dielectric properties and / or data about the internal Page 42 of 27214528758vlstructure of the mix. Additionally or alternatively, as the drum rotates, the signal generator(s) and / or receiver(s) mounted on the inner surface of the drum may rotate as well. Additionally or alternatively, the sensing system may be configured to transmit and receive pulses from / to different locations as the drum rotates and / or at different angles through the fresh concrete. Additionally or alternatively, the different data from different angles may be used to tomographically reconstruct the position and / or motion of the fresh concrete over time. Additionally or alternatively, reconstruction methods and / or algorithms may include inverse scattering methods, which may be used to determine / estimate the permittivity distribution of the fresh concrete in the drum and / or inhomogeneities in the concrete (by analyzing complex scattering patterns and associating scattering to different raw materials for example). Additionally or alternatively, by way of another example, another reconstruction method which may be used by the presently disclosed system may include Time Reversal or Back-Projection Techniques, which may be used to iteratively refine the spatial mapping over time through capturing different angles, and may be used to reconstruct reflectivity and / or dielectric properties distributions. Additionally or alternatively. Al / machine learning based methods may be used for image reconstruction and / or property distribution determinations. For example, this may include neural networks for tomography based methods, which may include the use of deep learning models (e.g. convolutional neural networks) for determination of rheological properties based on signal data. Machine learning models herein may be trained on datasets of known mixes and / or known calibration mixes. Additionally or alternatively, these datasets may be labelled or unlabeled. Additionally or alternatively, the methods herein may include physics based models for data processing. Additionally or alternatively, the methods herein may include hybrid Al and physicochemical model based methods for data analysis / processing. Additionally or alternatively, the methods herein may include real-time adaptive calibration for parameters such as temperature changes, drum geometry and / or mix design parameters, wherein for example based on the difference between incoming data and expected data, the machine learning model may adaptively adjust its weights in real-time. Additionally or alternatively, the system herein may include an Al-based control system, which may be configured to control water addition, admixture (e g. superplasticizer) addition, rotation speed, applied torque and / or other truck control parameters based on incoming data in real-time. Additionally or alternatively, the methods herein may be used to determine rheological parameters such as viscosity and / or yield stress. Additionally or alternatively, this may be done using a machine learning model, for example that correlates changes in E&M signal attenuation and / or phase Page 43 of 27214528758vlchanges to fluctuations in viscosity and / or yield stress. Additionally or alternatively the methods herein may include determining aggregate and / or air void contents and / or distributions across the material. Additionally or alternatively, aggregate-related determination may be done in whole or in part based on differences in dielectric constants between aggregates and other materials in the fresh concrete (e.g. cement paste). Additionally or alternatively, the air-void determinations herein may be carried out by analyzing measured wave reflections. Additionally or alternatively, this may be used to determine aggregate clumping / clustering and / or segregation and / or bleeding. Additionally or alternatively, the system herein may output real time data associated with the fresh concrete in the truck and / or associated with the truck parameters or environmental parameters (e.g. temperature, rotations per minute / rpm, rotation speed, torque and the like). Additionally or alternatively the system and / or methods herein may output representations of real-time slump compared to target slump, measures associated with mix homogeneity and / or aggregate distributions, measures associated with air content / air-void content and / or any measures associated with any other property listed herein. Additionally or alternatively, in some embodiments, the system / method herein may include calibration of the sensing system using known reference mixes e.g. of varying slumps, water-to-cement ratios and the like. Additionally or alternatively, context awareness based methods as described elsewhere herein, may be applied to account for the contextual conditions of the fresh mix, including for the geometry of the drum, the temperature and other parameters. Additionally or alternatively, in some embodiments of the present disclosure, a plurality of sensor devices may be installed and / or distributed along the inside surface of the drum, and E&M waves generated and / or sensed by any one and / or any combination and / or all of the devices may be used for property determination and / or tomography. Additionally or alternatively, in some embodiments the system may include a plurality of sensor types placed for example on the inside surface of the drum, such that a multi-sensor array is used to sense the material. This may include temperature based sensors, accelerometers / inertial sensors, acoustic / vibration based sensors, camera / vision based systems and the like. Al-based data fusion modelling techniques may be used to fuse and analyze the distinct datastreams and determine associated fresh mix properties. Additionally or alternatively, higher frequency pulses may be used for image reconstruction and / or other property7determinations (e.g. terahertz pulses, infrared based methods, LIDAR based methods and the like). Additionally or alternatively, E&M based methods used herein may include RADAR and / or ground penetrating radar-like methods toPage 44 of 27214528758vlprobe and / or localize the fresh concrete. Additionally or alternatively, synthetic aperture radar based techniques may be used for high-resolution image reconstruction.

[0161] The sensors 500 may be located on the vehicle in order to capture data relevant to determining material properties and / or predicted material conditions. In this regard, and in some embodiments, the sensors 500 may be placed so as to contact the material being transported by the vehicle 400.

[0162] In some embodiments, the sensor 500 may be operatively coupled to an internal portion of the vehicle 400 defining a cavity (e.g., the container 402) configured to hold the material. An internal portion of the vehicle 20 is represented in Figure 2 and may include the sensors 500 using contact acoustic probes, ultrasonic probes, internal radar imaging, embedded sensor pills, internal vibration and / or sound emitters / receivers.. For example, and as shown in Figure 12, the sensors 500 may be placed inside the container 402 of the vehicle 400. In this regard, the sensors may be coupled to an internal portion 404 of the container 402, which may include an extension, inner wall, component, or the like. For example, as shown in Figure 15, the sensors 514 may couple directly to the internal portion 404, such as an inner wall. Further, in some embodiments, the internal portion may be an extension such as a mixing blade (e.g., the mixing blade 412) configured to mix the material. In this regard, the sensors 500 may be clamped or otherwise coupled to the mixing blade 412 such that the sensor 500 does not move, slide, or the like while the mixing blade rotates through the material. In some embodiments, the sensors 500 may provide local sensing capabilities (e.g., point sensors, distributed point sensors) used to profile a volume or area of the material inside the truck. Further, the sensors 500 may provide global sensing capabilities that may be used to map the internal geometries of the truck and / or provide a map of the entire material in the truck.

[0163] The sensor 500 may have a variety of configurations such that the sensor is capable of ingesting data associated with the material without being destroyed or becoming incapable of ingesting such data. Figure 7 provides a variety7of configurations of the sensors 500 that may be used to capture data. The sensors 500 may be configured to be powered via a battery that allows the sensors 500 to operate for a long period of time (e.g., days, weeks, months, years, and the like). Further, the sensors 500 may have components that allow the sensor to be operatively coupled to the internal portions (e.g., the internal wall of the container, the mixing blade, and the like) of the vehicle 400 as well as external portions (e.g., the external wall of the container, a suspension, an underbody, or the like) of the vehicle 400. In some embodiments, the sensors 500 may use a global positioning system (GPS), use truck Page 45 of 27214528758vlspeed, IMU data, and the like to provide contextual awareness data for use to interpret mixing data. In this regard, the contextual awareness data may be used to provide additional information or data to the system 100 to determine material properties or predicted outcomes of tests, as described herein. For example, a temperature sensor may provide material temperature data near a piezoelectric senor, while a second temperate sensor near a second piezoelectric sensor provides temperature data. In some embodiments, the system 100 may extrapolate (e.g., linearly, non-linearly, or the like) material property data using the contextual temperature data provided by the first and second temperature probes.

[0164] In some embodiments, the sensors 500 may be coupled to a discharge chute of the truck 400. In this regard, the discharge chute may be configured to allow material to flow through it during discharge of the material. For example, the discharge chute may be an opening into the drum that is used to load and unload material when the truck has reached its destination. In this regard, during discharge or unloading, the front end of the drum may lift upwards and the discharge chute may tilt downwards. Further, during discharge, the drum may rotate in an opposite direction so the mixing blades push material out of the chute. In some embodiments, sensors 500 coupled to the chute may capture material property data.

[0165] In some embodiments, the sensor 502 as shown in Figure 12 provides a plurality of attachment components 503 and an attachment opening 504 so the sensor 502 may couple with the mixing blade 412. The attachment components 503 may include clamps, magnets, fixtures, screws, adhesives, or the like configured to secure (e.g., operatively couple) the sensor 502 onto the mixing blade 412. The attachment opening 504 may be configured to accept a portion of the mixing blade 412 so the sensor 502 may releasably attach to the mixing blade 412. For example, the attachment opening 504 may be a “female” component configured to couple with a “male” portion of the mixing blade 412.

[0166] Further, the sensor 506 as shown in Figure 13 may include the embodiments of the sensor 502 with additional configurations, such as the protruded sensor portion 507. In some embodiments, the sensor may be configured to at least partially contact the material during operation. For example, the sensor may include the protruded sensor 507 to capture the one or more properties of the material via interacting with the material. In this regard, the protruded sensor 507 may include a sensor, such as a piezoelectric sensor, configured to generate a signal upon interacting with the material. For example, when the mixing blades 412 rotate through the material, the protruded sensor 507 may be configured to generate a signal, such as an electrical signal, as the protruded sensor 507 moves through the material. In this regard, the protruded sensor 507 may be used to determine, for example, a viscosity Page 46 of 27214528758vlof the material by analyzing the rotation speed of the mixing blade 412 and signal generated by the protruded sensor 507.

[0167] Further, the sensor 510 as shown in Figure 14 may include the embodiments of the sensor 506 and the sensor 502 along with additional configurations. In this regard, the sensor 510 may include piezoelectric sensors and / or components similar to those as described herein and with respect to other sensors 500. Further, in some embodiments, the sensor 510 may include a base 511 that is configured to include a camera, a LIDAR sensor, or the like. The base 51 1 may be configured to include a cover used as an anti-concrete splashing component that protects the vulnerable sensing equipment within the sensor 510.

[0168] Further, in some embodiments, any sensor 500 as described herein may include a protrusion (e.g.. the same or similar to protrusion 507) configured to ingest data related to the material. In this regard, any sensor 500, such as an internally wall mounted sensor similar to sensors 514 in Figure 15, may include protrusions 507 wherein the protrusions are configured to interact with the material to ingest data used to determine material properties and / or predict material outcomes.

[0169] Further, in some embodiments, the protrusions 507 (and any other sensing components of a sensor 500) may include any sensing functionalities and / or capabilities as described herein. For example, the protrusions 507 may include sensing functionalities configured for piezoelectric sensing capabilities, EMI capabilities, ECI capabilities, RF capabilities, and the like. In this regard, and in a specific non limiting example, a sensor, similar to the sensor shown in Figure 11, may include a protrusion (not shown) wherein the protrusion has sensing components configured for RF sensing of the material 406. Further, in some embodiments, the protrusion may include an antenna.

[0170] The sensor 500 may be operated via a printed circuit board (PCB), such as the PCB 700 as shown in Figure 9. For example, the PCB 700 may include a secure piezoelectric signal plug 702 that may be used to communicate and operate the piezoelectric components in the sensor 500. Further, the PCB may include an accelerometer and gyroscopic sensor 704 used to ingest data relating to the positioning of the sensor 500 at a given time. The PCB 700 may be coupled to a power source, such as an off-board battery 706.

[0171] As shown in Figure 10, a sensor 800 and / or 500 may include a housing 802 and a seal 804 configured to house or protect vulnerable parts of the sensor 500 during operation. In this regard, the piezoelectric sensor 600 as discussed herein may be installed within the housing 802 along with a PCB 700 used to operate the sensor 600. Further, thePage 47 of 27214528758vlPCB 700 may include additional components such as an ESP32 module 708 and an HV boost circuit 710.

[0172] In other embodiments, and as shown in Figure 23, the sensor 500 may include electrodes 712 and a microwave RF antenna 714 used to transmit signals. In some embodiments, the electrodes (e.g., the electrodes 712) may be stainless steel electrodes. In this regard, the sensor may combine three distinct physical measurements to characterize the concrete's quality in real-time. First, a Piezoelectric Stack Actuator drives mechanical vibrations into the mix to measure Mechanical Impedance; by analyzing how the concrete resists and dampens this energy, the system determines physical stiffness and workability (slump). Simultaneously, the ECI (Electrical Conductivity / Impedance) module uses flushmounted stainless steel electrodes (e.g., electrodes 712) to inject a low-voltage AC current, mapping the ionic connectivity of the paste to track chemical hydration and setting time. Finally, an embedded RF Antenna (e.g., RF Antenna 714) transmits microwave signals through a ceramic window to measure the dielectric constant, providing a precise, chemically independent reading of the absolute moisture content deep within the mix volume.

[0173] In some embodiments, the sensors 500 as discussed herein may include varying configurations and geometries based on where on the truck (e.g., vehicle 400) the sensor 500 is to be installed. In some embodiments, the truck 400 may include a hatch on the side of the drum 402 configured for access. In some embodiments, the hatch may be configured or reconfigured to be a “smart hatch" wherein the smart hatch includes a plurality of sensors 500 configured for analyzing the concrete within the drum 402. For example, as shown in Figure 11, a sensor 500 and / or 900 may be installed on the outside of the drum 402 (e.g., where the hatch and / or smart hatch is) while sensing components may physically interact with the material 406 inside the drum 402. In some embodiments, the sensor 500 or 900 may be installed in a hatch on the vehicle 400. In some embodiments, the hatch may provide an access point to the internal portion of the container (e.g., drum 402) of the vehicle. The hatch may be equipped with sensors 500 in order to provide access from the outside of the vehicle while providing interfacing opportunities between the sensing components and the material. In this regard, the sensor 500 may include a flush diaphragm sensor face 902 configured to be abrasion resistant to the material 406 within the drum 406. Further, the sensor 500 may include a hermetic seal 904 configured to prevent leakage of material 406 outside the truck. A load cell 906, which may include a strain gauge, may be configured to ingest data relating to the load of the material 406 as the drum rotates. Further, the sensor 500 may communicate via an integrated electronic wireless transmitter 908. The sensor 500Page 48 of 27214528758vlmay be housed in a rugged exterior housing 910 to prevent the vulnerable sensor components from degrading due to harsh environments. Further, a signal conditioner 912 may be used to prepare or pre-process the data captured by the sensor 500 for a processing at a later stage.

[0174] Further the sensor as shown in Figure 11 may act as a draft force flowmeter wherein instead of vibrating, a rigid "finger" or "paddle" protrudes into the moving concrete. As the drum 402 rotates, the viscous concrete 408 pushes against this paddle 904. In some embodiments, the internal load cell 906 is isolated behind a heavy-duty flexible membrane 904 (likely reinforced rubber or a steel bellows). The paddle "floats" on this membrane. In some embodiments, when stiff concrete hits the paddle, a force is applied to the paddle. This force is transferred through the membrane to the load cell 906 (strain gauge bridge) sitting safely in the dry housing. In some embodiments, a high force may indicate stiff and / or dry concrete with a high yield stress while a low force may indicate soup and / or wet concrete with a low yield stress.

[0175] Further, as shown in Figure 27, a sensor may be configured with a flexible flap (e.g., the flexible flap 2508) that measures a pressure or force applied to the sensor by the material. In this regard, viscosity and / or slump may be determined based on the pressure readings. In this regard, the sensor may include a coupling 2502 configured to attach to the vehicle 400 in various locations (e.g., the internal portion, the mixing blade, or the like). Further, an enclosed PCB 2504 may be configured to operate the sensor while a flexible protective casing 2512 is configured to protect the vulnerable sensing components and PCB components. The flap 2508 may be coupled to the housing 2512 via a hinge 2506. Further, in some embodiments, the sensor may be configured with a sliding shutter 2510 that can slide across the flap 2508 to remove accumulated material (e.g., crusted concrete). In this regard, the sliding shutter 2510 may be configured to be an anti-concrete crusting mechanism used to remove material build up from the sensor that may affect the sensor’s ability to determine material properties or the like.

[0176] Further, in some embodiments, and as shown in Figure 28, a sensor may be configured to couple to the vehicle 400 via a clamp 2602. A PCB 2604 may be configured to operate the sensor. The sensor may be housed in an injection molded casing 2608. The sensor may be configured with a sensor array in a tuning fork configuration 2606 wherein a gap is present between at least two longitudinal sensor arrays including a plurality of corresponding sensing components on each array. In this regard, the material may pass through the tuning fork 2606 while the sensor arrays are configured to transmit and / or receive signals from one another. For example, a transmitting sensor on one of the longitudinal sensor arrays may Page 49 of 27214528758vltransmit a signal that passes through material within the tuning fork gap 2606 and arrives at the receiving sensor on the other longitudinal sensor array. In this regard, the signal received may be used to determine material properties such as viscosity and / or predicted test outcomes such as slump test. In some embodiments, the sensor may use electromagnetic waves wherein the transmitting sensor emits the electromagnetic wave and the receiving sensor receives the electromagnetic waves after they have passed though the material within the tuning fork gap 2606.

[0177] Further, in some embodiments, and as shown in Figure 29, a sensor may be configured with a ring of sensors 2706, wherein the ring 2706 includes emitters and receivers. In this regard, material (e.g., concrete) may pass through the middle of the ring 2706. as shown by arrow 2708. In some embodiments, the ring of sensors 2706 may have arrangements of emitters and receivers such that the material is measured in any orientation or flow rate through the ring 2706. For example, the ring 2706 may still capture the data from the material flow even if the flow is not completely filling the ring 2706 with material. Further, the sensor may be operated via a PCB 2704 and may be coupled to the vehicle 400 via a bracket 2702.

