Methods and systems relating to concrete production and delivery

Abstract

Increasing demands for cost reductions, profitability, tighter construction deadlines and potential liabilities mean construction companies, raw material suppliers, infrastructure owners, etc. are seeking cost effective systems, method and processes relating to the quality control of said construction materials at all stages from initial raw materials through production and on to delivery and deployment. Further, demands for improved flexibility in delivering concrete in dynamic delivery environments with real-time decision making require accurate information of concrete loads pre-delivery, during delivery and post-delivery. Real-time load monitoring allows verification of the load initially batched, the load delivered, and the load remaining such that concrete re-allocation and real-time mix adjustments can be made. Accordingly, multiple solutions for real-time load determination and real-time slump measurement are presented allowing automated decision making processes to optimize subsequent batches, re-purpose remaining loads or redirect remaining loads to other deployment locations.

Description

METHODS AND SYSTEMS RELATING TO CONCRETE PRODUCTION ANDDELIVERYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application claims the benefit of priority to U.S. Provisional Patent Application 63 / 660,825 filed June 17, 2024; the entire contents of which are incorporated herein by reference.FIELD OF THE INVENTION

[0002] This patent application relates to construction materials and more particularly to the systems, method and processes relating to the production and delivery of such construction materials.BACKGROUND OF THE INVENTION

[0001] Globally, the construction industry output is forecast to rise from US$10.8 trillion in 2017 to US$12.9 trillion in 2022. A wide variety of materials are employed within the construction industry of which some are chemically active materials, e.g., concrete, that often need to be analyzed so as to determine the structural properties parameters, particularly strength and other physical-mechanical properties of the final cured product, such as its potential for shrinkage.

[0002] Increasing demands for cost reductions, profitability, tighter construction deadlines and potential liabilities mean construction companies, raw material suppliers, infrastructure owners, etc. are seeking cost effective systems, method and processes relating to the quality control of said construction materials at all stages from initial raw materials through production and on to delivery and deployment.

[0003] Further, demands for cost reductions, carbon footprint reduction and improved flexibility in delivering concrete in dynamic delivery environments with real-time decision making require accurate information of concrete loads pre-delivery, during delivery and postdelivery which can be established by embedded sensors or sensors associated with the delivery truck. For example, real-time load monitoring allows verification of the load initially batched, the load delivered, and the load remaining such that concrete re-allocation and real-time mix adjustments can be made.

[0004] Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.SUMMARY OF THE INVENTION

[0005] It is an object of the present invention to mitigate limitations within the prior art relating to construction materials and more particularly to the systems, method and processes relating to the production and delivery of such construction materials.

[0006] In accordance with an embodiment of the invention there is provided a method comprising: disposing one or more acoustic sensors upon an exterior surface of a drum containing a material; acquiring an electrical output from each of the one or more acoustic sensors during rotation of the drum; and processing the acquired electrical outputs from the one or more acoustic sensors to establish a load of the material within the drum.

[0007] In accordance with an embodiment of the invention there is provided a method comprising: disposing one or more sensors with respect to at least one of a drum and a truck comprising the drum where the drum contains a material; acquiring data from the one or more acoustic sensors; processing the acquired data from the one or more sensors to establish a load of the material within the drum; and establishing an adjusted slump measurement for the material in the drum in dependence upon the established load of the material in the drum and a slump measurement of the material within the drum.

[0008] In accordance with an embodiment of the invention there is provided a method comprising: disposing a first temperature sensor in a first position with respect to chute employed in dispensing the material from the drum; disposing a second temperature sensor in a second position with respect to chute employed in dispensing the material from the drum;disposing a heater element between the first temperature sensor and the second temperature sensor; and establishing a mass flow of the material being dispensed from the drum in dependence upon the temperature measurements from the first temperature sensor and the second temperature sensor.

[0009] Other aspects and features of the present invention will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments of the invention in conjunction with the accompanying figures.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Embodiments of the present invention will now be described, by way of example only, with reference to the attached Figures, wherein:

[0011] Figure 1 depicts an exemplary electronic device supporting communications both to a network and with embedded sensors according to and supporting embodiments of the invention;

[0012] Figure 2 depicts an embedded sensor methodology for data logging concrete properties from initial mix through pouring, curing, and subsequently according to an embodiment of the invention;

[0013] Figure 3A depicts an exemplary process flow for optimizing a manufacturing specification for a construction material according to an embodiment of the invention exploiting machine learning and artificial intelligence;

[0014] Figures 3B and 3C depict exemplary schematics of extended sensor integration within a concrete production facility in conjunction with embedded sensors monitoring one or more portions of a subsequent life cycle of the construction material for optimizing a mixture for a construction material according to an embodiment of the material;

[0015] Figure 4 depicts an exemplary process flow for optimizing a construction material during transportation according to an embodiment of the invention exploiting machine learning and artificial intelligence;

[0016] Figure 5 depicts a sensor, pressure sensor and data acquisition system for a drum of a concrete delivery truck employed as part of a system for determining in situ yield stress measurements of a construction material according to an embodiment of the invention;

[0017] Figure 6 depicts methods of obtaining a load of a construction material within a delivery vehicle (vehicle, commonly referred to as a truck, or concrete truck where the constructionmaterial is concrete) at any point in time according to one or more embodiments of the invention;

[0018] Figure 7 depicts a schematic of obtaining the load of the construction material within a container (e.g. drum) of the truck via the load applied to the truck according to an embodiment of the invention;

[0019] Figure 8 depicts a method of obtaining the load of the construction material within the drum of the truck via axle strain monitoring according to an embodiment of the invention;

[0020] Figure 9 depicts a method of obtaining the load of the construction material within the drum of the truck via a string potentiometer(s) between a truck bed and truck axle(s) according to an embodiment of the invention;

[0021] Figure 10 depicts a method of obtaining the load of the construction material within the drum of the truck via ride height sensor(s) according to an embodiment of the invention;

[0022] Figure 11 depicts a method of obtaining the load of the construction material within the drum of the truck via measuring deflection of shock absorber(s) according to an embodiment of the invention;

[0023] Figure 12 depicts a method of obtaining the load of the construction material within the drum of the truck via rotational deflection measurements on spring(s) on the axle(s) of the truck according to an embodiment of the invention;

[0024] Figure 13 depicts a method of obtaining the load of the construction material within the drum of the truck via strain measurements on spring(s) on the axle(s) of the truck according to an embodiment of the invention;

[0025] Figure 14 depicts a method of obtaining the load of the construction material within the drum of the truck via resonance of the drum of the truck according to an embodiment of the invention;

[0026] Figure 15 depicts a method of obtaining the load of the construction material within the drum of the truck via employing the drum of the truck as a Helmholtz resonator according to an embodiment of the invention;

[0027] Figure 16 depicts a method of obtaining the load of the construction material within the drum of the truck via contact force of the drum to the truck bed of the truck according to an embodiment of the invention;

[0028] Figure 17 depicts a method of obtaining the load of the construction material dispensed via thermal mass flow measurements on chute(s) of the dispensing system according to an embodiment of the invention;

[0029] Figure 18 depicts a method of obtaining the load of the construction material within the drum of the truck via engine power monitoring and acceleration of the truck according to an embodiment of the invention;

[0030] Figure 19 depicts a method of obtaining the load of the construction material within the drum of the truck via measuring resonance of the truck bed according to an embodiment of the invention;

[0031] Figure 20 depicts a method of obtaining the load of the construction material within the drum of the truck via fill level monitoring within the drum according to an embodiment of the invention;

[0032] Figure 21 depicts a method of obtaining the load of the construction material within the drum of the truck via fill monitoring etc. via a replaced hatch of the drum according to an embodiment of the invention;

[0033] Figure 22 depicts a method of obtaining the load of the construction material within the drum of the truck via measuring drum deflection relative to the truck bed according to an embodiment of the invention;

[0034] Figure 23 depicts a system for dispensing a fluid and additive(s) to a load of construction material within the drum of a truck according to an embodiment of the invention;

[0035] Figure 24 depicts schematically an adjustment process for a construction material as established by an artificial intelligence / machine learning system according to an embodiment of the invention with geographically or project defined margin(s); and

[0036] Figure 25 depicts schematically an adjustment process for a construction material as established by an artificial intelligence / machine learning system according to an embodiment of the invention;

[0037] Figure 26 depicts an acoustic sensor according to an embodiment of the invention attached to a drum of truck;

[0038] Figure 27 depicts multiple acoustic sensors such as that depicted in Figure 26 attached to the drum of a truck and acoustic signal measurements from the acoustic sensors under rotation of the drum;

[0039] Figure 28 depicts multiple acoustic sensors such as that depicted in Figure 26 attached to the drum of a truck; and

[0040] Figure 29 depicts the variation in the acoustic sensor signal during rotation of the drum of a truck under varying loads within the drum.DETAILED DESCRIPTION

[0041] The present invention is directed to construction materials and more particularly to the systems, method and processes relating to the production and delivery of such construction materials.