[0178] Further, in some embodiments, and as show in Figure 30, a sensor may be configured with a sensor ring 2802 and an internal sensor 2808. In this regard, the sensor ring 2802 may emit a signal for the internal sensor 2808 to capture, or vice versa. For example, the sensor ring 2802 may include a plurality of LEDs and the internal sensor 2808 may be configured to detect reflection of the light emitted by the ring 2802. Further, in other embodiments, multispectral sensing techniques may be used which may include infrared light, visible light, UV light, and the like. In some embodiments, the sensor may use other spectroscopy based sensing methods or electromagnetic wave based sensing methods. In some embodiments, the sensor ring 2802 may be used for material illumination purposes.

[0179] In some embodiments, the sensing assembly comprises a piezoelectric element mechanically coupled to a geometry configured to interact with the material. The sensing assembly may be mounted in or on a wall, hatch, chute, or other structural element of the container. The sensing assembly may be: flush with an interior surface, recessed into an interior surface, partially protruding from an interior surface, mounted on an arm or rod extending into the material volume, or mounted in a replaceable or sacrificial insert. The sensing assembly may be mounted in a stationary or rotating reference frame. In rotating embodiments, the sensing assembly may rotate with the container.Page 50 of 27214528758vl

[0180] In some embodiments, the geometry' coupled to the piezoelectric element is configured to convert actuator motion into deformation of the material. The geometry may include, without limitation: planar or curved surfaces; textured, ribbed, or patterned surfaces; conical, wedge-shaped, or inclined surfaces; annular or circumferential features; vanes, fins, paddles, or protrusions; rocking, tilting, or flexural elements; combinations thereof. The geometry may be rigid or compliant and may include wear-resistant or protective coatings. The geometry defines a spatial mapping between actuator motion and induced deformation in the material. This mapping may be represented abstractly by a geometry-dependent operator relating actuator displacement or velocity to strain or strain-rate within the material.

[0181] In some embodiments, the geometry' includes one or more inclined surfaces oriented at a non-zero angle relative to the primary actuation axis of the piezoelectric element. Actuation of the piezoelectric element produces motion having a component normal to the inclined surface and a component tangential to the inclined surface, thereby inducing shear deformation in the adjacent material. Non-limiting examples include: a single inclined ramp or wedge surface; a V-shaped or chevron-shaped geometry comprising two opposing inclined surfaces; a pyramidal or multi-faceted geometry' comprising three or more inclined faces. Such geometries may be flush or slightty protruding and may be symmetric or asymmetric. Inclined surface geometries may be particularly suitable for converting axial piezo motion into lateral shear while maintaining mechanical simplicity and durability'.

[0182] In some embodiments, the geometry comprises a conical or frustoconical shape, optionally with a truncated tip. Actuation of the piezoelectric element produces normal motion along the cone axis, which is resolved into radial and circumferential components along the sloped surface of the cone. This produces shear deformation in the surrounding material, distributed circumferentially about the cone. Conical geometries may be advantageous in rotating containers, as they are less sensitive to flow direction and provide substantially isotropic shear coupling. The surface of the cone may be smooth or textured to increase shear engagement.

[0183] In some embodiments, the geometry is configured to rock, tilt, or flex in response to actuation of the piezoelectric element. Examples include: a plate mounted on a compliant hinge or flexural element; a domed or spherical cap supported on a compliant seat; a flexural arm or cantilever configured to rotate or twist about a pivot. Actuation of the piezoelectric element causes the geometry' to undergo angular displacement, resulting in alternating tangential motion at the interface with the material. Such geometries may generate strong shear deformation at relatively small actuator displacement amplitudes and may be Page 51 of 27214528758vlparticularly sensitive to amplitude-dependent nonlinear or yield-related behavior of the material. In some embodiments, rocking or flexural geometries are housed within a recessed pocket to protect against abrasion or impact while maintaining controlled shear engagement.

[0184] In some embodiments, the geometry includes an annular ridge, circumferential edge, or stepped shoulder. Normal motion of the piezoelectric element causes relative motion concentrated at the edge or ridge, producing high shear stress in a localized region of the material adjacent to the ridge. The ridge may be rounded, chamfered, or sharp, depending on durability requirements. Annular geometries may be used to concentrate shear while remaining substantially flush with the surrounding surface and may be combined with other geometries, such as central flat regions or textured faces.

[0185] In some embodiments, the sensor types may include a load cell, a piezoelectric sensor, a tuning fork-like sensor with a speaker or a motor, a wheatstone bridge, RF sensors with calibrated range and gain, an IR camera, an RGB camera, a lidar 3D scanning and / or mapping device, a sweep frequency from a transmission device to a receiver device while logging RSSI to map hydration (e.g., via voltage controlled oscillator to radio amplifier and monitoring the RSSI on the receiver device), inductive and / or capacitive measure of humidity to determine concrete hydration levels.

[0186] In some embodiments, sensors 500 may be enclosed in a short case to be installed in the inside of the truck, on a wall in between stirring blades, by using attachment magnets. The device will possibly be covered in concrete after the first usage and may not have a direct transparent aperture on the fresh concrete, hence will need to be equipped with “blind’’ sensors, able to measure the fresh concrete above the old dried one. The device may be equipped with mechanical probe / probes, sticking perpendicularly to the device body and measuring deflection and other parameters in order to indirectly measure viscosity’, workability' and temperature of the concrete.

[0187] In some embodiments, sensors 500 may be enclosed in a case suspended in the center of the drum. In some embodiments, the device will be suspended by means of legs arranged radially in many directions, free to move inside the drum but creating space between the center of the star (where the active part of the device is) and the walls / blades of the drum. In some embodiments, the device will be suspended through a wire anchored to two opposite walls of the drum. The wire may be anchored in fixed positions and may be attached to 2 magnets. Both these embodiments will be less subject to accumulated material which may accommodate sensors that need visual access to the wet concrete. One strategy would be toPage 52 of 27214528758vlrotate the device in order to keep it always facing down. In both cases the legs or the anchorage tethers might double as sensors, measuring workability through their deflection.

[0188] In some embodiments, the sensors 500 may be powered via a variety of methods. For example, internal batteries, external batteries, energy harvesting techniques, power couplings to the truck, and the like may be used. In this regard, power sources may be configured to be removed. In other embodiments, power sources may be able to be recharged. For example, configurations and / or modifications to the truck may be made for access to the power source, such as an opening in the truck that allows for access to the power source.

[0189] In some embodiments, sensors 500 may be attached to the internal blades of the drum through magnets and use them as a rails, moving along by means of wheels or similar gears. The goal of the movement will be to keep the embodiment always on the upper wall of the drum pointing down. This will prevent the fresh concrete (that tends to be on the floor and to drop from the top to the bottom) to obstruct the embodiment. This means that such embodiments could be equipped with visual inspection sensors, able to investigate the concrete characteristics at a short distance by means of cameras, mics and lidars.

[0190] In some embodiments, sensors 500 may be coupled to the mixing blades closes to the center of the drum. In some embodiments, the device would be around the clamp attachment, have a shape designed to facilitate the flow of the concrete around it and to avoid the concrete to stick. Although the device will be in contact with concrete it reasonably may not be left submerged by it. This will help with recovery, visual inspection and data exchange. In some embodiments, the device would be suspended aw ay from the clamp and thus elevated from it by means of a rod. The rod would act as a seal, capturing the shearing force of the concrete passing. The inclination of the rod, with the device at its extremity (acting also as a mass) will give real time information about the shear and would be measurable by the relative position of the device in space, measurable by means of inertial sensors. This embodiment could also accommodate visual inspection sensors pointing down at all times to capture images of the concrete w hile staying reasonably clean.

[0191] In some embodiments, the sensors 500 may be attached to the drum motor (should be pneumatic, powered by the truck engine) sensing vibrations and determining characteristics of the drum revolution, motor rpm, speed and torque. It could be mounted through magnets, be quite small and battery / energy harvesting powdered. Correlating the information of the motor operations (i.e. rpm, speed and torque provided) with the ones from the drum (i.e. revolutions, load displacement) various info about the concrete level, mass distribution, viscosity and total weight could be calculated.Page 53 of 27214528758vl

[0192] In some embodiments, the sensors 500 may be attached to the outside of the drum with magnets and spinning with it. The device would be small and adherent to the drum, avoiding getting caught while spinning. It could be powered by batteries or energy harvesting devices (solar, motion). It could sense the characteristics of the drum revolution but indirectly also motor rpm, speed and torque. It could also sense the inside of the drum from the outside, using for example acoustic sensors to determine the volume occupied by the concrete, its level and mass distribution and its viscosity etc. The device could also actively stimulate the drum through sounds, vibrations or radio waves studying the response to such stimulation (i.e. frequency / phase shifts) modulated by the physical characteristics of the drum and of its content.

[0193] In some embodiments, sensors 500 may include devices measuring the forces applied to the truck, vertical load, forces in all directions. They could be deployed underneath the truck measuring the distance to ground at four extremities measuring loads and forces applied to the vehicle by the drum, by measuring its relative inclination and its vibrations. They’re outside, hence can be equipped with wireless radios, streaming information directly to the cloud while the truck moves. Typically the distance from the ground will be easily measured when the truck is still at location: during the loading operations at the beginning of a trip and at the end of a trip during the unloading operations. While concrete is poured the vehicle gets closer to the ground and when the drum starts to spin the waves of the material inside will apply forces to all directions causing the center of balance of the vehicle to change and the distance from the ground of different points to change accordingly.

[0194] In some embodiments, the sensors 500 may include devices on the inside of the hatch, but with the possibility of getting cleaned and inspected after each load transportation. Being easily accessible after each operation it offers flexibility in terms of small batteries and possibility to retrieve data and to clean the sensor. Assuming the hatch may be opened after each trip, the idea is to introduce the sensor in the drum before a trip, let it record what happens during it and retrieve it (cleaning it, getting data, recharge batteries) at the end of the trip to acquire information about that specific trip.

[0195] In some embodiments, the sensors 500 may include camera, lidar, mic hanging outside the drum, close to the aperture, pointing inside. To be moved away when pouring. It looks inside the drum while it spins and records what happens during each truck trip. At the beginning of the trip, after loading, its mounting bracket is placed in front of the pouring hole. At the end of the trip, before pouring the concrete out of the drum, the bracket it’s moved away in a resting position away from the hole, not interfering with the pouring Page 54 of 27214528758vloperation. It’s outside, hence can be equipped with wireless radios, streaming information directly to the cloud while the truck moves.

[0196] In some embodiments, the sensors 500 may be configured to transmit data in a variety7of methods. In some embodiments, other data communication devices may7be available for the sensors 500 to communicatively couple with to transmit data to the system 100. For example, as show in Figure 17, the truck 400 may be equipped with additional communication devices 1502 to transmit data to the network 104. In this regard, data may be transmitted from the sensors 500 to the system 100. In some embodiments, the communication equipment may include directed antennas, beacons, gateways, acoustic communications, hydrophones, and the like configured to transmit data.

[0197] In some embodiments, the sensing assembly is mechanically attached to a vehicle-mounted container, such as a rotating drum of a concrete delivery vehicle, using one or more attachment mechanisms selected to maintain a desired positional relationship between the sensing assembly and the container, the material within the container, or both. In some embodiments, the sensing assembly rotates with the container. In other embodiments, the sensing assembly remains stationary relative to the vehicle frame. In further embodiments, one or more components of the sensing assembly are mounted such that rotation of the container induces relative motion between the container and the sensing assembly while a sensing element or sub-assembly maintains a defined orientation or constrained range of motion.

[0198] The attachment mechanisms described herein may be implemented independently, or may be combined in any technically compatible manner.

[0199] In some embodiments, the sensing assembly is attached to a surface of the container using one or more magnetic elements. Magnetic attachment may be implemented on an interior surface of the container, on an exterior surface of the container, or both. In some embodiments, the sensing assembly includes a magnetic base configured to releasably secure the sensing assembly to a metallic surface of the container. Additionally or alternatively, the sensing assembly may be removably inserted into or coupled to a magnetically attached base or carrier.

[0200] In some embodiments, one or more contact surfaces of the sensing assembly are curved or contoured to conform to a curvature of the container wall, such that the sensing assembly sits flush or substantially flush against the container surface. Additionally or alternatively, one or more compliant layers, adhesive materials, frictional layers, orPage 55 of 27214528758vlelastomeric interfaces may be provided between the sensing assembly and the container surface to improve retention, reduce vibration, or accommodate surface irregularities.

[0201] In some embodiments, the sensing assembly is mounted on an exterior surface of the vehicle, such that it is located outside the container and not in direct contact with the material. The sensing assembly may be attached to an exterior surface of the vehicle body, chassis, frame, support structure, or additionally or alternatively to an exterior surface of the container or drum. Attachment may be achieved using magnetic elements, adhesive materials, mechanical fasteners, clamps, bands, or combinations thereof, and the sensing assembly may include one or more curved or contoured contact surfaces configured to conform to a curvature of the vehicle or container surface so as to sit flush or substantially flush thereagainst. In such embodiments, the sensing assembly may rotate with the container, remain stationary relative to the vehicle frame, or exhibit constrained or partially decoupled motion depending on the attachment configuration. Measurements obtained by externally mounted sensing assemblies may be indicative of internal material behavior through indirect coupling mechanisms, including vibration, acoustic transmission, structural resonance, torque response, or dynamic deformation of the container or vehicle structure. In some embodiments, a plurality of sensing assemblies are distributed at different locations on the exterior of the vehicle and / or container, including at different circumferential, axial, or longitudinal positions, such that spatially distributed measurements are obtained and combined to infer material properties, mixing state, flow behavior, or operational conditions within the container.

[0202] In some embodiments, the sensing assembly is attached to an internal surface of the container, such that it rotates with the container and is exposed to material within the container. The sensing assembly may be positioned between internal mixing blades, attached directly to a blade or fin, or attached to a region of the drum wall between blades.

[0203] In some embodiments, the sensing assembly is attached to an internal blade or fin using magnets, clamps, fasteners, or combinations thereof. Additionally or alternatively, the sensing assembly may be shaped to align with or partially conform to the blade geometry', including blade-shaped, fin-shaped, or streamlined housings configured to promote material flow and reduce buildup. In some embodiments, the sensing assembly includes one or more pockets, recesses, cavities, apertures, or flow-through openings configured to allow material to enter, pass through, or flow relative to the sensing assembly during operation. Characteristics of material interaction with such features, including a rate of filling, rate of emptying, flow rate, residence time, pressure, drag force, or force required to displace Page 56 of 27214528758vlmaterial, may be measured directly or inferred and may be indicative of material properties or condition(s) such as viscosity’, yield stress, flowability’, setting state, or degree of mixing. Additionally or alternatively, deformation, vibration, or force experienced by the sensing assembly as material flows into, through, or past such features may be sensed and used to determine data indicative of material behavior or material condition(s).

[0204] In some embodiments, the sensing assembly is mechanically coupled to the material through a second material, rather than being in direct contact with the material. The second material may comprise a coating, liner, housing, blade, fin, wall section, protruding element, or other intermediate structure that interacts with the material. Signals transmitted through the second material, including mechanical, acoustic, vibrational, or electromagnetic signals, may be sensed and may be indicative of one or more properties of the material, while reducing direct exposure of the sensing assembly to abrasion, buildup, or chemical interaction.

[0205] In some embodiments, the sensing assembly is suspended within the interior of the container away from the drum wall. Suspension may be achieved using one or more tethers, cables, rods, legs, or supports attached to opposing walls, blades, or structural features of the container.

[0206] In some embodiments, a central sensing assembly is supported by a plurality of cables or tethers extending radially outward toward the container walls or blades. The cables may be rigid, flexible, or spring-biased. Additionally or alternatively, the cables themselves may function as sensing elements, with deflection, tension, or strain indicative of material shear, viscosity7, or flow behavior.

[0207] In some embodiments, the sensing assembly includes a central processing unit or “core” coupled to one or more distributed sensing elements located along cables, rods, or peripheral structures. The central unit may manage power, processing, and communication, while sensing elements are positioned to interact with the material at different locations.

[0208] In some embodiments, the sensing assembly is attached to a rotating portion of the container and rotates with the drum. In other embodiments, the sensing assembly is attached to a non-rotating portion of the vehicle, such as a frame, support structure, or rear mounting bracket, and remains stationary while the drum rotates.

[0209] In some embodiments, a stationary sensing assembly is configured to sense properties of the rotating container or the material within the container indirectly, for example by measuring vibration, acoustic response, torque, load distribution, or dynamic forces transmitted through the vehicle structure. In some embodiments, the sensing assembly is Page 57 of 27214528758vlattached to a motor, gearbox, or drive mechanism associated with the container rotation, and sensor data indicative of rotational behavior is used to infer properties of the material.

[0210] In some embodiments, the sensing assembly is mounted on, through, or adjacent to a hatch, access panel, or aperture of the container. The sensing assembly may be mounted on an interior surface of a removable hatch, on an exterior surface thereof, or partially through the hatch.

[0211] In some embodiments, a portion of the sensing assembly, such as an antenna, window, probe, or coupling element, passes through the container wall or hatch, while other components remain inside or outside the container. Additionally or alternatively, such portion may pass through the container hatch via a feed-through, flange, or sealed interface. Such configurations may reduce wireless signal attenuation, improve communication, or facilitate maintenance and cleaning.

[0212] In some embodiments, the sensing assembly is configured to move relative to the container during operation. For example, the sensing assembly may travel along internal blades or rails, move circumferentially with constrained motion, or be guided to remain near a particular region of the container, such as an upper portion of the drum. In some embodiments, relative or guided motion of the sensing assembly is utilized during installation. Where direct human access to an interior region of the container is limited, the sensing assembly may be positioned near an opening and guided along internal blades, fins, or structural features into a desired operational position as the container is rotated, thereby enabling installation at locations not directly reachable during manual installation. In some embodiments, the sensing assembly includes wheels, sliders, or guide elements configured to cooperate with internal structures of the container. Movement may be passive, driven by¬ gravity or material flow, or actively controlled (for example, using a motor).

[0213] In some embodiments, the attachment mechanism defines a mechanical coupling that governs relative displacement and motion of the sensing assembly7with respect to the container in response to forces or motion originating from the container or the material.

[0214] In some embodiments, relative displacement of the sensing assembly causes deformation, shear, flow, or displacement of the material, wherein such material deformation is determined at least in part by one or more external surfaces of the sensing assembly, or a portion thereof, that define a geometry engaging the material within the container.

[0215] In some embodiments, attachment mechanisms are selected to provide rigid mechanical coupling, such that forces or vibrations experienced by the container or internal structures are transmitted substantially directly to the sensing assembly. Such configurations Page 58 of 27214528758vlmay be used where measurements are indicative of shear, torque, vibration spectra, resonance behavior, or dynamic loading.

[0216] Additionally or alternatively, attachment mechanisms include compliant, damping, or isolating elements, such as elastomeric layers, spring elements, viscoelastic materials, or constrained interfaces, configured to attenuate or filter selected frequency components or transient mechanical disturbances. Such configurations may reduce sensitivity to vehicle-induced vibration while preserving sensitivity to material -induced forces or deformations.

[0217] In some embodiments, the attachment mechanism defines a directional sensitivity, such that the sensing assembly preferentially couples to forces, motions, or deformations along one or more axes. Directional coupling may be achieved through asymmetric mounting, blade-aligned attachment, curved contact surfaces, or constrained degrees of freedom.

[0218] In some embodiments, the attachment mechanism permits relative motion between the sensing assembly and the container or material, while constraining motion along one or more axes. Relative motion may be indicative of material flow, shear, drag, or viscosity, and may be sensed directly or indirectly.

[0219] In some embodiments, the sensing assembly is mechanically coupled to both the container structure and the material, such that measurements are indicative of interactions between the material and the container, including slip, adhesion, impact, or flow-induced loading.

[0220] Any of the attachment mechanisms described herein may be selected independently of sensing modality, excitation method, power architecture, or communication strategy’. Attachment mechanisms may be combined, substituted, or reconfigured without departing from the scope of the disclosure.

[0221] In some embodiments, the sensing assembly includes one or more protruding elements configured to extend into the material within the container. The protruding element defines a sensing geometry that mechanically engages the material and establishes a controlled interaction volume between the sensing assembly and the material.

[0222] In some embodiments, the protruding element comprises a rod-like, bladelike, pin-like, or elongated geometry extending from a housing or mounting surface attached to the container wall, blade, fin, hatch, or other internal structure. The protruding element may be oriented radially, axially, tangentially, or at an oblique angle relative to the container rotation axis.Page 59 of 27214528758vl

[0223] The protruding element may be configured to experience shear, drag, bending, torsion, compression, or combinations thereof as the material moves relative to the container. Mechanical response of the protruding element may be indicative of material properties or material condition(s) such as viscosity, yield stress, flow behavior, or degree of setting.

[0224] In some embodiments, the protruding element includes or comprises a piezoelectric element configured to generate or respond to mechanical excitation. The piezoelectric element may be integrated within the protruding element, bonded thereto, or formed as a layered or composite structure.

[0225] In some embodiments, the sensor assembly includes one or more electrodes arranged along the protruding element. The electrodes may be positioned axially, circumferentially, radially, or in segmented arrangements, and may be used to apply excitation, measure response, or both. Additionally or alternatively, the protruding element may include two electrodes, three electrodes, or four electrodes. Additionally or alternatively, one of the electrodes may be a reference electrode. Additionally or alternatively, different electrode pairs may be used for different functions, including excitation, sensing, impedance measurement, energy harvesting, or combinations thereof. Multi-electrode configurations may enable spatially resolved sensing along the protruding element, differential measurements, or discrimination between material-induced and sensor-assembly structure-induced responses.

[0226] In some embodiments, the protruding element includes an electromagnetic radiator or antenna. For example, a rod-like protruding element may serve as a monopole, dipole, or electrically coupled antenna structure to transmit or receive one or more electromagnetic waves. In such embodiments, interaction between the material and an electromagnetic field associated with a protruding element that forms part of the sensing assembly alters electromagnetic boundary conditions of the protruding element. Such alteration of electromagnetic boundary conditions produces corresponding changes in electrical characteristics of the protruding element, which may be sensed, measured, or determined by the sensing assembly and used to determine one or more properties, states, or conditions of the material. In some embodiments, the protruding element functions as an antenna or radiator, and the electrical characteristics include one or more of impedance, resonant frequency, quality factor, matching condition, radiation efficiency, and / or detuning.

[0227] In some embodiments, the protruding element is configured to harvest energy from material motion, container rotation, vibration, or flow-induced loading. For example,Page 60 of 27214528758vlbending, oscillation, or strain of a piezoelectric protruding element may generate electrical energy

[0228] In some embodiments, the protruding element has a defined length, crosssection, surface texture, or compliance selected to control penetration depth, shear profile, or interaction strength with the material. The protruding element may include tapered, stepped, textured, or compliant regions. Additionally or alternatively, the protruding element may be retractable, flexible, or elastically deformable, such that effective penetration depth or coupling varies during operation.Inertial Sensing. Timing & Positioning

[0229] In some embodiments, the system comprises one or more inertial sensing elements configured to measure motion, orientation, and rotational state of a container, vehicle, and / or sensing assembly. Such inertial sensing elements may include one or more accelerometers, gyroscopes, inertial measurement units (IMUs), rotation sensors, encoders, tachometric sensors, or combinations thereof. The inertial sensing elements may be integrated into the sensing assembly, mounted on or coupled to the container or vehicle, or provided as separate sensors in communication with the system.

[0230] In some embodiments, inertial measurements are used to determine a rotation state of a rotating container or vehicle, including one or more of angular position, angular velocity, angular acceleration, rotation frequency, phase, periodicity, stability, or higher-order derivatives thereof. Angular position or phase may be inferred from periodic variation of gravity-referenced acceleration measured by one or more accelerometers, from direct angular velocity measurements obtained using one or more gyroscopes integrated over time, from rotation sensors providing direct angle or count information, or from combinations thereof. The system is not limited to a particular mechanism for determining rotation state.

[0231] In some embodiments, inertial measurements are transformed between reference frames, including one or more of a sensor-referenced frame, container-referenced frame, vehicle-referenced frame, and inertial or global reference frame. Such transformations enable decomposition of measured acceleration into gravity -related components and nongravity-related components, separation of normal and tangential components relative to a sensing geometry or container wall, and compensation for rotation-dependent effects including gravity loading, centrifugal effects, vibration, or transient motion. This enablesPage 61 of 27214528758vlresponse signals to be interpreted in a manner that distinguishes static gravitational loading, externally induced excitation, and actively applied excitation.

[0232] In some embodiments, inertial sensing is used to determine a spatial orientation or angular location of the sensing assembly relative to the container, vehicle, or gravity7vector, including identification of when the sensing assembly occupies a repeatable spatial location such as a lowest point, highest point, or predetermined angular reference within the container. In rotating containers having a known or estimated radius of rotation, angular position and angular velocity measurements may be combined to infer circumferential position, tangential speed, or effective radial location of the sensing assembly relative to the material, enabling association of measured responses with a particular angular location or effective material volume encountered during rotation.

[0233] In some embodiments, inertial sensing is used to synchronize excitation, measurement, or sampling with rotation of the container or vehicle. Excitation or measurement may be triggered when the sensing assembly reaches a predetermined angular position, angular range, phase of rotation, or rotation condition, or when rotation satisfies a specified criterion such as target angular velocity or acceleration. Inertial sensing may further be used to segment response data, select valid measurement intervals, or disambiguate excitation-induced responses from externally induced motion.

[0234] In some embodiments, one or more kinematic metrics are derived from inertial measurements, including angular position 0, angular velocity’ ® = d0 / dt, angular acceleration a = do / dt, angular jerk da / dt, rotation frequency f = CD / 2JC, rotational phase, tangential acceleration a t, radial (centripetal) acceleration a_r, and combinations thereof. Angular position 0 may be expressed relative to a gravity-aligned reference, a container-fixed datum, or a phase reference derived from inertial signals. Angular velocity co may be obtained directly from one or more gyroscopes or inferred from periodic components of acceleration, while angular acceleration a may be derived from time-varying angular velocity or from tangential acceleration measurements. Linear acceleration measured by one or more accelerometers may be decomposed into components including gravity -aligned acceleration, tangential acceleration a t « r a, radial acceleration a r » r m2. and non-rotational acceleration components. In some embodiments, inertial measurements are transformed between reference frames, including a sensor-referenced frame, a container-referenced rotating frame, and an inertial or gravity -referenced frame, using one or more coordinate transformations, rotation matrices, or fdtering operations. Such transformations enable separation of gravity-induced acceleration from rotation-induced and externally induced Page 62 of 27214528758vlacceleration, and enable interpretation of measured responses in a rotating reference frame associated with the container or sensing assembly. The derived kinematic metrics may be computed continuously, intermittently, or over defined time windows, and may be filtered, averaged, or statistically characterized to represent steady-state rotation, transient motion, or operational variability, and may be used to determine measurement timing, condition excitation timing, for response interpretation, and / or for material property or material condition determination as described herein.

[0235] In some embodiments, inertial measurements are used for event detection and operational state identification, including detection of start or end of mixing, changes in rotation speed, vehicle acceleration or braking, impact events, vibration, avalanching, material redistribution, transition to discharge, or changes in loading condition. Detected events may be used to condition material-state inference, normalize response features, select inference models, or associate response signals with a corresponding mode of operation, including mixing, transit, idle, or discharge modes.

[0236] In some embodiments, inertial sensing is combined with electromechanical, electromagnetic, electrochemical, or other sensing modalities described herein, such that material response measurements are interpreted in view of container motion, orientation, excitation conditions, and operating state. In such embodiments, inertial measurements provide a temporal reference used to synchronize, align, or associate excitation events, response signals, and derived features across one or more sensing modalities. Signals obtained from different sensors may be time-stamped, indexed, or phase-referenced using inertial measurements, a system clock, or both, such that measurements acquired during different excitation windows, angular positions, or operating conditions are coherently- related in time. Additionally or alternatively, inertial sensing provides a time reference and an orientation reference used to align, index, or associate response signals from one or more sensing modalities with a corresponding rotational phase, excitation interval, and kinematic or gravitational condition of the container or vehicle.

[0237] In some embodiments, the system further comprises a timing source configured to associate sensed responses, excitation events, inertial measurements, and derived material conditions with a timestamp. The timing source may comprise a clock, timer, synchronized system time, vehicle time reference, network-synchronized time, or global navigation satellite system (GNSS) time reference. In some embodiments, the system further comprises a positioning source configured to associate measurements with a location, including a GNSS receiver or other satellite-based or terrestrial positioning system. Time- Page 63 of 27214528758vland optionally location-associated data enable longitudinal tracking of material evolution, correlation with operational or environmental conditions, synchronization across multiple sensing assemblies or vehicles, and integration into material prediction, quality assurance, optimization, or record-keeping systems.

[0238] In some embodiments, the system determines a reference angular orientation of a rotating container, such as a lowest or gravity -aligned position, by analyzing inertial measurements over one or more revolutions, rather than relying solely on instantaneous or open-loop integration. For example, periodic components of acceleration attributable to gravity7may be extracted from one or more accelerometer signals over a single revolution to identify a phase, angle, or time at which a gravitational extremum occurs, corresponding to a bottom, top, or other gravity-defined orientation of the container. In further embodiments, the reference orientation is refined or validated by aggregating measurements across multiple revolutions, enabling suppression of integration drift, bias, and cumulative error associated with gyroscope integration, sensor offset, or transient inertial disturbances. By identifying a stable or repeatable phase reference corresponding to gravitational alignment, the system establishes a robust angular datum indicative of a true physical orientation of the container.

[0239] In some embodiments, the determined reference orientation is used as a phase-locked or drift-corrected angular datum for synchronizing excitation, sensing, or sampling events. The reference may be updated continuously or periodically using rolling or windowed analysis across successive revolutions, enabling correction of slow drift, bias accumulation, or transient disturbances. Such multi-revolution referencing allows excitation and measurement to be consistently aligned to a physical orientation of the container, even when rotational speed varies or when inertial measurements are subject to bias or integration error. This approach enables reliable rotation-referenced sensing without requiring high-precision absolute encoders and supports repeatable material interrogation under gravity-conditioned loading across extended operating intervals.Machine Learning Models & Data Aggregation

[0240] In some embodiments, one or more material models are used to process sensing data obtained during transport, handling, or processing of a material. For example, a material model, as described herein may include artificial intelligence model 208 as shown in Figure 4. As used herein, a material model refers to any algorithmic, statistical, deterministic, rule-based, or data-driven construct configured to receive one or more inputsPage 64 of 27214528758vlindicative of material composition, material properties, material condition, contextual conditions, or sensed responses, and to produce one or more outputs indicative of material behavior, material properties, material conditions, material state, or recommended actions. A material model may comprise, without limitation, a physics-based model, empirical model, regression model, probabilistic model, state-space model, lookup-table model, or a machinelearning model including one or more of a neural network, ensemble model, decision tree, Bayesian model, or hybrid thereof. The material model may operate in real time, near real time, or offline, and may be trained, calibrated, updated, or executed using data obtained from one or more sensing assemblies mounted on, in, or coupled to a vehicle-mounted container, to concrete pours, and / or to batching equipment. In this regard, and in some embodiments, the artificial intelligence model 208 may include the components and / or functionalities as described herein.

[0241] In some embodiments, the method comprises deploying a plurality of sensing assemblies across a plurality of containers, pours, vehicles, or transport units, including a plurality of trucks operating concurrently or sequentially. The method further comprises, for each sensing assembly, generating one or more response signals indicative of a material carried by a corresponding container, pour or vehicle, and deriving one or more material conditions therefrom. The method further comprises aggregating, storing, and processing response signals and / or derived material conditions obtained from the plurality of sensing assemblies, such that material conditions are determined, compared, or analyzed across multiple containers, pours, vehicles or deliveries. The sensing assemblies may operate independently, synchronously, or asynchronously, and may transmit data to, receive data from, or otherwise communicate with a centralized computing system, a distributed computing system, or combinations thereof.

[0242] In some embodiments, sensor measurements, derived material conditions, or other material properties (such as workability-related parameters) obtained from a plurality of containers, pours, deliveries, vehicles, or mix batches are stored, accessed, or processed in association with one another, where association comprises logical, computational, or contextual linkage rather than requiring explicit relational storage. Such association may be established by joint availability for analysis, comparison, inference, training, calibration, normalization, or optimization, including cases in which data from different pours, containers, deliveries or vehicles are processed together, referenced against one another, or used as part of a shared dataset or model input. Association as described herein does notPage 65 of 27214528758vlrequire explicit linking at the time of data storage and may arise implicitly through joint use of data in any computational, statistical, or model-based process.

[0243] In some embodiments, data from a plurality of sensing assemblies are provided as either inputs to or training data for one or more material models. The material models may be configured to: (i) determine predicted mix properties (such as expected workability evolution); (ii) determine improved mix design recipes; (iii) generate new mix design recipes; (iv) determine an adjustment to a mix design recipe; or (v) generate one or more recommendations to change mix design recipe based on predicted mix properties, optionally based on historical and contemporaneous sensing data obtained during transport or from historical concrete pours. Such use of sensing data across the value-chain enables material optimization without requiring manual testing or destructive sampling.

[0244] In some embodiments, the system obtains first data indicative of one or more material properties or material conditions from a first sensing assembly associated with a first container, pour, vehicle, delivery7, or mix batch, and obtains second data indicative of one or more material properties or material conditions from a second sensing assembly associated with a second container, pour, vehicle, delivery, or mix batch. The first data and second data may be generated at different times, from different material instances, and / or under different transport, handling, or environmental conditions.