[0042] The ensuing description provides representative embodiment(s) only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the embodiment(s) will provide those skilled in the art with an enabling description for implementing an embodiment or embodiments of the invention. It being understood that various changes can be made in the function and arrangement of elements without departing from the spirit and scope as set forth in the appended claims. Accordingly, an embodiment is an example or implementation of the inventions and not the sole implementation. Various appearances of “one embodiment,” “an embodiment” or “some embodiments” do not necessarily all refer to the same embodiments. Although various features of the invention may be described in the context of a single embodiment, the features may also be provided separately or in any suitable combination. Conversely, although the invention may be described herein in the context of separate embodiments for clarity, the invention can also be implemented in a single embodiment or any combination of embodiments.

[0043] Reference in the specification to “one embodiment,” “an embodiment,” “some embodiments” or “other embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment, but not necessarily all embodiments, of the inventions. The phraseology and terminology employed herein is not to be constmed as limiting but is for descriptive purpose only. It is to be understood that where the claims or specification refer to “a” or “an” element, such reference is not to be constmed as there being only one of that element. It is to be understood that where the specification states that a component feature, stmcture, or characteristic “may,” “might,” “can” or “could” be included, that particular component, feature, stmcture, or characteristic is not required to be included.

[0044] Reference to terms such as “left,” “right,” “top,” “bottom,” “front” and “back” are intended for use in respect to the orientation of the particular feature, stmcture, or element within the figures depicting embodiments of the invention. It would be evident that such directional terminology with respect to the actual use of a device has no specific meaning as the device can be employed in a multiplicity of orientations by the user or users.

[0045] Reference to terms “including,” “comprising,” “consisting,” and grammatical variants thereof do not preclude the addition of one or more components, features, steps, integers, or groups thereof and that the terms are not to be construed as specifying components, features, steps, or integers. Likewise, the phrase “consisting essentially of,” and grammatical variants thereof, when used herein is not to be constmed as excluding additional components, steps, features integers or groups thereof but that the additional features, integers, steps, components, or groups thereof do not materially alter the basic and novel characteristics of the claimed composition, device, or method. If the specification or claims refer to “an additional” element, that does not preclude there being more than one of the additional element.

[0046] A “wireless standard” as used herein and throughout this disclosure, refer to, but is not limited to, a standard for transmitting signals and / or data through electromagnetic radiation which may be optical, radiofrequency (RF) or microwave although typically RF wireless systems and techniques dominate. A wireless standard may be defined globally, nationally, or specific to an equipment manufacturer or set of equipment manufacturers. Dominant wireless standards at present include, but are not limited to IEEE 802.11, IEEE 802.15, IEEE 802.16, IEEE 802.20, UMTS, GSM 850, GSM 900, GSM 1800, GSM 1900, GPRS, ITU-R 5.138, ITU- R 5.150, ITU-R 5.280, IMT-1000, Bluetooth, Wi-Fi, Ultra-Wideband and WiMAX. Some standards may be a conglomeration of sub-standards such as IEEE 802.11 which may refer to, but is not limited to, IEEE 802.1a, IEEE 802.11b, IEEE 802.11g, or IEEE 802.1 In as well as others under the IEEE 802.11 umbrella.

[0047] A “wired standard” as used herein and throughout this disclosure, generally refer to, but is not limited to, a standard for transmitting signals and / or data through an electrical cable discretely or in combination with another signal. Such wired standards may include, but are not limited to, digital subscriber loop (DSL), Dial-Up (exploiting the public switched telephone network (PSTN) to establish a connection to an Internet service provider (ISP)), Data Over Cable Service Interface Specification (DOCSIS), Ethernet, Gigabit home networking (G.hn), Integrated Services Digital Network (ISDN), Multimedia over Coax Alliance (MoCA), and Power Line Communication (PLC, wherein data is overlaid to AC / DC power supply). In some embodiments a “wired standard” may refer to, but is not limited to, exploiting an optical cable and optical interfaces such as within Passive Optical Networks (PONs) for example.

[0048] A “user” as used herein may refer to, but is not limited to, an individual or group of individuals. This includes, private individuals, employees of organizations and / or enterprises, members of community organizations, members of charity organizations, men, women, and children. In its broadest sense the user may further include, but not be limited to, mechanicalsystems, robotic systems, android systems, etc. that may be characterised by an ability to exploit one or more embodiments of the invention.

[0049] A “sensor” as used herein may refer to, but is not limited to, a transducer providing an electrical output generated in dependence upon a magnitude of a measure and selected from the group comprising, but is not limited to, environmental sensors, medical sensors, biological sensors, chemical sensors, ambient environment sensors, position sensors, motion sensors, thermal sensors, infrared sensors, visible sensors, RFID sensors, and medical testing and diagnosis devices.

[0050] A “portable electronic device” (PED) as used herein and throughout this disclosure, refers to a wireless device used for communications and other applications that requires a battery or other independent form of energy for power. This includes devices, but is not limited to, such as a cellular telephone, smartphone, personal digital assistant (PDA), portable computer, pager, portable multimedia player, portable gaming console, laptop computer, tablet computer, a wearable device, and an electronic reader.

[0051] A “fixed electronic device” (FED) as used herein and throughout this disclosure, refers to a wireless and / or wired device used for communications and other applications that requires connection to a fixed interface to obtain power. This includes, but is not limited to, a laptop computer, a personal computer, a computer server, a kiosk, a gaming console, a digital set-top box, an analog set-top box, an Internet enabled appliance, an Internet enabled television, and a multimedia player.

[0052] A “server” as used herein, and throughout this disclosure, refers to one or more physical computers co-located and / or geographically distributed running one or more services as a host to users of other computers, PEDs, FEDs, etc. to serve the client needs of these other users. This includes, but is not limited to, a database server, file server, mail server, print server, web server, gaming server, or virtual environment server.

[0053] An “application” (commonly referred to as an “app”) as used herein may refer to, but is not limited to, a “software application,” an element of a “software suite,” a computer program designed to allow an individual to perform an activity, a computer program designed to allow an electronic device to perform an activity, and a computer program designed to communicate with local and / or remote electronic devices. An application thus differs from an operating system (which runs a computer), a utility (which performs maintenance or general-purpose chores), and a programming tools (with which computer programs are created). Generally, within the following description with respect to embodiments of the invention an applicationis generally presented in respect of software permanently and I or temporarily installed upon a PED and I or FED.

[0054] An “enterprise” as used herein may refer to, but is not limited to, a provider of a service and / or a product to a user, customer, or consumer. This includes, but is not limited to, a retail outlet, a store, a market, an online marketplace, a manufacturer, an online retailer, a charity, a utility, and a service provider. Such enterprises may be directly owned and controlled by a company or may be owned and operated by a franchisee under the direction and management of a franchiser.

[0055] A “service provider” as used herein may refer to, but is not limited to, a third-party provider of a service and / or a product to an enterprise and / or individual and / or group of individuals and / or a device comprising a microprocessor. This includes, but is not limited to, a retail outlet, a store, a market, an online marketplace, a manufacturer, an online retailer, a utility, an own brand provider, and a service provider wherein the service and / or product is at least one of marketed, sold, offered, and distributed by the enterprise solely or in addition to the service provider.

[0056] A “third party” or “third party provider” as used herein may refer to, but is not limited to, a so-called “arm's length” provider of a service and / or a product to an enterprise and / or individual and / or group of individuals and / or a device comprising a microprocessor wherein the consumer and / or customer engages the third party but the actual service and / or product that they are interested in and / or purchase and / or receive is provided through an enterprise and / or service provider.

[0057] A “user” as used herein may refer to, but is not limited to, an individual or group of individuals. This includes, but is not limited to, private individuals, employees of organizations and / or enterprises, members of community organizations, members of charity organizations, men, and women. In its broadest sense the user may further include, but not be limited to, software systems, mechanical systems, robotic systems, android systems, etc. that may be characterised by an ability to exploit one or more embodiments of the invention. A user may also be associated through one or more accounts and / or profiles with one or more of a service provider, third party provider, enterprise, social network, social media etc. via a dashboard, web service, website, software plug-in, software application, and graphical user interface.

[0058] A “wearable device” or “wearable sensor” relates to miniature electronic devices that are worn by the user including those under, within, with or on top of clothing and are part of a broader general class of wearable technology which includes “wearable computers” which in contrast are directed to general or special purpose information technologies and mediadevelopment. Such wearable devices and I or wearable sensors may include, but not be limited to, smartphones, smart watches, e-textiles, smart shirts, activity trackers, smart glasses, environmental sensors, medical sensors, biological sensors, physiological sensors, chemical sensors, ambient environment sensors, position sensors, neurological sensors, drug delivery systems, medical testing and diagnosis devices, and motion sensors.

[0059] “Electronic content” (also referred to as “content” or “digital content”) as used herein may refer to, but is not limited to, any type of content that exists in the form of digital data as stored, transmitted, received and / or converted wherein one or more of these steps may be analog although generally these steps will be digital. Forms of digital content include, but are not limited to, information that is digitally broadcast, streamed, or contained in discrete files. Viewed narrowly, types of digital content include popular media types such as MP3, JPG, AVI, TIFF, AAC, TXT, RTF, HTME, XHTME, PDF, XES, SVG, WMA, MP4, FEV, and PPT, for example, as well as others. Within a broader approach digital content mat include any type of digital information, e.g., digitally updated weather forecast, a GPS map, an eBook, a photograph, a video, a Vine™, a blog posting, a Facebook™ posting, a Twitter™ tweet, online TV, etc. The digital content may be any digital data that is at least one of generated, selected, created, modified, and transmitted in response to a user request, said request may be a query, a search, a trigger, an alarm, and a message for example.