[0245] In some embodiments, the system processes the first data and the second data using one or more models. In one embodiment, the first data and the second data are both used as training data for a machine learning model. In another embodiment, the first data is used as training data for a machine learning model and the second data is provided as an input to the trained model for inference. In another embodiment, the second data is used as training data for a machine learning model and the first data is provided as an input to the trained model for inference. In another embodiment, the first data and the second data are both provided as inputs to a model, wherein the model may comprise a machine learning model or a deterministic model. The first data and the second data may originate from the same sensing assembly, different sensing assemblies, the same vehicle, different vehicles, the same pour, different pours, the same material batch, or different material batches. Additionally7or alternatively, the model may be a material model.

[0246] In some embodiments, the model comprises one or more adjustable parameters, including weights, coefficients, kernels, basis functions, lookup tables, state variables, or equivalent functional representations. Additionally or alternatively, the system may update, refine, or adapt such parameters based on the first data, the second data, or both.Page 66 of 27214528758vlIn some embodiments, parameters are updated incrementally, batch-wise, or continuously as additional data are obtained. In machine learning embodiments, the first data and / or the second data may be used to update model weights or learned representations. In deterministic or physics-based embodiments, the first data and / or the second data may be used to update model parameters, calibration factors, thresholds, or functional mappings. Parameter updates may be performed in whole or in part based on either data set, such that information derived from one material instance influences subsequent inference, prediction, or determination performed for another material instance.

[0247] In some embodiments, one or more models are employed, including predictive models, classification models, optimization models, generative models, and hybrid physics-informed or data-driven models. Model inputs may include raw response signals from electromechanical, electromagnetic, electrochemical, inertial, or environmental sensors; derived features or parameters extracted therefrom; material identity or composition descriptors; and contextual condition variables associated with truck operation and environment. Model outputs may include inferred material conditions such as yield-related parameters, flow or slump indicators, workability evolution, composition-adjusted rheological properties, confidence measures, and recommended actions. Optimization or generative models may further output modified mix design parameters, material adjustment recommendations, or control targets for subsequent batches. The system is not limited to a particular model class, and may employ deterministic models, probabilistic models, machinelearning models, neural networks, ensemble models, Bayesian models, or combinations thereof, with model selection or orchestration dependent on data availability, sensing configuration, and inference objective.

[0248] In some embodiments, one or more material models receive and / or are trained using one or more input data obtained during handling, transport, or placement of material. The input data may comprise one or more of: (i) material identity or composition data, which may include (but is not limited to) mix design recipes, batch or delivery’ identifiers, target performance parameters, water-to-cement ratio, cement type, aggregate type or grading, admixture information, or historical mix performance data; (ii) sensor-derived material response data obtained from one or more sensing assemblies disposed within, on, or coupled to (a) a container carried by a vehicle, (b) a pour location or receiving structure, and / or (c) batching, mixing, or material handling equipment, the sensor-derived data including response signals or derived features indicative of one or more of yield behavior, flow behavior, stiffness, damping, recovery, permittivity, conductivity, impedance, resonance behavior.Page 67 of 27214528758vlcompressive strength, material composition or other material-related conditions or properties; (iii) operational, inertial, or excitation context data associated with a vehicle, container, batching system, or pour operation, including one or more of angular position, angular velocity, angular acceleration, rotation history, excitation timing or amplitude, vehicle motion, vibration, gravity-referenced orientation, or effective loading conditions; (iv) temporal or environmental data, including one or more of elapsed time since batching, measurement timestamps, temperature, hydration age. or operating state: and (v) spatial or positional data, including one or more of sensor location relative to a container, pour location, or batching system, angular location within a container, effective radius of rotation, vehicle locationjobsite location, or delivery stage. In such embodiments, the one or more material models may be configured to process the input data individually or in combination to infer material properties and / or material conditions, predict material behavior and / or properties, generate control or recipe-related outputs, or update model parameters. Additionally or alternatively, the one or more material models are configured to process first response data obtained from a first material instance and second response data obtained from a second material instance, wherein the first and second material instances correspond to different vehicles, pours, batching operations, deliveries, or different temporal states of a same material, and wherein the first and second response data are used for one or more of: (i) training the one or more material models, (ii) updating parameters or weights of the one or more material models, (iii) calibrating or conditioning the one or more material models, or (iv) generating one or more inference, prediction, control, or recipe-related outputs indicative of material behavior or material properties.

[0249] In some embodiments, the one or more material models generate one or more outputs, including without limitation one or more of: (i) inferred material properties or material conditions, including yield stress, yield point, yield range, slump, flowability, viscosity, stiffness, damping, workability, or time-dependent evolution thereof; (ii) predicted future material states or trajectories, including predicted changes in slump, yield stress, flow behavior, or workability as a function of time, transport conditions, handling history, or operating state; (iii) material classification, identification, or fingerprinting outputs indicative of material type, mix family, composition class, hydration state, or similarity to previously observed material instances; (iv) material composition-related estimates or indicators, including water content, free-to-bound water ratio, water-to-cement ratio, aggregate distribution indicators, or admixture effectiveness; (v) quality, compliance, or risk indicators, including likelihood of out-of-spec material behavior, probability of insufficient workability Page 68 of 27214528758vlat delivery or discharge, or probability of non-conformance with one or more specified performance criteria; (vi) control, actuation, or operational recommendations, including recommendations to adjust container rotation rate, excitation timing, mixing duration, discharge timing, handling procedures, or transport conditions; (vii) recipe-related outputs, including recommendations to modify an existing mix design, generate an alternative mix design, update one or more batching parameters, or apply recipe adjustments to subsequent batches sharing one or more raw materials: (viii) model-internal outputs, including updated model parameters, weights, state variables, confidence measures, uncertainty estimates, calibration parameters, or latent representations; and (ix) derived datasets or explanatory outputs, including feature attributions, sensitivity measures, attribution datasets, confidence bounds, intermediate representations, or data suitable for audit, explainability, validation, or downstream learning.

[0250] The outputs may relate to material carried by a current vehicle, material delivered to a pour, material carried by one or more other vehicles, or material to be produced in subsequent batches, such that sensing data obtained from one container or delivery’ informs material decisions across a broader production, transport, or placement system.

[0251] In some embodiments, the models produce outputs that include uncertainty, confidence, or probability, rather than single deterministic values. Such outputs may include confidence intervals, probability distributions, likelihood scores, or uncertainty bounds associated with inferred material conditions or recommended actions. Uncertainty may arise from sensor noise, incomplete sensing coverage, variability in material composition, or contextual disturbances during transport. The system may propagate uncertainty' through successive inference stages and may use uncertainty' thresholds to trigger additional sensing, conservative control actions, alternative model selection, or operator notification.

[0252] In some embodiments, the system generates explainability data associated with one or more inferred material conditions, predictions, or recommendations. Such explainability' data may comprise attribution datasets identifying contributions of specific sensor modalities, response features, contextual variables, or historical data to a given model output. Attribution may be generated using model-specific techniques, sensitivity analysis, probabilistic decomposition, feature importance measures, or causal inference methods. The explainability data may be stored alongside inferred material conditions and may be used for validation, audit, model refinement, regulatory reporting, or operator interpretation. In some embodiments, attribution datasets generated from truck-mounted sensing are further aggregated across vehicles or deliveries to identity- systematic drivers of material behavior.Page 69 of 27214528758vlenabling refinement of models and improved separation of composition-driven effects from context-driven effects.

[0253] In some embodiments, the system is configured to execute one or more inference, control, and feedback loops operating at different spatial, temporal, and operational scopes, each loop using response data obtained from one or more sensing assemblies and generating outputs that affect material handling, placement, or production.

[0254] In a first embodiment, first response data obtained from a sensing assembly during rotation or transit of material within a vehicle-mounted container are processed to infer one or more current material conditions, including conditions indicative of yield behavior, slump, flow, workability, or composition-related state. Based on the inferred material conditions, the system generates first outputs applicable to the same container and delivery, including one or more of commands, control signals, or recommendations to: (i) adjust container rotation rate, rotation duty cycle, or hold duration; (ii) initiate, suppress, or condition addition of water; (iii) initiate, suppress, or condition addition of one or more admixtures; or (iv) modify excitation, mixing, or transport parameters. Such actions are performed while the material remains within the same container and are based on sensing data obtained from that container.

[0255] In a second embodiment, second response data, comprising response data accumulated over a transport interval, plurality of rotations, or defined time window, are processed to predict material condition or material properties at a downstream stage, including discharge or placement. Based on the predicted material condition or material properties, the system generates second outputs indicative of one or more of: (i) suitability of the material for placement; (ii) timing of discharge or pour; (iii) need for corrective action prior to discharge; or (iv) acceptance or rejection of the material. Corrective actions may include, without limitation, controlled addition of water, controlled addition of one or more admixtures, additional mixing or rotation, or delayed discharge.

[0256] In a further embodiment, third response data obtained from a first vehicle are used to influence handling or preparation of material in a second vehicle, including a subsequent truck carrying the same mix design or material batch. In such embodiments, sensing data from one delivery inform adjustments applied to a next delivery, including adjustment of water addition, admixture dosage, rotation protocols, or transport parameters. In some embodiments, response data obtained from a plurality of trucks carrying the same mix design during a defined time period are aggregated and processed to determine mix-Page 70 of 27214528758vlspecific properties or conditions and to generate mix adjust outputs that adjust the mix recipe of a subsequent truck.

[0257] In further embodiments, response data obtained from a plurality of trucks and deliveries are aggregated and processed to infer properties of shared or related raw materials, including cement, aggregate, water sources, or admixtures, wherein related raw materials may comprise materials of a same class, grade, source category, supplier, geological origin, processing method, or functional equivalence. Based on such inference, the system generates outputs that modify: (i) mix recipes that include one or more shared or related raw materials; (ii) batching parameters at one or more batching plants; and / or (iii) production settings across a plurality of batching plants. In some embodiments, response data obtained from trucks associated with a first batching plant are used to update mix recipes or batching parameters at a second batching plant that uses similar or shared raw materials. In further embodiments, response data obtained across multiple mixes and multiple batching plants are used to inform future production across a fleet or network.

[0258] In some embodiments, response data obtained from one or more sensing assemblies are provided as inputs to a material model configured to predict material properties, material conditions, or material behavior, including predicted yield stress, slump, flow, or workability evolution, compressive strength or compressive strength evolution. Outputs of the material model are then provided as inputs to a recipe model configured to generate, modify’, or recommend one or more mix recipes or batching parameters. In such embodiments, sensing data obtained during transport are provided as inputs to a first material inference model, outputs of which are provided as inputs to one or more subsequent models, including recipe or batching models, such that material recipes for subsequent batches are modified based on model outputs derived from prior transport sensing data.

[0259] In such embodiments, response data obtained from a sensing assembly associated with a given delivery may be used simultaneously or sequentially in one or more of the foregoing control loops, such that sensing data influence: (i) control of the current container; (ii) downstream discharge or placement decisions; (iii) preparation of subsequent trucks carrying the same mix; (iv) modification of other mixes sharing raw materials; and / or (v) production at one or more batching plants. The resulting system implements a closed, multi-scope feedback architecture spanning transport, placement, and production, without requiring manual sampling or laboratory’ testing.

[0260] In some embodiments, the system measures material response at a plurality’ of times while the material is contained within the container, including during mixing, transit.Page 71 of 27214528758vlidle, and discharge phases. The measured responses are used to track temporal evolution of one or more material conditions, including slump, flowability, yield stress, or related rheological properties. In typical operation, such material conditions may evolve monotonically over time due to hydration, thixotropic rebuilding, or loss of workability during transit; however, non-monotonic changes may occur, for example due to addition of water, admixture, or other corrective intervention. In some embodiments, the system detects a material condition indicative of insufficient slump or flow and generates an output, recommendation, or control signal indicative of adding water or modifying material state, and subsequently measures a resulting change in material response to confirm or quantify7the effect of such addition.

[0261] In some embodiments, material conditions are evaluated relative to a reference state measured at or proximate to loading of the material into the container. One or more subsequent measurements obtained during transit or at delivery are compared to the reference state to determine a relative change in material condition, including a change in slump, flowability, yield-related parameter, or workability metric. The relative change may be expressed as a difference, ratio, normalized deviation, or rate of change with respect to elapsed time, distance traveled, or number of container revolutions. In some embodiments, the relative change between loading and delivery7is used to assess material degradation, compliance with specification, or the need for corrective action prior to discharge. In some embodiments, the system determines whether the relative change exceeds a threshold indicative of unacceptable loss of workability7, and generates an output, recommendation, or control action in response.

[0262] In some embodiments, a plurality of sensing assemblies are deployed within, on, or coupled to a same container, vehicle, or transport unit, including a plurality of sensing assemblies disposed at different angular positions, radial positions, axial positions, depths, or orientations relative to the container and the material. The plurality7of sensing assemblies may be of a same sensing ty pe or of different sensing types, and may differ in one or more of sensing modality, excitation modality7, geometry, interaction scale, coupling condition, or placement. The sensing assemblies may operate simultaneously, sequentially, or asynchronously.

[0263] In such embodiments, response data obtained from the plurality of sensing assemblies provide multiple measurements of material condition associated with different material volumes, orientations, loading states, or flow regimes within the container. The system is configured to process response data from the plurality of sensing assemblies Page 72 of 27214528758vlindividually or in combination, including by fusion, comparison, weighting, temporal alignment, or model-based aggregation, to infer one or more material conditions, material distributions, spatial gradients, heterogeneity, redistribution, or flow behavior within the container. In some embodiments, measurements from different sensing assemblies are associated with different angular positions, times, or operating conditions, enabling material condition determination under varying gravity, rotation, or excitation states.Electrochemistry

[0264] In some embodiments, the sensing assembly is configured to perform electrochemical sensing of cementitious material contained within a vehicle-mounted container, wherein an applied electrical stimulus and a resulting electrical response are governed by electrochemical transport, interfacial charge transfer, and polarization phenomena within the material. The electrochemical response is influenced by ionic concentration, moisture content, pore connectivity, aggregate surface properties, and hydration state, and is therefore indicative of, or related to, one or more material properties or material conditions.

[0265] In some embodiments, the sensing assembly applies an input electrical signal to the material via one or more electrodes, wherein the input signal is voltage-driven or current-driven. The input signal may comprise a static excitation, a pulsed excitation, or an oscillatory excitation. In some embodiments, the oscillatory excitation comprises an alternating voltage or an alternating current, and may include single-frequency, multifrequency, or multi-tone excitation, and may further comprise a frequency-swept excitation over one or more frequencies, frequency bands, or frequency ranges, thereby enabling frequency-dependent electrochemical impedance spectroscopy of the material.

[0266] The measured electrochemical response may comprise one or more of current magnitude, voltage magnitude, phase angle, impedance or admittance measured between two or more electrodes or with respect to a reference electrode, reference conductor, or container structure, including a real (resistive) component, an imaginary' (reactive) component, or a complex representation thereof, as well as polarization resistance, relaxation time constants, transient decay behavior, or non-linear response characteristics. Such responses may be indicative of bulk ionic conductivity, electrode-electrolyte interface behavior, diffusionlimited transport, or capacitive storage effects within the material.Page 73 of 27214528758vl

[0267] In some embodiments, electrochemical sensing is performed in dry-batched material prior to water addition, wherein measured responses are indicative of aggregate moisture content, adsorbed water films, ionic species present on aggregate surfaces, or preexisting pore moisture. Following water addition, temporal evolution of electrochemical response may be indicative of water distribution, dissolution of cementitious constituents, formation of pore solution, early hydration reactions, or progression toward a mixed and homogeneous state.

[0268] Electrochemical sensing may be performed repeatedly over time, across container rotations, or at predetermined angular positions, such that time-dependent or rotation-dependent changes in electrochemical response are tracked. Additionally or alternatively, electrochemical measurements may be correlated with mechanical excitation, shear conditions, or material flow to distinguish electrochemical changes arising from hydration versus those arising from mixing or segregation.

[0269] In some embodiments, electrode geometry, spacing, orientation, surface treatment, or protrusion depth constitutes an independent sensing dimension that affects the measured electrochemical response. Electrodes may be flush-mounted, embedded, or arranged along a protruding element extending into the material, and may additionally or alternatively be formed as elongated electrode strips extending along a circumferential direction of the container, along an axial direction, or along one or more internal blades or fins of the container.

[0270] In some embodiments, electrodes are arranged with a predetermined spacing, which may be uniform or non-uniform, fixed or variable. By way of non-limiting example, electrode spacing may fall within one or more ranges including approximately 1-5 cm, 5-10 cm, 10-25 cm, 25-50 cm, or 50-200 cm, depending on the material, sensing objective, and operational phase. Such spacing may define a characteristic length scale over which ionic transport, diffusion, or polarization effects are probed, thereby biasing the measured response toward bulk material properties or toward near-surface or interface-dominated phenomena. Electrochemical coupling may further vary with material motion, shear, or contact pressure.

[0271] In some embodiments, the applied alternating excitation for electrochemical impedance measurement or spectroscopy is selected to probe different physical domains of the material and electrode-material interaction. By way of non-limiting example, excitation may be applied over one or more frequency ranges including about 0.001-0.1 Hz, 0.1-10 Hz, 10-1,000 Hz, 1 kHz-100 kHz, and / or 100 kHz-10 MHz, and the measured response within each range may be indicative of different phenomena. In some embodiments, lower- Page 74 of 27214528758vlfrequency ranges (e.g., about 0.001-10 Hz) emphasize slower processes such as polarization, interfacial charge-transfer effects, diffusion-limited transport, and time-evolving hydration-related changes. In some embodiments, mid- frequency ranges (e.g., about 10-1,000 Hz) emphasize bulk ionic conduction and effective pore-solution connectivity and may be indicative of water content, water to cement ratio, moisture state, ionic mobility7, and continuity of conductive pathways. In some embodiments, higher-frequency ranges (e.g., about 1 kHz- 10 MHz) emphasize faster capacitive or dielectric contributions, contact coupling effects, and geometry-dependent field penetration, and may be indicative of interface condition(s), coupling stability, air-contact versus material-contact transitions, or near-surface versus bulk-dominated behavior.

[0272] In some embodiments, electrochemical sensing is performed externally to the container without direct placement of all electrodes within the material. A first electrode may be positioned on an exterior surface of the container or vehicle, and a second electrode may be positioned (i) on an exterior surface of the container or vehicle, or (ii) w ithin the container or otherwise electrically coupled to the material, such that an applied electrical stimulus and resulting electrical response are measured via an electrochemical measurement pathw ay that includes the material. The electrical stimulus may comprise a direct-current excitation, an alternating-current excitation, or a frequency-dependent excitation, and the measured response may include one or more of voltage, current, impedance, phase, or polarization behavior governed by electrochemical transport and interfacial phenomena within the material. In some embodiments, where both electrodes are external, coupling to the material is achieved through a container wall, liner, hatch, insert, or other intervening structure, including via capacitive coupling, such that frequency-dependent impedance behavior of the material contributes to the measured response.

[0273] In some embodiments, electrochemical or conductive measurements are further used to distinguish between periods in which the material is in electrical contact with one or more electrodes and periods in which the electrodes are exposed to air or a non-conductive environment as the container rotates. As the sensing assembly moves through different angular positions, intermittent contact between the material and the electrodes may produce corresponding changes in measured conductivity, impedance, or contact resistance. The duration, frequency, or angular extent of such contact events, and transitions between material-contact and air-contact states, may be indicative of macroscopic material distribution, spreading, or adhesion behavior within the container, which in turn may be used to determine material properties or conditions such as slump, flow', or workability.Page 75 of 27214528758vl

[0274] In some embodiments, electrochemical sensing provides impedance-based observables that are used to infer one or more rheological properties of the material, including slump, flowability, yield stress, viscosity, plastic viscosity, thixotropy, or time-dependent flow behavior. Changes in measured impedance, including impedance magnitude, phase, real or imaginary' components, frequency-dependent behavior, or derived spectral features, may arise from multiple underlying mechanisms and may therefore serve as a common sensing basis for rheological inference.

[0275] In some embodiments, changes in impedance are indicative of mix composition or mix identity7, including the presence and relative proportions of cementitious constituents, supplementary cementitious materials, admixtures, aggregate mineralogy, or pore solution chemistry. Such impedance signatures may be used to classify or identify a mix design, and inferred mix identity or compositional features may be provided as inputs to one or more rheological, yield, slump, or flow estimation models to improve robustness or calibration across different mixes.

[0276] In some embodiments, changes in impedance are indicative of material microstructure or flow-related state, including pore connectivity, ionic mobility, interparticle interactions, flocculation or dispersion state, or early hydration processes. Frequencydependent impedance behavior, including shifts in spectral slope, curvature, or peaks, may correlate with changes in yield behavior, apparent viscosity, thixotropic breakdown or rebuilding, slump loss, or transitions between more cohesive and more freely flowing material states.

[0277] In some embodiments, changes in impedance are indicative of water addition, free water content, or redistribution of pore solution, which directly influence rheological behavior. For example, water addition may produce characteristic changes in impedance magnitude, phase, or frequency response associated with increased pore solution continuity or dilution of ionic concentration. Such changes may be detected as step changes, transient responses, or evolving features in an impedance spectrum.

[0278] In one illustrative embodiment, impedance measurements obtained at one or more frequencies are processed to extract features including impedance magnitude, phase, real or imaginary components, spectral peaks, inflection points, or temporal evolution thereof, and such features are provided as inputs to a machine-learning or data-driven model configured to estimate one or more rheological properties, such as slump, yield stress, or flowability. In such embodiments, impedance-based electrochemical sensing providesPage 76 of 27214528758vlcomplementary' information that constrains and improves rheological inference, optionally in combination with mechanical sensing data.

[0279] In some embodiments, one or more temperature measurements are sampled by one or more temperature sensors associated with the sensing assembly. The sampled temperature measurements are used to account for temperature-dependent effects on electrochemical responses of the material, including effects on ionic mobility, conductivity, polarization behavior, or impedance. In some embodiments, electrochemical measurements are adjusted, normalized, weighted, or otherwise processed as a function of the sampled temperature measurements, or the temperature measurements are provided as additional inputs to a rheological inference model, including a machine-learning or data-driven model, to improve estimation of material properties including slump, flowability, yield stress, viscosity, or thixotropy under varying environmental or operational conditions.

[0280] In some embodiments, the sensing assembly samples electrochemical responses at a plurality7of spatially separated locations within the container using multiple electrodes, electrode pairs, or electrode spacings. Additionally or alternatively, the sensing assembly computes differences, ratios, gradients, or other comparative metrics between electrochemical responses obtained at different circumferential, axial, or radial locations. Such spatially distributed electrochemical measurements may be used to detect or quantify spatial non-uniformity in the material, including gradients or localized variations in moisture content, ionic concentration, pore connectivity, or material continuity7. Additionally or alternatively, spatial differences in impedance magnitude, phase, real or imaginary components, or spectral features are used to infer uneven mixing, aggregate segregation, localized free water, or preferential flow paths. The detected spatial non-uniformity may be used to assess material homogeneity, to determine readiness for discharge, or to adjust or refine estimation of rheological properties including slump, flowability, or yield behavior.

[0281] In some embodiments, the sensing assembly samples electrochemical responses over time and derives one or more temporal metrics indicative of material evolution. Additionally or alternatively, such metrics include time derivatives, rates of change, slopes, trends, or temporal patterns of impedance-based features, including impedance magnitude, phase, real or imaginary components, or spectral characteristics. In some embodiments, derived temporal metrics are used to detect or characterize dynamic material processes, including water addition, redistribution of pore solution, thixotropic breakdown or rebuilding, slump loss, or early hydration dynamics. Additionally or alternatively, such rate-based electrochemical observables are used directly, or are provided Page 77 of 27214528758vlas inputs to a rheological inference model, including a machine-learning or data-driven model, to estimate or update rheological properties including slump, flowability, yield stress, viscosity, or time-dependent rheological behavior.E&M Sensing

[0282] In some embodiments, the sensing assembly is configured to perform electromagnetic sensing of cementitious material contained within a vehicle-mounted container. In such embodiments, the sensing assembly generates, transmits, receives, and / or couples an electromagnetic field or signal into the material and measures a resulting electromagnetic response.

[0283] In some embodiments, the measured electromagnetic response includes one or more field-level observables, including one or more of signal amplitude, attenuation, phase shift, propagation delay, frequency content, frequency distribution, spectral features, polarization, reflection behavior, transmission behavior, resonance frequency shift, quality factor variation, impedance, complex impedance, coupling strength, and / or time-varying modulation, arising from interaction, directly or indirectly, between an electromagnetic field and / or wave that has interacted with the material.

[0284] Additionally or alternatively, the electromagnetic response is influenced by, and / or indicative of, one or more material properties including dielectric permittivity, electrical conductivity, loss characteristics, interfacial polarization effects, pore structure, moisture state, bound and free water distribution, aggregate composition, and / or frequencydependent dispersive behavior of the material.

[0285] In some embodiments, the electromagnetic response may vary as a function of material distribution, continuity, proximity, and / or motion within an effective material volume of interest V relative to the sensing assembly, where the volume V may correspond to a localized region proximate the sensing assembly, a regional portion of the container, or a substantially global volume of material within the container, depending on electromagnetic frequency, geometry, and coupling conditions. Changes in material flow rate, position, thickness, contact state, and / or presence of air gaps within or across the volume V may alter electromagnetic coupling conditions between the sensing assembly and the material and produce corresponding changes in measured response.

[0286] Additionally or alternatively, electromagnetic response(s) may be indicative of effective rheological behavior through their dependence on material continuity,Page 78 of 27214528758V1redistribution under applied and / or externally generated excitation, and interaction with gravity and / or rotation. In some embodiments, electromagnetic responses are measured as a function of container rotation, angular position, and / or gravitational orientation, such that periodic, asymmetric, or evolving variations in response are indicative of slump, flowability, material redistribution, and / or material avalanching within the container.

[0287] In some embodiments, electromagnetic sensing is performed using one or more conductive structures, electrodes, antennas, resonators, transmission lines, or waveguides, wherein the geometry, size, orientation, spacing, and placement of such structures define an electromagnetic interaction volume. In some embodiments, a rod-like or protruding element extending into the material functions as an electromagnetic probe or antenna, such that surrounding material alters electromagnetic boundary conditions of the structure and produces corresponding changes in measured response.

[0288] In some embodiments, the geometry and characteristic dimensions of conductive structures, antennas, resonators, transmission lines, waveguides, or protruding elements are selected relative to a desired electromagnetic interaction scale within the material, including global, regional, or local interrogation, as described elsewhere herein.

[0289] By way of example, characteristic dimensions including length, width, spacing, protrusion depth, or separation between conductive elements may be on the order of millimeters to tens of centimeters, including ranges of 0.01-0.1cm, 0.1-lcm, 1-5 cm, 5-10 cm, 10-25 cm, 25-50 cm, or 50-200 cm. depending on operating frequency, penetration depth, and sensing objective. Larger dimensions and lower frequencies may be selected to interrogate bulk material behavior, while smaller dimensions and higher frequencies may be selected to interrogate localized material regions proximate the sensing assembly.

[0290] In some embodiments, electromagnetic sensing is performed using one or more radio-frequency or microwave frequency regimes by actively exciting the material with an electromagnetic signal and / or passively or actively coupling an electromagnetic field or wave into the material. RF operation may provide larger interaction volumes and greater penetration depth, and may be sensitive to bulk conductivity, overall continuity, and large-scale material redistribution. Microwave operation may provide greater sensitivity to local dielectric contrast, bound versus free water, pore-scale structure, and interfacial phenomena, and may therefore be advantageous for localized sensing of yield onset, flow behavior, water content, or hydration-related changes.

[0291] In some embodiments, RF sensing is performed in one or more sub-bands including ultra-low frequency (ULF, <3 kHz), very-low frequency (VLF, 3-30 kHz), low Page 79 of 27214528758vlfrequency (LF, 30-300 kHz), medium frequency (MF, 300 kHz-3 MHz), high frequency (HF, 3-30 MHz), very high frequency (VHF, 30-300 MHz), and ultra-high frequency (UHF, 300 MHz-1 GHz) ranges.

[0292] Lower-frequency bands may be more sensitive to bulk material continuity, moisture redistribution, and large-scale flow or slumping, while higher-frequency RF bands may be more sensitive to aggregate distribution, moisture gradients, and near-surface effects. In some embodiments, RF frequencies are selected such that an effective wavelength within the material is smaller than, comparable to, or larger than characteristic aggregate dimensions. When the effective wavelength is smaller than aggregate dimensions, the electromagnetic response may be sensitive to aggregate properties, interfacial effects, or local heterogeneity. When the effective wavelength is comparable to or larger than aggregate dimensions, the electromagnetic response may be less influenced by individual aggregates and more representative of bulk material behavior, including paste continuity, moisture distribution, or large-scale material redistribution during mixing, transit, or discharge.

[0293] In some embodiments, microwave sensing is performed at frequencies including 1-3 GHz, 3-10 GHz, 10-30 GHz, or higher, wherein shorter wavelengths enable localized electromagnetic interaction with material regions proximate the sensing assembly. In such embodiments, microwave sensing may be sensitive to dielectric changes associated with bound water, pore structure, interfacial polarization, or localized yielding, and antenna or resonator dimensions may be selected accordingly to control spatial sensitivity.

[0294] In some embodiments, the sensing assembly comprises one or more electromagnetic structures having a characteristic dimension selected as a function of an electromagnetic wavelength associated with an operating frequency of the sensing assembly. The characteristic dimension may comprise one or more of an antenna length, resonator dimension, electrode spacing, protrusion depth, transmission-line length, or separation between conductive elements.

[0295] In some embodiments, the characteristic dimension is selected to be proportional to the wavelength, including approximately a fractional wavelength, a whole wavelength, or an integer multiple thereof, including X / 8, X / 4, X / 2, X, or m X / n, where m and n are integers. Selection of wavelength-scaled dimensions configures electromagnetic resonance behavior, impedance characteristics, field distribution, and coupling strength, thereby defining a spatial extent and sensitivity of electromagnetic interaction with the material.Page 80 of 27214528758vl

[0296] In some embodiments, the wavelength is determined as an effective wavelength within or proximate the material, based on dielectric properties of the material and its local environment. In such embodiments, the sensing assembly is configured using an effective wavelength X eff that accounts for relative permittivity and, in some cases, loss of the material, such that k eff is shorter than a corresponding free-space wavelength. In an illustrative embodiment, X_eff = Z_0 / sqrt(e_r,eff), where / ._() is a free-space wavelength associated with an operating frequency and e_r,eff is an effective relative permittivity of the material within the electromagnetic interaction volume. Additionally or alternatively, the effective propagation constant may be represented as k_eff = (27t / _0) sqrt(s_r,eff -j-a_r,loss), or equivalently via a complex permittivity 8_eff = s' - j s", such that phase velocity, attenuation, and wavelength are functions of s' and s". This configuration enables electromagnetic interaction to be tailored to material length scales of interest, including length scales smaller than, comparable to, or larger than characteristic aggregate dimensions, pore structure, or flow features.

[0297] In some embodiments, the sensing assembly comprises a plurality of electromagnetic structures having different characteristic dimensions and / or operating frequencies, such that electromagnetic interaction occurs at multiple spatial scales within the material. Responses associated with different wavelengths, frequencies, or interaction volumes may be compared, combined, or selectively weighted to distinguish global material behavior from localized material phenomena.

[0298] In some embodiments, the sensing assembly is configured to modify one or more electromagnetic interaction parameters in response to changes in material properties. Such parameters may include one or more of operating frequency, excitation bandwidth, waveform, transmission-line length, resonator geometry, matching network configuration, tuning element state, or antenna configuration. In such embodiments, changes in material dielectric properties — such as changes arising from water redistribution, hydration, or material evolution — modify electromagnetic coupling conditions, and the sensing assembly adapts one or more parameters to maintain a desired interaction scale, coupling strength, or sensitivity over time.

[0299] In some embodiments, the method comprises measuring a change in an electromagnetic response of an electromagnetic structure coupled to the material and using the measured change as a sensing signal indicative of a material condition. The electromagnetic structure may comprise one or more of an antenna, resonator, transmission line, electrode structure, or coupled conductive element.Page 81 of 27214528758vl

[0300] In such embodiments, the measured electromagnetic response includes one or more of a resonant frequency, impedance, reflection coefficient, transmission coefficient, quality factor, phase response, matching condition, or combinations thereof, and the method comprises detecting detuning of the electromagnetic structure relative to a reference state. Additionally or alternatively, the detected detuning is indicative of a change in dielectric permittivity, moisture state, free-to-bound water ratio, water to cement ratio, hydration state, or compositional evolution of the material.

[0301] In some embodiments, one or more characteristics of the detuning, including magnitude, direction, temporal rate, hysteresis, or frequency dependence, are evaluated and used to determine a material condition or a change in material condition over time. The detuning-derived signal may be used alone or combined with one or more additional measurements, including electromagnetic, mechanical, electromechanical, electrical, or electrochemical measurements, to infer material properties or material state.

[0302] In a further embodiment, electromagnetic sensing is performed at terahertz (THz) frequencies, wherein wavelengths on the order of tens to hundreds of micrometers enable highly localized interaction with near-surface material structure, bound water dynamics, or early-stage hydration products. Such THz sensing may be used for localized probing rather than bulk penetration, and may be implemented using pulsed or continuous-wave THz sources and detectors integrated into or coupled to the sensing assembly.

[0303] In still further embodiments, electromagnetic sensing is performed at infrared, visible, ultraviolet, or higher frequencies, including passive or active illumination and detection. Such embodiments may be particularly suited to imaging-based sensing, observation of exposed material during discharge, detection of thermal emission or hydration heat, or characterization of surface moisture, flow behavior, or segregation, and are described in greater detail in imaging-related embodiments elsewhere herein.

[0304] Additionally or alternatively, electromagnetic sensing as described herein is performed using time-varying measurements of electromagnetic response caused by physical movement, deformation, or redistribution of the material relative to the sensing assembly. Such time variation arises when the material moves, slumps, yields, flows, or rearranges under gravity, rotation, excitation, or a combination thereof.