[0060] A “profile” as used herein, and throughout this disclosure, refers to a computer and / or microprocessor readable data file comprising data relating to settings and / or limits of an adult device. Such profiles may be established by a manufacturer / supplier / provider of a device, service, etc. or they may be established by a user through a user interface for a device, a service, or a PED / FED in communication with a device, another device, a server, or a service provider etc.

[0061] A “computer file” (commonly known as a file) as used herein, and throughout this disclosure, refers to a computer resource for recording data discretely in a computer storage device, this data being electronic content. A file may be defined by one of different types of computer files, designed for different purposes. A file may be designed to store electronic content such as a written message, a video, a computer program, or a wide variety of other kinds of data. Some types of files can store several types of information at once. A file can be opened, read, modified, copied, and closed with one or more software applications an arbitrary number of times. Typically, files are organized in a file system which can be used on numerous different types of storage device exploiting different kinds of media which keeps track of where the files are located on the storage device(s) and enables user access. The format of a file isdefined by its content since a file is solely a container for data, although, on some platforms the format is usually indicated by its filename extension, specifying the rules for how the bytes must be organized and interpreted meaningfully. For example, the bytes of a plain text file are associated with either ASCII or UTF-8 characters, while the bytes of image, video, and audio files are interpreted otherwise. Some file types also allocate a few bytes for metadata, which allows a file to carry some basic information about itself.

[0062] “Metadata” as used herein, and throughout this disclosure, refers to information stored as data that provides information about other data. Many distinct types of metadata exist, including but not limited to, descriptive metadata, structural metadata, administrative metadata, reference metadata and statistical metadata. Descriptive metadata may describe a resource for purposes such as discovery and identification and may include, but not be limited to, elements such as title, abstract, author, and keywords. Structural metadata relates to containers of data and indicates how compound objects are assembled and may include, but not be limited to, how pages are ordered to form chapters, and typically describes the types, versions, relationships, and other characteristics of digital materials. Administrative metadata may provide information employed in managing a resource and may include, but not be limited to, when and how it was created, file type, technical information, and who can access it. Reference metadata may describe the contents and quality of statistical data whereas statistical metadata may also describe processes that collect, process, or produce statistical data. Statistical metadata may also be referred to as process data.

[0063] An “artificial intelligence” system (referred to hereafter as artificial intelligence, Al) as used herein, and throughout disclosure, refers to machine intelligence or machine learning in contrast to natural intelligence. An Al may refer to analytical, human inspired, or humanized artificial intelligence. An Al may refer to the use of one or more machine learning algorithms and / or processes. An Al may employ one or more of an artificial network, decision trees, support vector machines, Bayesian networks, and genetic algorithms. An Al may employ a training model or federated learning.

[0064] “Machine Learning” (ML) or more specifically machine learning processes as used herein refers to, but is not limited, to programs, algorithms, or software tools, which allow a given device or program to leam to adapt its functionality based on information processed by it or by other independent processes. These learning processes are in practice, gathered from the result of said process which produce data and or algorithms that lend themselves to prediction. This prediction process allows ML-capable devices to behave according to guidelines initially established within its own programming but evolved as a result of the ML.A machine learning algorithm or machining learning process as employed by an Al may include, but not be limited to, supervised learning, unsupervised learning, cluster analysis, reinforcement learning, feature learning, sparse dictionary learning, anomaly detection, association rule learning, inductive logic programming.

[0065] Within the following description exemplary embodiments of the invention with respect to embedded or embeddable sensors, embedded or embeddable sensor measurements, monitoring systems employing embedded or embeddable sensors, construction material properties, construction material specifications, construction material verification, construction material qualification etc. are presented and described with respect to concrete. However, it would be evident to one of skill in the art that the embodiments of the invention may be applied to other construction materials and / or other applications other than concrete and that the scope of the invention is as defined by the claims and not the exemplary embodiments of the invention described below and as depicted in the Figures.

[0066] EMBEDDED SENSORS, COMMUNICATION NETWORKS AND CONNECTIVITY

[0067] Figure 1 depicts an exemplary electronic device supporting communications to a Network lOOand with embedded sensors according to and supporting embodiments of the invention. Within schematic 100 the Electronic Device 101 supporting Embedded (or Embeddable) Sensor (MBEDSEN) Systems, Applications and Platforms (SAPs) and MBEDSEN-SAP features according to embodiments of the invention is connected. Electronic Device 101 may, for example, be a PED, a FED, or a wearable device and may include additional elements beyond those described and depicted. Also depicted in conjunction with the Electronic Device 101 are exemplary internal and / or external elements forming part of a simplified functional diagram of an Electronic Device 101 within an overall simplified schematic of a system supporting MBEDSEN-SAP features according to embodiments of the invention which include includes an Access Point (AP) 106, such as a Wi-Fi AP for example, a Network Device 107, such as a communication server, streaming media server, and a router. The Network Device 107 may be coupled to the AP 106 via any combination of networks, wired, wireless and / or optical communication links. Also connected to the Network 100 are Social Media Networks (SOCNETS) 190; Government Body 160, Concrete Analysis Environment 165, State Body 170, Building Analysis Environment 175; first and second remote systems 170A and 170B respectively; first and second websites 175A and 175B respectively; first and second 3rd party service providers 175C and 175D respectively; and first and second Servers 190A and 190B, respectively.

[0068] As depicted the Electronic Device 101 may communicate directly with AP 106 as well as with embeddable and / or embedded sensors (MBEDSENs) 180, such as Giatec Scientific’s SmartRock™ and BlueRock™ sensor devices for example, wherein MBEDSENs 180 may be embedded into construction materials at various points in their life cycle such as during their manufacture, deployment, and post-deployment. The inventors describing several MBEDSEN 180 concepts within patent applications including, but not limited to, World Patent Applications PCT / CA2021 / 050141; PCT / 2020 / 050440; PCT / CA2019 / 000057PCT / CA2015 / 000,314 and their corresponding national phase entry applications; and U.S. Patent Applications US 2021 / 0,063,336; US 2021 / 0,382,032; US 2020 / 0,408,707; and US 2021 / 0,033,553 and any corresponding other jurisdictional applications with common priority.

[0069] An MBEDSEN 180 may communicate as depicted (although other configurations not described are supported by embodiments of the invention) by one or more of:• directly with Electronic Device 101;• to Electronic Device 101 via a Wireless Booster / Repeater such as described below in respect of embodiments of the invention;• to a Hub 185 which acquires data from MBEDSENs 180 and pushes it to an endpoint, such a first Server 190A for example, via a Network Device 107 (which may be integrated with the Hub 185) through wired or wireless link(s);• to an AP 106; and• to a Network Device 107.

[0070] An Electronic Device 101 includes one or more Processors 110 and a Memory 112 coupled to Processor(s) 110. AP 106 also includes one or more Processors 111 and a Memory 113 coupled to Processor(s) 110. A non-exhaustive list of examples for any of Processors 110 and 111 includes a central processing unit (CPU), a digital signal processor (DSP), a reduced instruction set computer (RISC), a complex instruction set computer (CISC), a graphics processing unit (GPU) and the like. Furthermore, any of Processors 110 and 111 may be part of application specific integrated circuits (ASICs) or may be a part of application specific standard products (ASSPs). A non-exhaustive list of examples for Memories 112 and 113 includes any combination of the following semiconductor devices such as registers, latches, ROM, EEPROM, flash memory devices, non-volatile random access memory devices (NVRAM), SDRAM, DRAM, double data rate (DDR) memory devices, SRAM, universal serial bus (USB) removable memory, and the like.

[0071] Electronic Device 101 may include an audio input element 114, for example a microphone, and an Audio Output Element 116, for example, a speaker, coupled to any of Processor(s) 110. Electronic Device 101 may include an Optical Input Element 118, for example, a video camera or camera, and an Optical Output Element 120, for example an LCD display, coupled to any of Processor(s) 110. Electronic Device 101 may also include a Keyboard 115 and Touchpad 117 which may for example be a physical keyboard and touchpad allowing the user to enter content or select functions within one of more Applications 122. Alternatively, the Keyboard 115 and Touchpad 117 may be predetermined regions of a touch sensitive element forming part of the display within the Electronic Device 101. The one or more Applications 122 that are typically stored in Memory 112 are executable by any combination of Processor(s) 110. Electronic Device 101 also includes Accelerometer 160 providing three-dimensional motion input to the Processor(s) 110 and GPS 162 which provides geographical location information to Processor(s) 110.

[0072] Electronic Device 101 includes a Protocol Stack 124 and AP 106 includes an AP Stack 125. Within Protocol Stack 124 an IEEE 802.11 protocol stack is depicted but alternatively Electronic Device 101 may exploit other protocol stacks such as an Internet Engineering Task Force (IETF) multimedia protocol stack for example or another protocol stack. Likewise, AP Stack 125 exploits a protocol stack but is not expanded for clarity. Elements of Protocol Stack 124 and AP Stack 125 may be implemented in any combination of software, firmware and / or hardware. Protocol Stack 124 includes an IEEE 802.11 -compatible PHY module that is coupled to one or more Tx / Rx & Antenna Circuits 128 A and an IEEE 802.11 -compatible MAC module which is coupled to an IEEE 802.2-compatible LLC module. Protocol Stack 124 also includes modules for Network Layer IP, a transport layer User Datagram Protocol (UDP), a transport layer Transmission Control Protocol (TCP), a session layer Real Time Transport Protocol (RTP), a Session Announcement Protocol (SAP), a Session Initiation Protocol (SIP) and a Real Time Streaming Protocol (RTSP). Protocol Stack 124 includes a presentation layer Call Control and Media Negotiation module 150, one or more audio codecs and one or more video codecs. Applications 122 may be able to create maintain and / or terminate communication sessions with the Network Device 107 by way of AP 106 and therein via the Network 100 to one or more of Social Media Networks (SOCNETS) 190; Government Body 160, Concrete Analysis Environment 165, State Body 170, Building Analysis Environment 175; first and second remote systems 170A and 170B respectively; first and second websites 175A and 175B respectively; first and second 3rd party service providers 175C and 175D respectively; and first and second Servers 190A and 190B respectively.

[0073] Typically, Applications 122 may activate any of the SAP, SIP, RTSP, and Call Control & Media Negotiation 150 modules for that purpose. Typically, information may propagate from the SAP, SIP, RTSP, Call Control & Media Negotiation 150 to the PHY module via the TCP module, IP module, LLC module and MAC module. It would be apparent to one skilled in the art that elements of the Electronic Device 101 may also be implemented within the AP 106 including but not limited to one or more elements of the Protocol Stack 124, including for example an IEEE 802.11 -compatible PHY module, an IEEE 802.11 -compatible MAC module, and an IEEE 802.2-compatible LLC module. The AP 106 may additionally include a network layer IP module, a transport layer User Datagram Protocol (UDP) module and a transport layer Transmission Control Protocol (TCP) module as well as a session layer Real Time Transport Protocol (RTP) module, a Session Announcement Protocol (SAP) module, a Session Initiation Protocol (SIP) module and a Real Time Streaming Protocol (RTSP) module, and a call control & media negotiation module. Portable electronic devices (PEDs) and fixed electronic devices (FEDs) represented by Electronic Device 101 may include one or more additional wireless or wired interfaces in addition to or in replacement of the depicted IEEE 802.11 interface which may be selected from the group comprising IEEE 802.15, IEEE 802.16, IEEE 802.20, UMTS, GSM 850, GSM 900, GSM 1800, GSM 1900, GPRS, ITU-R 5.138, ITU-R 5.150, ITU-R 5.280, IMT-1010, DSL, Dial-Up, DOCSIS, Ethernet, G.hn, ISDN, MoCA, PON, and Power line communication (PLC).

[0074] The Front End Tx / Rx & Antenna 128A wirelessly connects the Electronic Device 101 with the Antenna 128B on Access Point 106, wherein the Electronic Device 101 may support, for example, a national wireless standard such as GSM together with one or more local and / or personal area wireless protocols such as IEEE 802.11 a / b / g Wi-Fi, IEEE 802.16 WiMAX, and IEEE 802.15 Bluetooth for example. Accordingly, it would be evident to one skilled the art that the Electronic Device 101 may accordingly download original software and / or revisions for a variety of functions. In some embodiments of the invention the functions may not be implemented within the original as sold Electronic Device 101 and are only activated through a software / firmware revision and / or upgrade either discretely or in combination with a subscription or subscription upgrade for example. Accordingly, as will become evident in respect of the description below the Electronic Device 101 may provide a user with access to one or more MBEDSEN-SAPs including, but not limited to, software installed upon the Electronic Device 101 or software installed upon one or more remote systems such as those associated with Social Media Networks (SOCNETS) 190; Government Body 160, Concrete Analysis Environment 165, State Body 170, Building AnalysisEnvironment 175; first and second remote systems 170A and 170B respectively; first and second websites 175A and 175B respectively; first and second 3rd party service providers 175C and 175D respectively; and first and second Servers 190A and 190B respectively.

[0075] Accordingly, within the following description a remote system / server may form part or all of the Social Media Networks (SOCNETS) 190; Government Body 160, Concrete Analysis Environment 165, State Body 170, Building Analysis Environment 175; first and second remote systems 170A and 170B respectively; first and second websites 175A and 175B respectively; first and second 3rd party service providers 175C and 175D respectively; and first and second Servers 190A and 190B respectively. Within the following description a local client device may be Electronic Device 101 such as a PED, FED or Wearable Device and may be associated with one or more of the Social Media Networks (SOCNETS) 190; Government Body 160, Concrete Analysis Environment 165, State Body 170, Building Analysis Environment 175; first and second remote systems 170A and 170B respectively; first and second websites 175A and 175B respectively; first and second 3rd party service providers 175C and 175D respectively; and first and second Servers 190A and 190B respectively.. Similarly, a storage system / server within the following descriptions may form part of or be associated within Social Media Networks (SOCNETS) 190; Government Body 160, Concrete Analysis Environment 165, State Body 170, Building Analysis Environment 175; first and second remote systems 170A and 170B respectively; first and second websites 175 A and 175B respectively; first and second 3rd party service providers 175C and 175D respectively; and first and second Servers 190A and 190B respectively.

[0076] Within embodiments of the invention an Electronic Device 101 may be an MBEDSEN such as MBEDSEN 270 in Figure 2, first to fourth MBEDSENs 270A to 270D respectively in Figure 2 or part of a monitoring system such as Data Acquisition (DAQ) 1140 as depicted in Figure 11 or Truck Sensor 260 as depicted in Figure 2

[0077] Figure 2 depicts an embedded sensor methodology for data logging concrete properties from initial mix through pouring, curing, and subsequently according to embodiments of the invention. As such MBEDSENs 270 in Figure 2, may be added to a construction material batch, e.g., concrete which is loaded onto a concrete truck at the batching plant, within an embodiment of the invention. It is therefore possible to “tag,” i.e., load into, the MBEDSEN information relevant to the mix as well as delivery data etc. This information as well as other measurements made by the MBEDSENs during the transportation, delivery, deployment, etc. can be accessed by a wireless interface by an end user. According to the designof the MBEDSEN the lifetime of the MBEDSEN may be short, e.g., a few days or weeks, moderate, e.g., a few months, or long, e.g., years.

[0078] As such the tagging of the MBEDSENs may include, but not be limited to, information such as batch identity, truck identity, date, time, location, batch mix parameters, etc. but also importantly information such as the maturity calibration curves for the mix established by the material manufacturer. Accordingly, depending upon the degree of complexity embedded into the MBEDSEN such data may be either retrieved for remote storage and subsequent use or it may be part of the MBEDSENs processing of electrical measurement data such that calibration data of the construction material is already factored into the data provided by the MBEDSENs. Accordingly, the MBEDSENs 270 may be added to the construction material, e.g., concrete, at the batching point 210 either tagged already or tagged during loading, depicted as first MBEDSEN 270 A. Subsequently upon delivery and pouring 220 the MBEDSENs may be read for information regarding the delivery process etc., depicted as second MBEDSEN 270B, or they may be added at this point, e.g. to formwork, rebar etc.

[0079] Once poured the MBEDSENs may be read for curing information 230, depicted as third MBEDSEN 270C, and then subsequently, depending upon the battery - power consumption etc., periodically read for lifetime data 240 of the concrete, depicted as fourth MBEDSEN 270D. In each instance the acquired data may be acquired wirelessly and stored on a user's PED or it may then be pushed to a Network 100 and therein to one or more servers 290. For devices wireless interrogating the MBEDSENs these may be executing a software application which presents to the user concrete parameter data either as provided from the MBEDSEN(s) directly using the calibration curves stored within or upon the device using calibration curve data stored within the MBEDSEN but not processed by it, stored within the device, or retrieved from the data stored upon the remote server 290, such as first and second Servers 190A and 190B, respectively of Figure 1.

[0080] MBEDSEN 270 may, within embodiments of the invention, be enabled through a wireless signal, a vibration exceeding a threshold, via an electrical circuit being completed upon removal of a sensor cable which incorporates a magnetic element within the sensor head removed from a housing of MBEDSEN 270, increase in humidity beyond a threshold, decrease in light, etc. Accordingly, the embodiments of the invention support tagging the sensors and embedding the maturity calibration curves in the sensor. These curves may for example be mixspecific and depending on the temperature history of the concrete can be used to estimate the strength of the construction material, e.g., concrete. By embedding them within the sensors andthe sensors employing this data the construction material manufacturer does not need to release commercially sensitive information such as their proprietary mix and calibration curves.