[0305] In some embodiments, as material adjacent to the sensing assembly deforms or flows, the electromagnetic coupling between the sensing assembly and the material changes over time. This produces measurable temporal changes in electromagnetic response, including changes in amplitude, phase, impedance, resonance frequency, coupling strength.Page 82 of 27214528758vlreflection, transmission, or loss. For example, as material begins to yield or flow, intermittent contact, changing thickness, air gaps, or relative motion within the sensing volume produce fluctuations or non-steady behavior in the measured electromagnetic signal.

[0306] In such embodiments, features derived from the time evolution of the electromagnetic response are used as indicators of material condition. These indicators may correspond to onset of yielding, degree of flow, transition between static and flowing regimes, or changes in workability, and may be used alone or in combination with electromechanical or electrochemical measurements to determine material conditions.

[0307] In some embodiments, electromagnetic sensing is performed at one or more discrete frequencies, across multiple frequency bands, or using swept-frequency, multi-tone, or broadband excitation. Frequency-dependent electromagnetic response may be indicative of different physical phenomena, including bulk dielectric behavior at lower frequencies, interfacial polarization at intermediate frequencies, and dipolar relaxation or bound-water effects at higher frequencies.

[0308] In such embodiments, material state is inferred from changes in the frequencydependent structure of the electromagnetic response rather than from a single absolute measurement, which may include changes in dispersion, relaxation behavior, or coupling characteristics across frequency.

[0309] Electromagnetic sensing may be performed during mixing, transit, discharge, loading, or idle phases of vehicle operation and may be scheduled as a function of container rotation, angular position, elapsed time, detected system state, inferred material stability, or sensing objective.

[0310] Sensing parameters, including excitation frequency, power level, duty cycle, waveform, sampling interval, or antenna selection, may be adjusted dynamically based on material behavior, power availability, communication constraints, or operational phase.

[0311] In some embodiments, electromagnetic measurements are used to determine a water content, moisture state, or water-to-cement ratio of the material. Because free water and bound water contribute disproportionately to the dielectric response of cementitious materials, changes in measured electromagnetic response, such as shifts in resonant frequency, impedance magnitude, phase, attenuation, or reflection coefficient may be correlated with total water content or with a ratio of water to cement. In such embodiments, the determined water content or water-to-cement ratio may be used directly as an inferred material parameter or used indirectly to inform assessment of workability, yield stress, slump, flow, hydration state, or expected evolution of rheological properties.Page 83 of 27214528758vl

[0312] In some embodiments, the sensing assembly determines an effective electrical permittivity, complex permittivity, or dielectric constant of the material from one or more electromagnetic measurements that are influenced by interaction of an electromagnetic field with the material.

[0313] In some embodiments, the sensing assembly determines an effective electrical permittivity of the material using resonance-based electromagnetic measurements. In such embodiments, the material is electromagnetically coupled to a resonant structure, including one or more of an antenna, resonator, transmission line, cavity, or equivalent structure, such that changes in material permittivity alter an effective electrical length, boundary condition, or stored electromagnetic energy of the structure. Additionally or alternatively, the sensing assembly measures detuning of the resonant structure, including one or more of a shift in resonant frequency tyres, a change in bandwidth, a change in quality factor Q, or a change in phase response, and determines the effective permittivity. Additionally or alternatively, the effective permittivity is based on a relationship in which resonant frequency is inversely related to the square root of permittivity, including relationships of the form e_eff <x (tyref / f_res)A2, or equivalent relationships.

[0314] In some embodiments, the sensing assembly determines an effective electrical permittivity7of the material using propagation-based electromagnetic measurements. In such embodiments, an electromagnetic signal propagates through, along, or adjacent to the material, and the sensing assembly measures one or more of phase delay, group delay, time-of-flight, or accumulated phase shift over a known or inferred propagation distance. Additionally or alternatively, the effective permittivity' is determined from a measured phase velocity' or group velocity of the electromagnetic signal, including relationships of the form v = c / sqrt(e_eff), or equivalent relationships, wherein c is a reference propagation velocity. The determined permittivity may be real or complex and may be derived from one or more propagation measurements.

[0315] In some embodiments, the sensing assembly determines an effective electrical permittivity of the material using impedance-based electromagnetic measurements. In such embodiments, the material presents an electromagnetic impedance to a driven structure, including one or more of an antenna, electrode, transmission line, resonator, or probe. Additionally or alternatively, the sensing assembly measures one or more of input impedance, reflection coefficient, standing-wave ratio, or matching condition, and determines the effective permittivity from changes in the measured impedance response, including but notPage 84 of 27214528758vllimited to amplitude-, phase-, or frequency-dependent variations. The permittivity' may be inferred directly or indirectly from the impedance response.

[0316] In some embodiments, the sensing assembly determines an effective electrical permittivity of the material using transmission-based electromagnetic measurements. In such embodiments, an electromagnetic signal is transmitted through, across, or proximate the material, and the sensing assembly measures one or more of transmission magnitude, insertion loss, attenuation, dispersion, or spectral roll-off across one or more frequencies. Additionally or alternatively, the effective permittivity, including a loss component, is determined from frequency-dependent transmission behavior indicative of dielectric loss, polarization, or absorptive characteristics of the material, and may be derived from singlefrequency or multi -frequency measurements.

[0317] In some embodiments, the sensing assembly determines an effective electrical permittivity of the material from frequency-dependent electromagnetic response across a plurality7of frequencies or frequency bands. In such embodiments, the sensing assembly analyzes variation of one or more electromagnetic observables as a function of frequency, which may include one or more of the resonance structure, impedance, phase, attenuation, or propagation characteristics of the response. Additionally or alternatively, frequencydependent variations are used to infer permittivity' behavior associated with one or more phy sical mechanisms, including bulk dielectric response, interfacial polarization, or boundwater or dipolar relaxation, and the inferred permittivity may comprise a frequencydependent or complex permittivity.

[0318] The effective permittivity determined via any of these mechanisms may then be used as an intermediate or derived parameter from which material properties and / or material conditions are determined. In some embodiments, the effective permittivity is used to determine data indicative of free water content, bound-to-free water ratio, total water content, or water-to-cement ratio of the material. Such data are then provided as input to one or more material models configured to determine one or more workability' -related or yield-related parameters, including but not limited to yield stress, slump, flowability, viscosity, or rheological state. In this manner, electromagnetic sensing provides a composition-sensitive intermediate observable that conditions, constrains, or complements mechanical or electromechanical response measurements when inferring material behavior.

[0319] In some embodiments, electromagnetic responses measured across one or more frequencies, times, orientations, or excitation conditions are used to construct a material identifier, signature, or electromagnetic fingerprint. Such a fingerprint may comprise a vector Page 85 of 27214528758vlor set of features including frequency -dependent permittivity, loss tangent, resonance structure, temporal modulation, or detuning behavior characteristic of a particular material composition, mix design, or hydration state. The electromagnetic fingerprint may be provided as input to one or more models, including material classification models, material identification models, rheology7prediction models, or material optimization models to improve determination of yield stress, flow behavior, slump, or other rheological properties of the material. In such embodiments, electromagnetic sensing augments other sensing modalities disclosed herein by providing composition-sensitive context that improves robustness, generalization, and accuracy of material condition determination.

[0320] In some embodiments, the system performs macroscopic electromagnetic mapping of material contained within a container by transmitting electromagnetic signals from one or more spatially separated electromagnetic elements and measuring corresponding responses at one or more other electromagnetic elements. The electromagnetic elements may be distributed circumferentially, radially, axially, or in combinations thereof along the container, such that measured responses are indicative of electromagnetic interaction along multiple propagation paths through the material.

[0321] In such embodiments, the system acquires electromagnetic response data indicative of bulk material distribution, continuity, redistribution, stratification, slumping, pooling, segregation, or global flow patterns within the container. The measured data may include one or more of transmission loss, reflection magnitude, phase delay, time-of-flight, scattering behavior, coupling strength, or frequency-dependent response along each path. The measurements may be acquired during container rotation, during stationary periods, or during mixing, transit, or discharge.

[0322] In some embodiments, the system processes the electromagnetic response data to determine a spatially resolved or spatially aggregated representation of material state within the container, including an estimated distribution of effective permittivity, attenuation, or other electromagnetic property', without requiring formation of a high-resolution image. The determined representation may be obtained using tomographic reconstruction, inverse methods, model-based estimation, feature extraction, or statistical aggregation across multiple propagation paths and orientations.

[0323] The determined macroscopic electromagnetic representation is then used as an input to material-state inference methods disclosed elsewhere herein, including imagingbased, kinematic, or flow-based inference methods. In particular, global electromagnetic features indicative of material redistribution, free-surface evolution, asymmetry, or bulk Page 86 of 27214528758vlmotion are mapped to material conditions indicative of yield stress, slump, flowability, or rheological regime using the same or analogous models, correlations, or inference frameworks described for imaging devices that observe global material flow within the container.

[0324] Accordingly, the macroscopic electromagnetic mapping embodiments disclosed herein provide an alternative or complementary mechanism to optical or imagingbased sensing for observing global material flow behavior, and enable inference of workability-related material conditions using electromagnetic measurements in situations where optical access is limited, obscured, or unavailable. The electromagnetic mapping may be used alone or fused with imaging-derived, mechanical, or time-domain response features to improve robustness, spatial coverage, or confidence of material-state determination.

[0325] In some embodiments, electromagnetic sensing is configured to provide low-resolution, medium-resolution, or high-resolution spatial characterization of material distribution, flow, or deformation within the container. Spatial resolution may be selected based on sensing objective, antenna geometry, frequency content, bandwidth, and spatial separation of electromagnetic elements, and may range from coarse, bulk-scale mapping sufficient to infer global flow, slump, or yield-related behavior, to finer-grained mapping capable of resolving localized flow features, asymmetries, or redistribution within the container. In some embodiments, multiple electromagnetic transmit and / or receive elements are co-located within a single sensing assembly, such that spatial information is inferred from frequency diversity, polarization diversity, multi-path response, or temporal evolution of the electromagnetic signal. In other embodiments, electromagnetic elements are distributed across two or more sensing assemblies positioned at different locations, orientations, or radial positions relative to the container, enabling spatially resolved measurements based on differences in propagation path, coupling geometry, or relative response between assemblies. The sensing assemblies may be fixed or movable, and may be arranged circumferentially, axially, radially, or in combinations thereof. Spatially resolved electromagnetic responses obtained using co-located or distributed configurations may be used directly or in combination with mechanical, electromechanical, or imaging-based measurements to infer material conditions indicative of yield stress, flow behavior, slump, material redistribution, or other rheological properties.

[0326] In some embodiments, the system determines a vector field representative of movement of the material within the container as a function of angular position 0 and spatial location. The vector field comprises, for each of a plurality of resolvable material volumes Page 87 of 27214528758vldV, one or more vectors indicative of local material motion, deformation, or flow direction and magnitude. The vectors may be determined from electromagnetic, mechanical, electromechanical, or combined sensing responses measured at different angular positions, times, frequencies, or spatial locations, including during rotation of the container. In such embodiments, each resolvable volume dV is associated with a corresponding motion vector derived from changes in measured response attributable to material movement relative to the sensing assembly. The resulting vector field may represent instantaneous, time-averaged, or cycle-averaged material motion and may be used to infer global and local flow patterns, shear distribution, sloshing behavior, or redistribution of material within the container, including material conditions indicative of yield stress, slump, flow, or other rheological properties.

[0327] In some embodiments, the system determines a vector field representative of motion, deformation, or stress -related behavior of the material within a container for a plurality of resolvable material volumes dV distributed across angular positions, spatial locations, or depths within the container. The vector field may comprise one or more components including velocity, strain rate, shear rate, or projected directional components thereof, and may be expressed in a rotating reference frame, a non-rotating reference frame, or transformed between reference frames. Vector components may be directly measured, inferred, interpolated, or reconstructed from sparse, distributed, or partially observable sensing responses obtained from one or more sensing assemblies, whether co-located or spatially separated. The vector field may represent instantaneous behavior, time-averaged behavior, cycle-averaged behavior, or temporal evolution across one or more rotations, excitation cycles, or operating intervals. In some embodiments, changes in coherence, saturation, attenuation, or decorrelation of vector components across space or time are themselves indicative of yielding, flow onset, or transitions between static, deforming, and flowing material states. The determined vector field, or features derived therefrom, may be used to infer one or more material conditions including yield stress, yield-related parameters, flow behavior, slump, or other rheological properties of the material.Strain and Load-Based Sensing of Material Redistribution and Yield

[0328] In some embodiments, the sensing assembly comprises one or more strain gauges configured to measure strain induced in a container wall by redistribution, movement, yielding, or flow of material within a vehicle-mounted container. In one embodiment, a strain gauge is mounted on an outer surface of a rotating container, such as a concrete truck drum,Page 88 of 27214528758vland mechanically coupled to the container wall. As the container rotates and the material shifts, slumps, avalanches, or flows within the container, the spatial distribution of material mass changes, thereby inducing a corresponding strain distribution in the container wall. The strain gauge measures one or more strain components associated with this loading, and the measured strain signals are indicative of material redistribution, yielding, slump or flow behavior.

[0329] In such embodiments, the system analyzes strain or load response signals as a function of one or more of rotation angle theta, time t, excitation condition, or operating state, and derives response features including one or more of temporal variation, periodicity, phase offset, asymmetry, hysteresis, or cycle-to-cycle variability. These response features may be evaluated in the angular domain, time domain, or a combined time-angle representation. Changes in one or more of response amplitude, angular dependence, phase relationship, waveform morphology, or harmonic content across successive rotations or excitation conditions may be used to infer material conditions which may include onset of material yielding, degree of flow or mobilization, redistribution or avalanching dynamics, or resistance to motion within the container. Transitions in such response features may be indicative of changes between static, deforming, and flowing material states.

[0330] In some embodiments, the system comprises a plurality' of sensors configured to measure response signals indicative of material behavior within a container carrying a flowable or partially flowable material. The plurality of sensors may be disposed at a plurality of locations relative to the container and are configured to generate response data that are processed in association to determine one or more material conditions. The present disclosure is not limited to a particular placement of the sensors relative to the container, provided that the measured responses are influenced by interaction between the container, the material, and motion, loading, or excitation experienced during operation. In some embodiments, the sensors may measure strain, load, pressure, acceleration, vibration, or other structural or dynamic responses of the container wall that arise from redistribution, motion, yielding, or flow of the material within the container.

[0331] In some embodiments, the plurality of sensors are mounted on an outer surface of the container. Measurements from the plurality of exterior-mounted sensors may be processed to infer material mass distribution, avalanching behavior, resistance to motion, or transitions between static and flowing states, optionally as a function of rotation angle, time, or excitation condition.Page 89 of 27214528758vl

[0332] In some embodiments, the plurality of sensors are mounted on an inner surface of the container or extend into the material volume. In such embodiments, the sensors may be in direct or indirect contact with the material and may measure forces, pressures, strains, accelerations, or other responses arising from direct interaction with the material during rotation, excitation, or flow. Interior-mounted sensors may provide localized measurements representative of material behavior within one or more sensing volumes adj acent to the sensor locations. In some embodiments, the plurality of sensors comprise a combination of sensors mounted on an inner surface of the container and sensors mounted on an outer surface of the container. In such embodiments, response data obtained from interior-mounted sensors and exterior-mounted sensors are processed in association to infer material conditions by correlating internal material interaction measurements with external structural response measurements.

[0333] In some embodiments, response signals from the plurality of sensors may be reconciled, compared, weighted, or otherwise processed to derive material condition(s) indicative of yield stress, flow behavior, slump, workability, or redistribution dynamics. Additionally or alternatively, response signals obtained from the plurality of sensors are processed to determine spatially varying material condition(s) within the container, including by resolving differences in response as a function of sensor location, rotation angle, or time, thereby enabling determination of bulk flow behavior, asymmetric material loading, localized yielding, or redistribution of material across a plurality of effective material volumes within the container.

[0334] Additionally or alternatively, response signals obtained from one or more sensors are processed to determine data indicative of a material surface or a material volume within the container, based on spatial, angular, or temporal variation in the measured response.

[0335] In some embodiments, the determination of data indicative of the material surface or material volume is based on response signals obtained from a plurality of sensors having a known spatial relationship, including a known distance, separation, or relative placement between the sensors.

[0336] Additionally or alternatively, the determination of data indicative of the material surface or material volume is based on response signals obtained from a plurality of sensors whose relative positions are measured, inferred, or estimated during operation. Additionally or alternatively such measurements may be carried out by an inertial sensing unit on the sensing assembly. Additionally or alternatively, relative positioning of a plurality Page 90 of 27214528758vlof sensor assemblies on a container on a vehicle may be determined based on receiving or transmitting at least one electromagnetic wave from one sensing assembly to a second sensing assembly.

[0337] Additionally or alternatively, the response signals used to determine data indicative of the material surface or material volume are obtained at a plurality7of times, such that the determination is based on multiple temporally separated measurements rather than a single instantaneous measurement. In further embodiments, data indicative of the material surface and / or material volume are determined repeatedly at a plurality of times, such that changes in surface position, volume distribution, or bulk material configuration are tracked over time during one or more phases of transport, rotation, excitation, or discharge.