[0081] Based upon the combination of MBEDSENs within the construction material and their wireless interrogation and mobile / cloud-based software applications other technical enhancements may be implemented, including for example:• Weather forecast API, such that the ambient temperature prediction in conjunction with current construction material data can be used to predict / project the strength identifying quality problems earlier;• Automatic detection of construction material pouring time, e.g., from electrical connection once the concrete is poured or changes in the pressure, humidity, light etc.;• Tagging the sensor using NFC with smartphone;• Data integrity and management on remote servers;• Data analytics and / or artificial intelligence on data analysis as the MBEDSEN manufacturer may acquire data from a large number of job sites allowing additional analytics, reporting, alarms etc.;• AN MBEDSEN manufacturer may establish so-called “big data” on construction material properties and construction material curing cycles / processes across a large number of job sites, geographic regions, time frames etc. allowing them to provide feedback from their server-based processes to the end user;• Push notifications, such as for example the formwork company is notified when it is time to remove the formwork based upon actual concrete curing data; and• Heat optimization wherein for example closed loop feedback of the temperature history and strength development can be employed to optimize heating employed in cold climates to ensure the concrete slabs gain sufficient strength within a specific period.

[0082] In addition to measuring, for example, temperature, DC electrical conductivity, and AC electrical conductivity it would be evident that additional parameters as discussed and described supra in respect of embodiments of the invention may be measured and monitored, including, but not limited to, concrete moisture content, concrete internal relative humidity, concrete pH, concrete mixture consistency, concrete workability (slump), concrete air content, hydraulic pressure, segregation, cracking, penetration of external ions into concrete, dispersionof fibers, and dispersion of chemical additives and efficacy / reactivity of supplementary cementitious materials.

[0083] It would also be evident that the acquisition of data relating to multiple construction materials, e.g. a concrete mix, also allows for optimization of a concrete mix as a discrete process for a manufacturer as opposed to the determination of a mix design for a specific project as described and depicted in Figure 3A. It would also be evident that the exemplary process described and depicted with respect to Figure 3A may be applied to continuous monitoring and optimization of a mix during its production. Such an exemplary process flow is depicted in Figure 3A for optimizing a manufacturing specification for a construction material according to an embodiment of the invention exploiting machine learning and artificial intelligence comprising first to seventh blocks 310 to 370 respectively, these being:

[0084] First block 310 wherein a user can select a concrete mix design;

[0085] Second block 320 wherein the concrete mix elements are established such as cement content, water content, admixture content and type, fine aggregate content and type, and coarse aggregate content and type;

[0086] Third block 330; wherein the performance data and history for the selected mix are extracted from the stored data within the remote servers which can comprise the data acquired from embedded sensors, partially embedded sensors, third-party sources such as environmental data etc., as well as data established at the time of concrete mix production and transportation;

[0087] Fourth block 340 wherein the extracted performance data and history are analysed to extract different properties of the concrete such as strength, resistivity, slump, temperature, ion content, cracking etc.

[0088] Fifth block 350 wherein artificial intelligence (Al) I machine learning (ML) algorithms and / or processes are employed to process the extracted data;

[0089] Sixth block 360 wherein the analysis performed by the AI / ML algorithms is assessed to establish the influence of mix design parameters on the performance of the concrete mix as variations in mix preparation, mix transportation, deployment, life cycle etc. can be determined and / or evaluated; and

[0090] Seventh block 370 wherein amendments to the concrete mix can be determined to optimize the mix such as for improved long term strength, reduced chloride ions, reduced time before form work removal, reduced impact of ambient environment etc.

[0091] The process described and depicted in respect of Figure 3A may be fully automated or it may require user input such as identification of which aspects of performance of the mixare to be assessed I optimized. Further, the analysis may be filtered such as for geographic location, season, type of infrastructure element, etc.

[0092] It would be evident that the exemplary process depicted in Figure 3A with respect to second Block 320, relating to concrete mix elements (such as cement content, water content, admixture content and type, fine aggregate content and type, and coarse aggregate content and type for example), and fourth Block 340, relating to extracted performance data and history (such as strength, resistivity, slump, temperature, ion content, and cracking for example) may exploit data acquired during different portions of the construction material life cycle such as production, delivery, deployment, and ongoing life post-completion of the construction phase.

[0093] However, it would be evident that other process flows for optimizing one or more aspects of the construction material life cycle and / or raw material(s) employed within the construction material may exploit data acquired from sensors embedded within the construction material, such as MBEDSEN 270 for example, as well as other sensors associated with different life cycle points may be employed according to or exploiting embodiments of the invention exploiting machine learning and artificial intelligence. For example, referring to Figure 3B there is depicted an exemplary schematic of extended sensor integration within a concrete production facility in conjunction with embedded sensors monitoring one or more portions of a subsequent life cycle of the construction material for optimizing a mixture for a construction material according to an embodiment of the invention.

[0094] Within Figure 3B there is depicted a schematic of a concrete plant (Plant 300) (also known as a concrete mixing plant or concrete batching plant) wherein additional sensors are incorporated to provide additional data to embodiments of the invention exploiting machine learning and artificial intelligence, including but not limited to, those depicted in Figure 7Afor concrete or other construction materials.

[0095] Accordingly, within Plant 300 of Figure 3B a Truck 3060 is depicted to transport a concrete mixture (hereinafter a “mix”) produced by Mixer 3050 according to a mixture specification (mix specification). The Mixer 3050 being fed with the raw materials comprising, for example, from one or more Aggregates 3010, Waster 3020, and Cement 3030. Optionally, one or more binders or admixtures may be added from further storage Binding and Admixtures 3040 may be added at this initial mixing rather than subsequently. Multiple silos may be provided within Aggregates 3010 to store different aggregates, aggregate grades, etc. whilst Cement 3030 may similarly store one or more cements within multiple silos. In addition to the Water 3020 a Recycled Water 3070 storage tank may be provided which is fed from the Mixer3050 or a stage after the Mixer 3050 wherein the linkage(s) to the Recycled Water 3070 from the Mixer 3050 and / or other stages are not depicted for clarity.

[0096] Accordingly, within an embodiment of the invention the Plant 300 may comprise one or more sensors in addition to the exploitation of embedded sensors within one or more of the Aggregates 3010, Water 3020, Cement 3030, and Admixtures 3040 or added to the Mixer 3050 at the production of the batch of construction material to be provided to the Truck 3060. These additional sensors may include, but not be limited to:• Aggregate Sensors:• Temperature sensor(s) per silo (and / or feed to Mixer 3050) to monitor temperature of the aggregate during storage and / or delivery to the Mixer 3050;• Moisture sensor(s) per silo (and / or feed to Mixer 3050) to monitor moisture level(s) of the aggregate(s) during storage and / or delivery to the Mixer 3050;• Water Sensor(s):• Temperature sensor(s) to monitor temperature of the fresh water, Water 3020, during delivery to the Mixer 3050;• Chemical sensor(s) to monitor one or more chemicals to determine their presence and / or level during delivery of the fresh water, Water 3020, to the Mixer 3050;• Ion sensor(s) to monitor one or more ionic species to determine their presence and / or level during delivery of the fresh water, Water 3020, to the Mixer 3050;• Binder and Admixture Sensors:• Temperature sensor(s) to monitor temperature of the binder(s) and / or admixture(s), Binders and Admixtures 3040, during storage and / or delivery to the Mixer 3050;• Chemical sensor(s) to monitor one or more chemicals to determine their presence and / or level within the binder(s) and / or admixture(s), Binders and Admixtures 3040, during storage and / or delivery to the Mixer 3050;• Ion sensor(s) to monitor one or more ionic species to determine their presence and / or level within the binder(s) and / or admixture(s), Binders and Admixtures 3040, during storage and / or delivery to the Mixer 3050;• Mixer Sensor(s):• Temperature sensor(s) to monitor temperature of the mixture during mixing and / or delivery to the Truck 3060;• Chemical sensor(s) to monitor one or more chemicals to determine their presence and / or level during mixing and / or delivery of the mixed mixture to the Truck 3060;• Ion sensor(s) to monitor one or more ionic species to determine their presence and / or level during mixing and / or delivery of the mixed mixture to the Truck 3060;• Moisture sensor(s) to monitor the moisture level during mixing and / or delivery of the mixed mixture to the Truck 3060;• Recycled Water Sensor(s):• Temperature sensor(s) to monitor temperature of the recycled water, Recycled Water 3070, during delivery to the Mixer 3050;• Chemical sensor(s) to monitor one or more chemicals to determine their presence and / or level during delivery of the recycled water, Recycled Water 3070, to the Mixer 3050;• Ion sensor(s) to monitor one or more ionic species to determine their presence and / or level during delivery of the recycled water, Recycled Water 3070, to the Mixer 3050; and• Density sensor(s) to monitor the density of the recycled water, Recycled Water 3070, to the Mixer 3050 wherein the density can be employed to establish a level of additional solids present within the recycled water fed to the Mixer 3050.

[0097] Further, as described below the load within the Truck 3060 may be monitored during loading, transportation and delivery by embedded sensors, such as MBEDSEN 270 in Figure 2, together with load and slump monitoring sensors which form part of the Truck 3060 as described and depicted below with respect to embodiments of the invention.