[0338] In some embodiments, the sensing assembly comprises one or more multi-axis strain gauges or multiple strain gauges oriented along different axes. Additionally or alternatively, a first strain gauge may be configured to sense strain in a direction that is orthogonal to a second strain gauge. In one embodiment, one strain gauge or sensing axis may be oriented substantially parallel to a circumferential direction of the container wall, while another is oriented substantially perpendicular thereto, such that strain is measured in a cylindrical or curvilinear coordinate system associated with the container geometry. Strain measurements obtained along different axes may be processed to determine, separate, or resolve loading components associated with one or more of tangential shear, normal pressure, bending, torsion, or combined deformation modes induced by material movement relative to the container.

[0339] In some embodiments, the sensing assembly comprises one or more forcesensitive elements, including one or more load cells and / or pressure sensors, configured to generate signals indicative of forces or pressures applied by the material to a container structure. Such force-sensitive elements may be mounted on an inner or outer surface of the container, on a hatch, access panel, chute, or other structural feature, or at an interface through which material loading is transmitted. The measured force or pressure signals may vary as a function of material redistribution, yielding, or flow during rotation, transit, or discharge, and may be processed, optionally in association with inertial or electromechanical measurements, to determine material conditions including yield-related behavior, flow behavior, or resistance to motion.

[0340] Strain gauges used in the embodiments described herein may include, without limitation, piezoelectric elements, piezoresistive elements, foil strain gauges, vibrating-wire strain gauges, capacitive strain sensors, or combinations thereof. Load cells may include.Page 91 of 27214528758vlwithout limitation, strain-based load cells, piezoelectric load cells, hydraulic load cells, pneumatic load cells, current-loop load cells, or other force-sensing elements. The present disclosure is not limited to a particular strain or load sensing technology.

[0341] In some embodiments, strain- or load-derived response signals are provided as inputs to one or more models to determine one or more material conditions, which may include yield stress, flow behavior, slump, workability, or redistribution dynamics. Such models may combine strain or load measurements with other sensing modalities described herein, including electromechanical, electromagnetic, electrochemical, or inertial sensing, to improve robustness and interpretability of inferred material conditions.

[0342] In some embodiments, the method comprises measuring one or more strain, load, or pressure response signals s(t) using one or more strain gauges, load cells, or pressure sensors coupled to a container carrying a material, and determining a rotational or kinematic state of the container including one or more of angular position theta(t), angular velocity dtheta / dt, or angular acceleration d2theta / dt2. The measured response signals may be represented as a function of angular position s(theta), as a joint function of time and angular position s(t, theta), or in another equivalent rotation-referenced representation obtained by mapping time-domain measurements into a rotating frame of reference. One or more response characteristics Phi may then be derived, additionally or alternatively, from the measured or transformed signals, where Phi may include one or more of peak magnitude, variance, harmonic content, phase offset, asymmetry, intermittency, hysteresis, or spectral energy, such that Phi may be expressed as a function of s(theta), dtheta / dt, and / or d2theta / dt2, without requiring evaluation of a specific functional form.

[0343] In some embodiments, the derived response characteristics may be interpreted in view of one or more material-related parameters M, additionally or alternatively including density rho, bulk mass m, fill fraction, mix identifier, mix design parameters, water content, or other composition-related properties, and in view of one or more geometric or contextual parameters G, additionally or alternatively including container radius R, gravity7g, rotation rate, effective contact area, or wall thickness. The response characteristics may be scaled, normalized, or otherwise transformed using one or more of such parameters, for example to obtain a scaled response Phi*, where Phi* may depend on Phi together with one or more of rho, m, R, g, M, or G, without requiring a specific physical model.

[0344] In some embodiments, one or more material conditions indicative of yield stress tau_y, yielding behavior, flow behavior, slump, or workability may be inferred by mapping the measured response signals s(t), the derived response characteristics Phi, and / or Page 92 of 27214528758vlthe scaled quantities Phi* to one or more yield-related or flow-related parameters using one or more models, correlations, rules, or learned mappings. Such mapping may be expressed, for example, as tau_y, flow, or slump being dependent on one or more of s, Phi, Phi*, M, and G, additionally or alternatively without requiring explicit calculation of stress or strain fields.

[0345] In such embodiments, changes in the response signals or derived characteristics as a function of angular position theta, time t, rotation rate, excitation, or operating condition may be indicative of redistribution, avalanching, yielding, or flow of the material within the container. The inferred material condition(s) may then be used to generate one or more outputs including material state determination, control actions applied to the container or vehicle, material addition or adjustment decisions, or material recipe-related outputs for the same or subsequent batches.Torque-. Energy7-, and Drive-System-Based Sensing Using Vehicle Components

[0346] In some embodiments, the system infers material conditions using one or more existing drive-system components of a vehicle or container as sensing elements, without requiring a dedicated material-contact sensor. In such embodiments, one or more signals indicative of torque, power, energy consumption, or drive effort associated with rotation of a container are measured, estimated, or derived from the operation of the vehicle or container system.

[0347] In some embodiments, a torque-related signal is obtained from a motor, drive train, hydraulic system, or control system associated with rotation of a container, including one or more of motor current, motor voltage, motor power, hydraulic pressure, hydraulic flow rate, inverter output, drive command signals, feedback signals, or combinations thereof. Such signals may be measured directly, estimated from control parameters, or obtained from vehicle telemetry, controller area network (CAN) data, or equivalent vehicle control interfaces. The measured or derived signal may be indicative of an instantaneous or time-averaged torque r(t) required to rotate the container.

[0348] Additionally or alternatively, one or more signals indicative of torque, power, energy consumption, or drive effort may be obtained using one or more sensors added to, retrofitted onto, or otherwise coupled to a vehicle, container, motor, drive train, or hydraulic system, rather than relying solely on signals provided by native vehicle instrumentation or control interfaces. Such added sensors may include one or more of torque sensors, current sensors, voltage sensors, power meters, pressure sensors, flow sensors, strain sensors, rotational speed sensors, or combinations thereof, mounted on or coupled to a motor, shaft.Page 93 of 27214528758vlgearbox, hydraulic line, pump, actuator, or structural component associated with container rotation. In such embodiments, the added sensors generate signals that are processed in the same manner as drive-system-derived or telemetry-derived signals described herein to determine torque-, power-, or energy-based quantities indicative of material resistance to motion.

[0349] In some embodiments, an inertial sensing unit is used in association with the torque-related signal to determine one or more kinematic parameters of rotation, including angular position 0(t), angular velocity a»(t) = dO / dt, angular acceleration dco / dt, rotation frequency, or rotation stability7. By combining torque-related measurements with kinematic measurements, the system determines one or more quantities indicative of rotational resistance or mechanical effort, including one or more of r(t). r(t) / o)(t). power P(t) = r(t)- o(t), energy per revolution, energy per unit time, or energy dissipated over a rotation interval.

[0350] In such embodiments, changes in required torque, power, or energy consumption for a given rotational speed, or changes in rotational speed for a given applied torque or drive command, are indicative of changes in internal resistance to rotation arising from material redistribution, yielding, or flow within the container. As the material transitions between static, deforming, and flowing states, the effective shear resistance imposed by the material on the container changes, resulting in measurable changes in drive effort.

[0351] In some embodiments, torque- or energy-based response signals are analyzed as a function of rotation angle 0. time t, rotation rate co, excitation condition, or operating state, and are mapped into a rotation-referenced representation. One or more response characteristics <D may be derived from such signals, including one or more of mean torque, peak torque, torque variance, harmonic content, asymmetry between angular sectors, cycle-to-cycle variability, hysteresis, or energy dissipation per revolution. Such characteristics may be evaluated over one or more revolutions or excitation intervals.

[0352] In some embodiments, the derived torque- or energy-based response characteristics are interpreted in view of one or more contextual or material-related parameters, including one or more of fill fraction, bulk mass, material density, container radius, gravity, rotation speed, or mix identifier. The response characteristics may be scaled, normalized, or compared across operating conditions without requiring explicit calculation of shear stress or rheological parameters.

[0353] In some embodiments, one or more material conditions indicative of yield stress, yielding behavior, flow behavior, slump, workability, or resistance to motion are inferred by mapping the torque- or energy-based response characteristics, alone or in Page 94 of 27214528758vlcombination with inertial measurements, to one or more material condition parameters using rules, correlations, models, or learned mappings. The inference may be local to a single truck, or aggregated across multiple trucks or deliveries.

[0354] In some embodiments, the system relates measured or derived drive-system quantities to effective shear interaction between the container and the material. In such embodiments, mechanical power associated with container rotation may be expressed as: P(t) = tau(t) * omega(t) where tau(t) is a torque-related signal associated with the drive system and omega(t) is angular velocity of the container. The power P(t) represents the rate at which mechanical energy’ is transferred to and dissipated within the combined container-material system during rotation.

[0355] In some embodiments, changes in P(t), tau(t), or energy’ dissipated per revolution are indicative of changes in effective shear resistance imposed by the material. As the material transitions between static, deforming, and flowing states, the relationship between tau(t) and omega(t) may change, reflecting onset of yielding, reduction in effective yield stress, redistribution of material mass, or changes in internal flow regime. In such embodiments, the system infers one or more material conditions based on deviations from an expected torque-speed or powder-speed relationship. Additionally or alternatively, the system determines data indicative of shear-rate fields at the material-container boundary.

[0356] In some embodiments, the system computes one or more normalised or specific drive-effort quantities to reduce dependence on absolute material quantity or container geometry. By way of non-limiting example, a specific po er quantity p specific(t) may be determined as: p_specific(t) = P(t) / m where m is an estimated bulk mass of material in the container. Additionally or alternatively, power may be normalised per unit volume, per unit effective contact area, or per unit fill fraction. In some embodiments, the specific power p_specific is treated as a function of one or more material parameters including yield stress tau_y, apparent or plastic viscosity mu, material density rho, and kinematic conditions including a characteristic velocity' or rotation speed V, such that: p_specific = f(tau_y, mu, V, rho. ...) where f(.) represents a model, mapping, correlation, or learned function. The function f may be implemented using analytical expressions, parametric regression, nonlinear fitting, or machine-learned mappings.

[0357] In some embodiments, yield-related behavior is identified by detecting changes in slope, curvature, regime, or nonlinearity' in the relationship betw een one or more of tau(t), omega(t), P(t), or p_specific(t). Such changes may be indicative of transitions in effective material mobilization, including expansion or contraction of yielded regions.Page 95 of 27214528758vlchanges in flow regime, or changes in resistance to shear under rotation. In such embodiments, a threshold, inflection point, or regime change in the torque-speed or powerspeed relationship is indicative of a change in effective yield behavior analogous to gravity -driven slump or flow under self-weight, without requiring the material to transition from a fully unyielded to a fully yielded state.

[0358] In some embodiments, one or more material models are configured to estimate rheological parameters by fitting measured torque- or power-based quantities to a model representation that includes yield stress and viscosity terms. In such embodiments, inferred parameters may include one or more of yield stress tau_y, apparent viscosity mu, resistance-to-flow metrics, workability indices, or other rheological proxies, without requiring direct measurement of shear rate (gamma_dot).

[0359] In some embodiments, numerical regularisation is applied such that effective viscosity depends on a regularised shear-rate proxy, for example using a relationship of the form: eta_eff = eta_0 + tau_y / (gamma_dot + epsilon) where epsilon is a small regularisation constant. Such regularisation enables stable inference in low-shear or near-static regimes commonly encountered during rotation and transit. The inferred parameters may represent bulk-effective material properties under rotational shear and gravity-conditioned loading rather than laboratory rheometer values.

[0360] In some embodiments, the system determines material conditions based on changes in drive effort required to rotate a container over time, using one or more signals indicative of torque, power, energy consumption, or rotation speed of a vehicle-mounted container. In such embodiments, as material rheological properties evolve during transit — such as due to hydration, water loss, remixing, or water or admixture addition — the resistance of the material to rotation changes, resulting in measurable changes in one or more of: (i) torque required to maintain a substantially constant rotation speed; (ii) power or energy consumed per unit time or per revolution at a given rotation speed; or (iii) achieved rotation speed in response to a substantially constant drive command or applied torque. Such changes are indicative of time-dependent evolution of material yield stress, flow resistance, slump, or workability.

[0361] In some embodiments, the system measures or derives a rotation-related quantity over a plurality of times, including torque r(t), angular velocity co(t), power P(t) = r(t) co(t), energy per revolution, or energy per unit mass, and determines a material condition based on relative change, trend, or rate of change of such quantities over time. For example, an increase in torque or energy required to maintain a given rotation speed over time may be Page 96 of 27214528758vlindicative of decreasing slump or increasing yield resistance, while a decrease in required torque or an increase in achieved rotation speed may be indicative of increased workability, such as following remixing or material addition. In such embodiments, material conditions are inferred from temporal evolution of drive-system response, rather than from a single absolute measurement, enabling determination of rheological state and its change during transport, mixing, or prior to discharge.

[0362] In some embodiments, the system performs a calibration procedure to relate drive-system-derived signals and inertial measurements to material -dependent resistance to rotation of a container. During calibration, one or more reference conditions are observed in which the container is empty, partially filled, filled with a known reference material, filled with water, or filled with a material having a known or estimated workability, slump, yield stress, or rheological state. During such reference conditions, one or more signals indicative of torque, power, energy consumption, or drive effort are measured in association with inertial measurements indicative of actual rotational motion, including angular position 6(t), angular velocity co(t), or angular acceleration. The calibration establishes one or more baseline relationships between drive effort and kinematic response that are not attributable to the material under test.

[0363] In some embodiments, calibration comprises associating measured drivesystem signals with measured rotational behavior to form one or more normalized or derived quantities indicative of material resistance to motion. For example, torque r(t), power P(t), or energy per revolution may be evaluated in association with measured angular velocity co(t), rotation frequency, or rotation stability to determine calibrated quantities such as P(t) / co(t), Jr(t)dO, or energy dissipated per rotation. Such calibration enables separation of material-driven resistance from variations arising from drive system behavior, operating conditions, container geometry, fill fraction, or vehicle-specific characteristics, without requiring direct control of rotation speed or explicit rheological modeling.

[0364] In some embodiments, the calibrated relationships are used to map subsequently measured drive- and motion-based signals to one or more material conditions, including yield-related behavior, flow behavior, slump, workability, or resistance to motion, using rules, correlations, models, or learned mappings. Additionally or alternatively, calibration parameters may be updated over time using repeated observations, fleet-level data, or reference events, and may be stored in association with a specific vehicle, container, sensing assembly, or mix identifier. The calibrated mapping enables consistent interpretation of torque- or energy-based sensing across different trucks, operating conditions, and time Page 97 of 27214528758vlintervals, and supports inference of time-evolving material properties during transport, including degradation or recovery of workability and identification of conditions warranting material adjustment or intervention.

[0365] In some embodiments, the container is rotated at a substantially constant rotational speed, and material condition(s) are inferred from changes in torque, power, or energy required to maintain said rotational speed over time. In other embodiments, rotational speed varies due to control strategy, load, or operating conditions, and material condition(s) are inferred from changes in rotational speed, acceleration, or stability for a given drive effort. In further embodiments, both effects are evaluated jointly using measured torque r(t) and angular velocity co(t), without requiring direct control of either variable.

[0366] In some embodiments, the system compares a commanded or nominal rotation state with a measured rotation state obtained from inertial sensing. Deviations between commanded rotation speed and measured angular velocity, or increased drive effort required to achieve a commanded rotation state, are indicative of changes in material resistance to motion within the container.

[0367] In some embodiments, the resistance to rotation reflected in torque or energy measurements is attributable, at least in part, to shear stresses developed within the material as it moves relative to the container wall and relative to itself under gravity7and rotation. The measured drive effort therefore provides a bulk proxy for internal shear resistance of the material, without requiring direct measurement of shear rate or explicit solution of a constitutive equation.

[0368] In some embodiments, torque- or energy-based response signals are further indicative of degree of mixing, homogenization, or segregation of the material within the container. Changes in drive effort, harmonic content, or cycle-to-cycle variability7over time may indicate transitions betyveen poorly mixed, partially mixed, and substantially homogeneous states, and may be used to determine readiness for discharge or need for continued rotation.

[0369] In some embodiments, torque-, power-, or energy -related signals are analyzed in the frequency domain or using harmonic decomposition as a function of rotation angle or time. Harmonic content, spectral energy distribution, or angular-sector asymmetry may be indicative of non-uniform material distribution, localized yielding, or asymmetric flow within the container.

[0370] In some embodiments, drive effort or energy7metrics are normalized by one or more of material mass, estimated fill fraction, container volume, or material density to obtain Page 98 of 27214528758vlspecific energy or specific resistance metrics that are comparable across different vehicles, batch sizes, or operating conditions.

[0371] In some embodiments, material condition(s) are inferred from a time evolution of torque-, power-, or energy-based metrics during transport, wherein workability or resistance to motion exhibits a generally monotonic increase over time due to hydration and thixotropic effects, except in cases of material addition or remixing. Deviations from an expected temporal trend may be used to identify material adjustment events or abnormal conditions.

[0372] In such embodiments, material condition(s) are inferred without requiring a sensor to be in contact with the material, relying instead on drive-system-derived signals and kinematic measurements.