[0098] Now referring to Figure 3C there is depicted a schematic System 3000 of an overall system according to embodiments of the invention for providing continuous mix design optimization of a material, in this instance concrete. As depicted the System 3000 employs one or more Al / ML Algorithms 3110, which executes upon a computer system and accesses one or more Databases 3170 that store information acquired from a number of sources. These sources may include, but not be limited to, the Concrete Plant 3120 where data relating to aspects of the Concrete Plant 3120 is acquired and pushed or pulled to the Databases 3170.This may include, for example, data relating to aggregate moisture and grading, cement performance, material density, material temperature and raw materials and other data such as described with respect to Figures 3 A and 3B, for example. The Databases 3170 also contain data acquired from Batch-Delivery 3130 relating to the batching and delivery of concrete from the Concrete Plant 3120 such as batch data across multiple batching plants, dispatch data, order tracking, GPS identifiers of trucks (and GPS data) as well as load status of delivery trucks leaving / returning to the Concrete Plant 3120.

[0099] Further, the Databases 3170 contain data from Truck Systems 3140 such as GPS data with time (for route tracking, time tracking, time stamping etc.), data from water and / or admixture dispensing systems on the trucks, and data relating to the concrete within the drum of the truck such as slump (as obtained by the embodiments of the invention described below), air content and water content.

[0100] The Databases 3170 also acquire Third Party Data 3150 using application programming interfaces (APIs) such as weather forecasts, weather stations (for actual weather data), and traffic data (either for dynamic routing / mix adjustment or analysis of deliveries etc.). Further an API may communicate with one or more laboratories performing tests, analysis, evaluations etc. upon concrete samples, raw materials, etc.

[0101] The Databases 3170 also acquire data from Construction Jobsites 3160 which may include, but not be limited to, data from embedded sensors such as described within this specification and others for temperature, strength, ion content, moisture etc. as well as data relating to pour time, pour location, framing, re-bar layout, etc.

[0102] Accordingly, the Databases 3170 comprise data acquired through the supply chain life cycle from delivered raw materials to the Concrete Plant 3120, production of the concrete (via Batch-Delivery 3130 and Concrete Plant 3120 for example), delivery information (via Batch-Delivery 3130 and Truck Systems 3140 for example), Third Party Data 3150, and deployment (via Construction Jobsites 3160 which includes embedded sensors).

[0103] As a result the Al / ML Algorithms 3110 can analyse data to provide data to one or more aspects of the system directly or to be employed in adjusting one or more systems. For example, the Al / ML Algorithms 3110 may provide continuous optimization of the concrete mixes produced by Concrete Plant 3120 and Batching System 3130, adjustments for raw material acceptance criteria, and timing data to Batching System 3130 for scheduling deliveries.

[0104] The Al / ML Algorithms 3110 may also provide adjustments to thresholds for dispensing systems, such as for water or admixtures, forming part of Truck Systems 3140, orwhere real time concrete data is accessible from the Truck Systems 3140 (either forming part of the truck directly or communicating with embedded sensors within the wet concrete) for real time dynamic adjustment and / or decision making.

[0105] The Al / ML Algorithms 3110 may also dynamically provide delivery acceptance at Construction Jobsite 3160 for a delivery or it may adjust the deployment of a delivery, e.g. a delivery scheduled for a support pillar is now more suited to pouring a floor at the same jobsite or another jobsite.

[0106] Within another embodiment of the invention the Al I ML Algorithms 3110 may in conjunction with data from Truck Systems 3140, and as also described below with respect to truck monitoring systems, determine that the remaining concrete after a delivery at one jobsite can be directly transported to another jobsite.

[0107] Within another embodiment of the invention the Al I ML Algorithms 3110 may in conjunction with data from Truck Systems 3140, and as also described below with respect to truck monitoring systems, determine that the remaining concrete after a delivery at one jobsite may be combined with an additional batch of concrete loaded into the truck when the truck returns where the Concrete Plant 3120 and Batching System 3130 generate a batch either to an existing recipe or to a custom recipe established in dependence upon a target concrete batch comprising the new load and the returning load.

[0108] Within another embodiment of the invention the Al I ML Algorithms 3110 may, by virtue of knowing the volume of concrete and performance of the concrete dynamically offer the load to one or more Construction Jobsites 3160, building contractors, etc. For example, various entities seeking smaller volumes of concrete with increased flexibility of pouring time / location may establish their requirements within a database accessible to the Al / ML Algorithms 3110. Accordingly, the Al I ML Algorithms 3110 may establish a returning truck may have a specific volume of a mix and match this to one or more requirements within the database, communicate the current immediate availability to those posting the one or more requirements, and upon receipt of an acknowledgement within a defined period of time re-route the truck(s) with this remaining concrete to the other locations. Further, by virtue of tracking the real time properties of the concrete within the truck the Al I ML Algorithms 3110 may dynamically adjust those to whom the option of a delivery is provided and advise others that due to the delay in acknowledgement the mixture is now no longer available.

[0109] The Al / ML Algorithms 3110 by having an overall perspective of the supply chain for multiple Concrete Plants 3120, Batching Systems 3130, Construction Jobsites 3160 etc. provide one or more dashboards to a user indicating the performance of the supply chain, statusof different elements of the supply chain, performance of different elements of the supply chain, current deliveries etc.

[0110] The Al / ML Algorithms 3110 may also, based upon knowledge of the projected requirements, provided by an order fulfilment system (not depicted for clarity), projected traffic, projected weather etc. may establish appropriate schedules for the Concrete Plant 3120 and Batching System 3130. Where the Al / ML Algorithms 3110 perform these functions for multiple Concrete Plants 3120 and Batching Systems 3130 then the Al I ML Algorithms 3110 may schedule concrete production and delivery to optimize aspects of the supply chain, such as limiting the number of changes to mixes made by a plant, adjusting delivery schedules, truck routing etc.

[0111] The Al / ML Algorithms 3110 may also trigger adjustments to raw material specifications, raw material delivery schedules or even raw material delivery locations. For example, assessment of sand being shipped may be more appropriate to mixes being scheduled at one Concrete Plant 3120 and / or Batching System 3130 than another, and hence the deliveries may be routed according to projected mixes and raw material quality assessments.

[0112] As noted above the inventors have established techniques for determining concrete quality and properties from initial manufacture or loading into a truck to its delivery. Further, AI / ML processes have been established to modify concrete mixes before manufacturing or during transport etc., establish concrete mixes in dependence upon target performance and deployment location, timing etc. Accordingly the inventors refer to this by the term SmartMix™ to denote its combination of AI / ML processing for concrete mix determination, optimization and feedback control. These same techniques can also be applied in monitoring the concrete quality and properties remaining within a truck post-delivery returning to a batching or manufacturing location which may be the same as that the original load was from or another location. Accordingly, the inventors refer to this system as Dispatch Co-Pilot™ which provides for concrete re-allocation and real-time mix adjustment.

[0113] Accordingly, embodiments of the invention disclosed herein provide for a system and method for optimizing the use of leftover concrete in ready-mix trucks by integrating realtime data acquisition, intelligent decision-making, and dispatch coordination. The system leverages embedded sensors (e.g., temperature, slump, volume, air and maturity) and wireless connectivity to monitor the physical and chemical properties of the concrete remaining after delivery during its transit back to a plant or after partial unloading.

[0114] Upon detecting surplus concrete, the system evaluates its suitability for reuse based on project-specific quality thresholds, time since mixing, and environmental conditions as well as current requirements established from customers which may be an initial delivery or a part of an overall larger requirement with current deliveries, prior deliveries or deliveries to be made subsequently. An Al model trained on historical performance, material behavior, and delivery constraints is employed to automatically identify an outcome. Such outcomes may include alternate delivery matching, top-up recipe adjustment, direct re-use or dumping.

[0115] Alternate Delivery Matching: This outcome is where the system recommends an alternate jobsite within a defined delivery radius where the remaining concrete can be delivered without compromising performance specifications. This is determined using real-time jobsite demand, geographic proximity, and compatibility with required mix class etc. The alternate delivery may be established without the truck returning to a plant, with the truck topping up water enroute, with the truck returning to a plant for different admixtures and / or water where the plant is the same or different to that from which the load remaining was loaded, with the truck returning to a plant for additional concrete of a different mixture to be combined with the load remaining or with the truck returning to a plant for additional concrete of the same mixture to be combined with the load remaining. The concrete specification / requirements for the subsequent delivery may be relaxed relative to the initial delivery. For example, the initial delivery may be for a structure designed to support a target load / stress / strain whereas the subsequent delivery has lower load / stress / strain requirement.

[0116] Top-Up Recipe Adjustment: Aspects of top-up recipe adjustment overlap the alternate delivery matching in that the system calculates an additive recipe (e.g., additional cement, water, admixtures, aggregates) that modify the existing concrete to meet the requirements of a new or existing order. The recommended adjustments are provided to the plant or truck-mounted dosing system, enabling real-time transformation of the remaining mix. Top-up delivery requires the truck to return to a plant whereas aspects of alternate delivery do not require the truck to return to a plant.