[0373] In some embodiments, the system determines a material-attributable component of a measured torque-, power-, or energy-related signal by decomposing a total drive-effort signal into one or more components attributable to container mechanics, drivetrain losses, or vehicle operating conditions, and one or more components attributable to resistance imposed by the material. Such decomposition may be performed using calibration baselines, normalization, temporal differencing, trend analysis, model-based separation, or learned mappings. Material condition(s) are inferred from the material-attributable component without requiring explicit modeling of drivetrain losses, bearing friction, or container mechanics.

[0374] In some embodiments, material condition(s) are inferred not only from changes in torque-, power-, or energy-based metrics, but additionally or alternatively from persistence, absence, delay, or deviation from an expected change in such metrics over time. For example, failure of torque, power, or specific power to increase or decrease as expected may be indicative of altered hydration kinetics, ineffective remixing, admixture performance, or abnormal material behavior.

[0375] In some embodiments, torque-, power-, or energy-based quantities are evaluated as a function of angular position 0 of the container, such that T(9), P(9), energy -per-sector, or related angularly resolved quantities are determined over one or more revolutions. Angular variation, asymmetry, or harmonic structure in such quantities may be indicative of non-uniform material distribution, localized yielding, asymmetric flow, or preferential material loading within the container.

[0376] In some embodiments, one or more time-derivative or rate-of-change quantities are derived from torque-, power-, or energy-based measurements, including dr / dt.Page 99 of 27214528758vldP / dt, d(p_specific) / dt, or higher-order derivatives. Such rate-based quantities may be indicative of time-dependent material evolution including hydration progression, thixotropic rebuilding, remixing response, or recovery following material addition, and may be used as primary sensing signals rather than relying on absolute magnitude alone.

[0377] In some embodiments, torque-, power-, or energy-based sensing is used to infer material condition(s) based on comparisons across operating states, including comparisons between steady-state rotation and transient acceleration, between loaded and unloaded rotation, or between different rotation speeds or duty cycles. Such comparisons may be performed without requiring direct control of rotation speed and may rely on naturally occurring variations in vehicle operation.

[0378] In some embodiments, torque- or power-based inference is combined with inertial sensing, strain-based sensing, electromechanical sensing, electromagnetic sensing, or electrochemical sensing as described elsewhere herein. In some embodiments, drive-system-derived signals provide a global or bulk measure of material resistance to motion, while other sensing modalities provide local, spatially resolved, or composition-sensitive measurements.

[0379] In some embodiments, material conditions inferred from torque- or powerbased sensing are used to generate one or more outputs including, additionally or alternatively: adjustment of container rotation rate or duty' cycle; initiation, suppression, or recommendation of water or admixture addition; determination of suitability for discharge or placement; or generation of material recipe-related outputs for the same or subsequent batches.Combination Embodiments

[0380] In some embodiments, the system obtains response signals from a plurality' of sensing modalities described herein and uses such response signals, or features derived therefrom, to determine one or more material conditions. The sensing modalities may include, without limitation, electromechanical sensing, electromagnetic sensing, electrochemical sensing, inertial sensing, temperature sensing, or combinations thereof. The response signals may be obtained from the same or different material volumes, at the same or different times, and under the same or different excitation, orientation, or operating conditions.

[0381] In some embodiments, electromechanical sensing is combined with electromagnetic sensing, such that electromechanical response features indicative ofPage 100 of 27214528758vldeformation, damping, yielding, or flow behavior are interpreted in combination with electromagnetic response features indicative of material composition, moisture state, dielectric properties, or water-to-cement ratio. In such embodiments, the combined information is used to determine rheological properties including yield stress, slump, flowability', or workability' under static or dynamic conditions.

[0382] In some embodiments, electromechanical sensing is combined with electrochemical sensing, such that electromechanical response features indicative of bulk mechanical behavior are interpreted in combination with electrochemical response features indicative of ionic concentration, pore solution chemistry7, hydration state, or admixture effects. The combined response information is used to determine material conditions including yield-related parameters, flow behavior, or temporal evolution of workability.

[0383] In some embodiments, response signals from multiple sensing modalities are further combined with contextual or environmental measurements obtained from one or more accelerometers, gyroscopes, inertial measurement units, rotation sensors, temperature sensors, or combinations thereof. Such contextual measurements are used to condition, normalize, or time-align material response signals, or to disambiguate excitation and operating conditions.

[0384] In some embodiments, response signals or derived features from tw o or more sensing modalities are provided as inputs to one or more material inference, classification, prediction, or optimization models, including models for predicting rheological behavior, workability7, material composition, or material evolution. The present disclosure is not limited to any specific pairing, ordering, or dependency among sensing modalities and encompasses any combination of sensing approaches described herein used to determine material conditions.Unified Field Interaction Framework for Material Sensing in a Vehicle-Mounted Container

[0385] As described throughout this specification, the sensing assembly' may be implemented using a plurality' of sensing modalities, excitation mechanisms, geometries, and operating schedules. In some embodiments, the sensing assembly is configured to interact with material contained within a vehicle-mounted container via one or more physical fields and to infer one or more material properties or material conditions from measured responses to such interaction. The physical fields may include one or more of mechanical deformationPage 101 of 27214528758vlfields, elastic or acoustic wave fields, electrical or electrochemical fields, electromagnetic fields, gravitational or load-induced stress fields, or combinations thereof.

[0386] In some embodiments, material property determination or material condition determination is performed by observing a response of the material to an excitation, wherein the excitation comprises a perturbation, force, field, or form of energy' that interacts with the material. The excitation may be controlled, observed, inferred, or measured, and the sensing assembly is configured to associate a measured material response with the excitation in order to infer material properties or material conditions.

[0387] In some embodiments, the excitation is generated by the sensing assembly itself. In such embodiments, the sensing assembly applies an input stimulus to the material comprising excitation of one or more physical fields. The excitation may include one or more of: mechanical excitation, electrical or electrochemical excitation, electromagnetic excitation, or acoustic excitation, or combinations thereof.

[0388] In some embodiments, excitation generated by the sensing assembly may be static, oscillatory, pulsed, stepped, alternating, multi-tone, or swept, including excitation defined over one or more frequencies, frequency bands, or frequency ranges. Additionally or alternatively, excitation parameters including amplitude, phase, frequency, duty cycle, duration, waveform, polarity, bias level, spatial distribution, electrode selection, antenna selection, or temporal sequencing may be controlled, varied, or modulated by the sensing assembly.

[0389] In some embodiments, excitation applied in a first physical domain produces a response in a second physical domain. For example, mechanical excitation may produce an electrochemical, electromagnetic, acoustic, or electrical response, or electrical excitation may induce mechanical deformation, vibration, or flow within the material. In such embodiments, the sensing assembly is configured to apply excitation in one domain and observe a response in another domain, and to infer material properties based on the relationship between the excitation and the observed response.

[0390] In some embodiments, mechanical excitation applied to the material spans one or more frequency regimes selected based on desired interaction scale, material response time, and sensing objective. Excitation frequencies may include quasi-static or low-frequency excitation below approximately 1 Hz, low-frequency and audible excitation from approximately 1 Hz to 20 kHz, and ultrasonic excitation above approximately 20 kHz, including ranges such as 20-100 kHz or higher. Lower-frequency excitation may be used to induce bulk deformation, sloshing, or redistribution of material within a container, while Page 102 of 27214528758vlhigher-frequency or ultrasonic excitation may be used to induce localized shear, probe viscoelastic response, or excite resonance of a sensing geometry or coupled structure. Excitation may be continuous, pulsed, burst-based, swept in frequency, or applied intermittently during defined time, phase, or rotation windows, and may be generated actively by a sensing assembly or arise passively from vehicle, container, or drive-system motion.

[0391] In some embodiments, mechanical excitation is applied or coupled through structures having characteristic dimensions selected to define a spatial scale of interaction with the material. Such characteristic dimensions may include, without limitation, lengths on the order of approximately 0.1-lcm, 1-5 cm, 5-10 cm, 10-50 cm, or greater, corresponding to localized, regional, or bulk interaction volumes, respectively. Excitation “amplitude” may be expressed using one or more physically meaningful quantities, including displacement amplitude, velocity amplitude, acceleration amplitude, force amplitude, stress amplitude, strain amplitude, shear rate, energy input, or power density, depending on excitation mechanism and coupling geometry.

[0392] In some embodiments, mechanical excitation applied to the material is selected relative to stress and deformation scales associated with gravity-driven slump and flow of cementitious materials, and relative to a finite material volume of interest defined by the sensing geometry and excitation coupling. By way of non-limiting example, displacement amplitudes may range from micrometers to millimeters; velocity amplitudes may range from millimeters per second to meters per second; acceleration amplitudes may range from less than 0.1 g to several g; and force amplitudes may range from fractions of a newton to tens or hundreds of newtons, depending on sensing geometry, coupling area, and the effective material volume engaged by the excitation. Such excitation may induce local or regional shear stresses within a material volume of interest on the order of tens to thousands of pascals, corresponding to stress levels known to govern yield stress, slump, and flow behavior under self-weight. In some embodiments, the material volume of interest corresponds to a localized region adjacent to the sensing assembly; in other embodiments, it corresponds to a larger regional portion of the container influenced by excitation, gravity, or motion. Excitation parameters may be selected to remain below yield within the volume of interest, to traverse a yield-transition region comparable to that observed in standardized slump behavior, or to exceed yield to induce localized yielding or flow within that volume. In some embodiments, excitation intensity is characterized using integrated or effective measures including one or more of energy per cycle, energy per unit volume, power, or power density associated with the material volume of interest, rather than instantaneous amplitude. Material condition(s)Page 103 of 27214528758vlindicative of yield stress, slump, flowability', or workability are inferred from amplitude- or intensity-dependent changes in measured response within the material volume of interest. All ranges and metrics described herein are exemplary and non-limiting, and excitation may be characterized using equivalent force-, stress-, strain-, energy-, or rate-based quantities or combinations thereof.

[0393] In some embodiments, the excitation is generated externally to the sensing assembly, including excitation arising from operation of the vehicle, container, or container drive system. Such excitation may include rotation of the container, operation of a drum motor, vibration, acceleration, torque, or dynamic loading imparted by vehicle motion. In such embodiments, material motion, deformation, impact, flow, redistribution, or sloshing induced by vehicle or container operation constitutes an excitation to which the material responds.

[0394] In some embodiments, the excitation arises from environmental or ambient sources, including road-induced vibration, terrain-induced motion, wind loading, thermal gradients, or other external influences. Such excitation may be unintentional, intermittent, or stochastic, and may nevertheless interact with the material in a manner that produces measurable responses.

[0395] In some embodiments, when excitation is generated in whole or in part externally to the sensor assembly, the system is configured to measure, estimate, or infer characteristics of the excitation in order to associate a measured material response with a corresponding excitation. In such embodiments, the system uses one or more sensing elements integrated within the system to determine excitation characteristics which may include one or more of acceleration, rotation rate, angular position, vibration, or motion. In some embodiments, a same sensing element or sensing sub-assembly is used both to characterize the externally generated excitation and to sense the material response, such that excitation characterization and material sensing are performed using shared hardware resources. In another embodiment, a different sensing element is used to determine data indicative of the excitation. The system correlates measured or inferred excitation characteristics with measured material responses to distinguish excitation-driven effects from intrinsic material behavior.

[0396] In some embodiments, the sensing assembly measures material responses in the time domain, frequency domain, or both, which may include amplitude, phase, impedance, admittance, attenuation, resonance behavior, relaxation behavior, dispersion,Page 104 of 27214528758vlnon-linear response characteristics, spectral features, or spatial response patterns indicative of interaction between the excitation and the material.

[0397] In some embodiments, interaction between an electromagnetic field and the material is used to infer material properties or material conditions. In some embodiments, electromagnetic field interaction includes imaging-based sensing, wherein electromagnetic radiation interacting with material surfaces, exposed material during discharge, or thermally emitting material is observed using one or more imaging modalities. Such imaging may include optical imaging, infrared or thermal imaging, multispectral imaging, or hyperspectral imaging, and may be performed using passive illumination, active illumination, or combinations thereof. Spatial, spectral, and / or temporal variations in observed electromagnetic response may be indicative of material yield behavior, material flow behavior, segregation, moisture distribution, hydration heat, or discharge consistency.

[0398] In some embodiments, interaction between an acoustic or elastic wave field and the material is used to infer material properties or material conditions. Pressure waves, vibrations, or sound generated by material motion, impact, or flow within the container may be observed using microphones, accelerometers, vibration sensors, or other transducers located on or outside the container. Measured acoustic or vibrational responses may be indicative of aggregate impacts, flow regimes, damping behavior, resonanc...

Claims

WHAT IS CLAIMED IS:

1. A method for material property determinations, the method comprising:receiving sensor data from a sensor associated with a vehicle;generating a predicted material condition of a material at least partially contained by the vehicle; andoutputting the predicted material condition.

2. The method of Claim 1, wherein, in generating the predicted material condition, the method further comprises determining one or more properties of the material.

3. The method of Claim 2, wherein the one or more properties of the material comprise a viscosity.

4. The method of Claim 1, wherein the predicted material condition comprises a predicted outcome of a slump test for the material.

5. The method of Claim 1, wherein the sensor is operatively coupled to an internal portion of the vehicle that at least partially contains the material.

6. The method of Claim 4, wherein the internal portion defines one or more extensions to which the sensor is operatively coupled.

7. The method of Claim 4, wherein the sensor is configured to at least partially contact the material during operation.

8. The method of Claim 1, wherein the sensor is operatively coupled to an external portion of the vehicle opposite an internal portion of the vehicle that at least partially contains the material.

9. The method of Claim 2, wherein the sensor comprises an imaging device configured to generate image data associated with the material, and wherein the method furtherPage 268 of 27214528758V1comprises determining the one or more properties of the material via analyzing a captured image of the material.

10. The method of Claim 2, wherein the method further comprises:determining an accumulation of the material on a housing of the sensor; determining an interference on the sensor data based on the accumulation; and modifying the predicted material condition and / or the one or more properties of the material to compensate for the interference.

11. A sensor device for material property' determinations comprising:a housing configured to be operatively coupled with a vehicle,a sensor associated with the housing and configured to generate data associated with a material at least partially contained by the vehicle,a processor, anda non-transitory storage device containing instructions that, when executed by the processor, causes the processor to:receive sensor data from the sensor associated with the vehicle; generate a predicted material condition of the material; andoutput the predicted material condition.

12. The sensor device of Claim 11 , wherein, in generating the predicted material condition, executing the instructions further causes the processor to determine one or more properties of the material.

13. The sensor device of Claim 12, wherein the one or more properties of the material comprise a viscosity.

14. The sensor device of Claim 11, wherein the predicted material condition comprises a predicted outcome of a slump test for the material.

15. The sensor device of Claim 11, wherein the sensor is operatively coupled to an internal portion of the vehicle, wherein the internal portion defines a cavity configured to at least partially contain the material.Page 269 of 27214528758V116. The sensor device of Claim 15, wherein the internal portion comprises one or more extensions to which the sensor is operatively coupled.

17. The sensor device of Claim 15, wherein the housing is configured to interact with the material.

18. The sensor device of Claim 17, wherein the sensor data is indicative of a shear force experienced by the housing when interacting with the material.

19. The sensor device of Claim 17, wherein the sensor is a piezoelectric-based sensor.

20. The sensor device of Claim 12, wherein the sensor comprises an imaging device configured to generate image data associated with the material, and wherein executing the instructions further causes the processor to determine the one or more properties of the material via analyzing a captured image of the material.

21. The sensor device of Claim 12, wherein executing the instructions further causes the processor to:determine an accumulation of the material on the housing of the sensor; determine an interference on the sensor data based on the accumulation; and modify the predicted material condition and / or the one or more properties of the material to compensate for the interference.

22. The sensor device of Claim 12, wherein the sensor further comprises an acoustic sensor operatively coupled to an external portion of the vehicle, wherein the acoustic sensor is configured to:transmit an acoustic wave toward the material;receive a response wave corresponding to a portion of the acoustic wave after interaction with the material; anddetermine the one or more properties of the material based on the received response wave.

23. A method comprising:Page 270 of 27214528758vlcoupling a sensing assembly to an at least partially flowable material such that a response of the material to excitation influences the sensing assembly;subjecting the material to excitation under a plurality of excitation conditions, the excitation conditions differing in at least one of excitation amplitude, excitation frequency, excitation waveform, excitation duration, or excitation timing;measuring, using the sensing assembly, one or more response signals for each of the plurality of excitation conditions;extracting, from the measured response signals, one or more response features that vary as a function of the excitation conditions; anddetermining, based on a change in at least one of the response features across the plurality of excitation conditions, one or more material conditions for the material.

24. The method of Claim 23, wherein the excitation conditions and measurement of the response signals are coordinated based on one or more of (i) a measured rotational state of a container or vehicle carrying the material, (ii) a time reference provided by a clock, or (iii) a location reference derived from a positioning system.

25. The method of Claim 23, wherein subjecting the material to excitation comprises applying a sequence of excitation amplitudes across successive excitation events, and wherein changes in one or more response features across the sequence are used to determine a material condition indicative of material yielding, slump, flow, or rheological property of the material.Page 271 of 27214528758vl

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