[0117] The Dispatch Co-Pilot™ system would interface with other software systems such as fleet management software, plant batching systems and quality control platforms allowing Dispatch Co-Pilot™ to automatically or in combination with human dispatchers or other automated systems to establish data-driven routing or remixing decisions in real-time. Accordingly, the Dispatch Co-Pilot™ system allows for improvements in material efficiency,reducing concrete waste and carbon footprint as well as enhancing responsiveness in dynamic delivery environments.

[0118] Optionally, a variant process may be implemented such as depicted in Figure 4 wherein analysis is performed in respect of transportation of the construction material, e.g. concrete mix. In many concrete mix designs and deployments one or more admixtures are added to the concrete. These may be added at various points including, but not limited to, concrete batching, in truck, during deployment, and after deployment. Accordingly, Figure 4 depicts an exemplary process for assessing admixtures, water etc. both in terms of which to add to the construction material based upon acquired historical data relating to their addition, delivery, performance etc. also determine when to add a particular admixture to a construction material batch and the quantity to add. For example, the analysis may determine that an admixture improving the low temperature pouring characteristics and initial curing of concrete is best added thirty minutes prior to pouring. Further, as this may be problematic for some or all deliveries the admixture(s) may be preloaded into one or more dispensers which are automatically triggered based upon downloading of a program to the concrete truck from the database for a specific delivery batch. In this manner, the admixture(s) are automatically added rather than when the truck driver can stop and add them. Equally, such analysis may determine that a batch having been loaded for two hours reaches a point where subsequent deployment will result in reduced performance or that the current projected environmental conditions will require all loads to be poured within a predetermined period of time if the concrete is required as a single contiguous block rather than multiple layers as a second pour is made upon a curing previous pour etc.

[0119] Accordingly, the exemplary process flow comprises first to seventh blocks 410 to 470 respectively, these being:

[0120] First block 410 wherein data acquired from in-truck and in-concrete sensors such as described above is collected and stored within the one or more remote servers storing information relating to the sensors as well as that established from concrete batch manufacturing plants, sensors embedded within the infrastructure elements, semi-embedded sensors associated with infrastructure elements, etc.;

[0121] Second block 420 wherein data relating to the mix transported for which data exists at the various points such as batching, truck loading, pouring, curing, ongoing life cycle monitoring etc. are retrieved and associated with the in-truck and inconcrete sensor data;

[0122] Third block 430 wherein the fresh concrete properties such as temperature, slump, air content, setting time etc. are retrieved and associated with the data existing at the various points such as batching, truck loading, pouring, curing, ongoing life cycle monitoring etc. are retrieved and associated with the in-truck and inconcrete sensor data;

[0123] Fourth block 440 wherein a plurality of artificial intelligence (Al) / machine learning (ML) algorithms and / or processes are employed upon the data in conjunction with data from other sources such as weather conditions and weather projections extracted from fifth block 450;

[0124] Sixth block 460 wherein the analysed effects of the mix constituents on the fresh concrete properties are established against the fresh concrete properties; and

[0125] Seventh block 470 wherein optimizations of the mix design, admixture dosage and time, water additions etc. are established.

[0126] Each of the exemplary processes described and depicted above exploits the acquisition of data from embedded sensors within the infrastructure.

[0127] A method of monitoring concrete slump and adjusting the concrete during transit and delivery would provide significant benefits to concrete producers in terms of reducing concrete load rejection, reducing disposal of unused / rejected concrete, reducing use of water / admixture adjustments, reducing cementitious materials and subsequently significant cost savings. Reductions in concrete load rejection would also benefit those to users of the concrete by reducing production delays during concrete pours or reworking of poured concrete to correct for material quality issues.

[0128] Within the prior art two methods have been generally employed to monitor slump in transit. The first method relies upon measuring the rotation and torque applied to the mixing drum from which the Bingham parameters (primarily yield stress) of the mixture can be determined. Such a method being described within US Patent 8,020,431 “Method and System for Calculating and Reporting Slump in Delivery Vehicles.” Concrete is a non-Newtonian fluid whose rheological properties are represented by a Bingham model where parameters that represent the flow properties of the material are T0and p where the flow equation of concrete is defined in the Bingham model by where p is slope of rate of shear rate ( y) with shear stress (p) and T0the minimum stress value below which the concrete will not flow.

[0129] The other method relies upon a probe which is installed on the inner wall of a drum of a concrete delivery truck where deformation of the probe as the drum rotates is monitoredand used to calculate a resistance pressure. From this resistance pressure the Bingham parameters may be deduced. This being described within US Patent 9,199,391 “Probe and Method for Obtaining Rheological Property Value” for example.

[0130] However, these aforementioned methods are invasive, costly, require significant adjustments to the drum / truck system to operate and require significant maintenance. Previously, within PCT / CA2019 / 000057 “Construction Material Assessment Method and Systems” some of the inventors established a technique employing a sensor monitoring electrical resistivity wherein this exhibits a time dependent response where the electrical resistivity is high when the sensor is in air and low when the sensor is within the concrete material. Accordingly, based upon the frequency, amplitude, phase shift (between the resistivity fluctuation cycles and the drum revolution cycles), and duty cycle of the electrical resistivity measurement the inventors established yield stress, plastic viscosity and slump for the concrete within the drum. Whilst non-invasive the method still requires a sensor inside the drum.

[0131] However, the inventors wished to establish a method wherein the slump can be monitored non-invasively without accessing the drum internally or the drum systems, including the engine. The benefits being significantly reductions in the cost of installation and maintenance of such a system. Accordingly, the inventors established a method, as depicted in Figure 5 in first to third Schematics 500A to 500C, wherein a Sensor Module 520 is disposed upon the exterior surface of the Drum 510 as depicted in first Schematic 500 A in Figure 5. The method established by the inventors monitors the acceleration and angular velocity of the Drum 510 through an externally installed gyroscopic sensor within the Sensor Module 520 which determines the acceleration, angular velocity and orientation of the drum. For example, the Sensor Module 520 may be permanently attached to the Drum 510 or it may be demountably attached to the Drum 510. For example, the Sensor Module 520 may be magnetically attached to the Drum 510. Whilst the Sensor Module 520 has been depicted upon the exterior of the Drum 510 it would be evident that within other embodiments of the invention the Sensor Module 520 may be within the Drum 510 or form part of the Drum 510.

[0132] Accordingly, a system comprising a multi-axis accelerometer sensor provides data that is converted by a microprocessor to changes in angular velocity (i.e. changes in revolutions per minute (RPM) of the Drum 510). These RPM changes in the Drum 510 may, for example, be forced by the operator of a system (e.g. truck) associated with the Drum 510 or caused by agitation of the mixture within the Drum 510. Within another embodiment of the invention theRPM changes may be established under signals from a control module associated with the system, e.g. in the rotation

[0133] The system generates in dependence upon measured angular velocity the torque exerted by the engine driving the Drum 510. The time required to establish a change in RPM or angular velocity of the drum is related to the torque applied on the drum, the drum’s diameter, the weight of the drum and the properties of the concrete including its weight. Further, by determining the engine’s torque at several RPM levels, the yield stress of the Bingham fluid can be determined and used to calculate the concrete slump through defined correlations. The relationship between the torque at different RPMs is used to calculate yield stress by extrapolating the relationship to the torque required at 0 RPM.

[0134] For example, during routine rotation of the drum during transport a controller may cycle the drum through two or more rotation rates automatically. Based upon the sensor measurements the system can then automatically determine the yield stress etc. This data can then be pushed to a remote server for storage and / or processing such as described within this specification or employed to provide a driver or other operator / user associated with the drum data relating to the condition of the concrete. Optionally, the overall system may only trigger communications to a user / operator etc. based upon determining that a property of the concrete has gone without predetermined boundary conditions thereby requiring that an action be taken, e.g. addition of water to the concrete mixture, addition of an additive to the concrete mixture, termination of the delivery etc.

[0135] The inventors have extended the system described above to a three-component system which in addition to determining slump also allows concurrent determination of load size during delivery of concrete in a ready -mix concrete truck. The system contains:• a Sensor Module 520 installed on the outside of the drum either permanently attached or demountable attached (e.g. magnetically fixed) which monitors angular velocity, positioning and acceleration / tilt;• a Hydraulic Sensor 530 to monitor the drum’s hydraulic pressure; and• a Data Acquisition (DAQ) 540 system installed upon the truck, for example within the truck cabin.

[0136] An example of the Hydraulic Sensor being depicted in second Schematic 500B in Figure 5 which is coupled to the hydraulic system of the drum. An example of the DAQ being depicted by DAQ 540 in third Schematic 500C in Figure 5 where the DAQ 540 connects to the truck power through an interface such as the Connector 550 depicted.

[0137] This system of Sensor Module 520, Hydraulic Sensor 530 and DAQ 540 provides for dynamic measurement of the concrete’s slump within the drum. The method is based upon monitoring the hydraulic pressure of the drum before and after concrete introduction (i.e. before and after concrete batching). This difference in pressure, related to the introduced concrete, is monitored over a range of angular velocities. Accordingly, from the angular velocity and the hydraulic pressure the shear rate and shear stress applied to the concrete can be monitored.

[0138] For example the measurement data is applied to one or more models, which may include for example a Herschel-Bulkley model or Bingham model, depending upon the concrete constituents, to determine the yield stress, viscosity / consistency and flow index of the concrete. The yield stress is then used to determine the slump of the concrete under consideration.

[0139] Accordingly, the inventors system allows the determination of slump in a manner that is non-intrusive, can be performed during transportation of the concrete or upon the concrete at the delivery location prior to pouring. This provides significant advantages compared to prior art methods that require significant intrusion, installation and maintenance. Data from this system can be used along with the concepts defined within this specification to exploit AI / ML processes that allow for processes, models and algorithms to be established and dynamically employed for adjusting mixes based on raw material variation

[0140] The inventors also have established that measurements of hydraulic pressure and angular acceleration can be used to determine load size (or the center of mass) of the system under consideration. Knowing that the net torque acting on concrete drum is directly proportional to the angular acceleration and moment of inertia / center of mass of the concrete mass then by monitoring changes in hydraulic pressure and the associated changes in angular velocity (i.e. angular acceleration) within an assumed system efficiency the concrete load size can be determined. Whilst the system efficiency for improved accuracy should be considered as truck dependent it can be deduced from measurements taken of the empty truck and hence can be defined per truck.

[0141] Accordingly, the hydraulic pressure acting on the drum is monitored using a pressure gauge, e.g. Hydraulic Sensor 530, and the acceleration of the system is monitored, e.g. using a Sensor Module 520. The data from both systems are acquired by the data acquisition system, DAQ 540. The changes in acceleration and changes in pressure are correlated to yield the center of mass / moment of inertia / load size of concrete within the concrete drum. This may be performed by the DAQ 540 or remotely.

[0142] Accordingly, this system allows concrete producers to determine the volume of concrete not only initially loaded into the truck, to ensure compliance with vehicle regulatory requirements on maximum weight, for example, it also allows for the volume of concrete being rejected or returned to a concrete plant after the truck has reached the delivery location to be determined along with the slump of the concrete being returned. This, as noted above, allows AI / ML processes to either adjust the returned concrete to meet specific criteria for a different concrete at the same jobsite or another jobsite, re-direct the rejected concrete to a different jobsite or different pour at the same jobsite to avoid waste and increase material utilization, or configure an additional mixture to add to the returned material to meet other specific criteria of a further pour required at the jobsite or a further jobsite.

[0143] Figure 6 depicts methods of obtaining a load of a construction material within a delivery vehicle (vehicle, commonly referred to as a truck, or concrete truck where the construction material is concrete) at any point in time according to one or more embodiments of the invention. Within the embodiments of the invention described above with respect to AI / ML Algorithms for construction material definition, monitoring, and formulation it is evident that monitoring and adjusting the concrete during transit and delivery would provide benefits to producers in terms of reducing load rejection, reducing disposal, reducing use of water / admixtures, reducing cementitious materials etc. Whilst as discussed above slump measurements provide data relating to the concrete and that MBEDSENS within the concrete load may also provide additional information or information without concurrent slump measurements it would be evident that load information would also be required in order to verify initial batch loading independent of the batching at the concrete plant, e.g. Batch- Delivery 3130 in Figure 3, verify load poured and provide data with respect to the load remaining for establishing subsequent adjustments for subsequent deliveries from the truck or for a subsequent load to be added at the concrete plant for combination with the original load.

[0144] Accordingly, the inventor have established a series of load measurement and / or monitoring systems for the concrete load within the drum of the concrete truck (truck). Image 600 in Figure 6 depicts these including:• Measure Axle Strain 610 as described and depicted in respect of Figure 8;• Measure Deflection between Body and Wheel via String Potentiometer 602(1); RideHeight Sensor 602(2); Shock Absorber Deflection 602(3) and Leaf Spring Rotation for example as described and depicted in respect of Figures 9 to 12 respectively;• Leaf Spring Strain 603 as described and depicted in respect of Figure 13;• Drum Resonance 604 as described and depicted in respect of Figure 14;• Helmholtz Resonator 605 as described and depicted in respect of Figure 15;• Contact Force 606 as described and depicted in respect of Figure 16;• Thermal Mass Flow Meter 607 as described and depicted in respect of Figure 17;• Truck Measurements 608 as described and depicted in respect of Figure 18;• Suspension Resonance 609 as described and depicted in respect of Figure 19;• Fill Level 610 as described and depicted in respect of Figure 20;• Static Pressure 611;• Drum Deflection 612 as described and depicted in respect of Figure 22; and• Drum Load 613 as described and depicted in respect of Figure 7.

[0145] Whilst the emphasis within the following description is with respect to systems that can be retro-fitted to an existing truck as well as integrated into a new truck build it would be evident that other variants of embodiments of the invention may be integrated into a new truck design whilst not being compatible with retro-fitting existing trucks.

[0146] Now referring to Figure 7 there are depicted first and second Schematics 700 A and 700B of obtaining the load of the construction material within a container (e.g. drum) of the truck via the load applied to the truck according to an embodiment of the invention. First and second Schematics 700A and 700B depicting plan and side elevations of a Truck 710 with Drum 720. As depicted first to fourth Sensors 730A to 730D are depicted coupled to a Hub 740. Hub 740 may for example be DAQ 540 as depicted in Figure 5 or it may be another PED forming part of the Truck 710 or supported by the Truck 710.

[0147] Hub 740 communicates via one or more wireless interfaces according to one or more wireless standards to a remote server, such as first and second Servers 190 A and 190B, respectively in Figure 1 and therein the data transmitted from the Hub 740 is accessible to one or more applications including those as described and depicted with respect to Figures 2 to 5 respectively. Optionally, the Hub 740 may communicate via one or more wired interfaces to a PED or FED and therein to a remote server. A PED, for example, may be associated with the Truck 710 or a driver of the Truck 710 etc.; The PED or FED may be associated with the batching plant, construction site etc.

[0148] Optionally, the Hub 740 may push data to these applications or respond to pulls from these applications wherein it provides all of the acquired data or a processed summary of the acquired data or a subset of the acquired data. Where the Hub 740 provides a processed summary of the acquired data or a subset of the acquired data this may be via one wirelessstandard supporting long range communications and the Hub 740 may subsequently provide all of the acquired data via a second wireless standard supporting short range communications when the Truck 710 returns to a batching plant, e.g. Mixer 3050 in Figure 3B or Batch-Delivery 3130 in Figure 3C. Within Figures 8 to 22 a Hub 740 is described and depicted with respect to a load measurement system discretely. However, it would be evident that a Hub 740 may be discretely employed or employed with other systems with their own Hubs 740 wherein these each push or transmit the data to one of more remote server(s) or to an aggregation hub acquires data from the Hubs 740 before pushing it or transmitting it to one of more remote server(s).

[0149] Accordingly, each of first to fourth Sensors 730A to 730D may be a strain sensor or strain gauge attached to a support of the Drum 720. Whilst four sensors are depicted it would be evident that within other embodiments of the invention a different number of sensors may be employed which may be established based upon factors such as position of supports, number of supports etc. The data acquired may be combined with other data acquired by the Hub 740 or combined with other data remotely upon a server or other data pushed to the Hub 740 from a remote server. Within embodiments of the invention the strain gauge may have a long time constant such that rotational factors of the Drum 720 are filtered automatically by the strain gauge or alternatively the strain gauge may have a short time constant such that rotational factors of the Drum 720 are acquired so that additional aspects of the load may be obtained discretely or in combination with other sensors including, but not limited to, a fill sensor or rotational sensor disposed upon the Drum 720.

[0150] Within an embodiment of the invention the Hub 740 may be loaded with calibration routine(s) and / or data such that it can establish the load from the measurements obtained from first to fourth Sensors 730A to 730D. Optionally, the Hub 740 may self-calibrate based upon obtaining measurements during loading a batching plant, e.g. Mixer 3050 in Figure 3B or Batch-Delivery 3130 in Figure 3C, wherein the measurement data is established during a single loading as a function of time / load established by the batching plant or it may be acquired / established over multiple loads.

[0151] Referring to Figure 8 there is depicted a method of obtaining the load of the construction material within the drum of the truck via axel strain monitoring according to an embodiment of the invention. First and second Images 800A and 800B depict a Simplified Schematic (Sketch) and photograph of a deployed system according to an embodiment of the invention. As depicted in first and second Images 800A and 800B a Strain Sensor 820 is disposed upon the Axle 810 of a truck and coupled to a Hub 740 directly or via intermediate elements that provide power, signal conditioning, analog-to-digital conversion, etc. accordingto the design of the Hub 740 and Strain Sensor 820. Accordingly, with a Strain Sensor 820 on each Axle 830 of the truck the load may be established in dependence upon calibration data / processes within the Hub 740 or remote server(s).

[0152] Accordingly, the Strain Sensor 820 is glued, welded or otherwise attached to the Axle 810 and connected to the Hub 740. Whilst a wired interface is depicted in Figure 8 a wireless interface may be employed wherein the Hub 740 may be disposed within wireless range of the Strain Sensor 820 allowing alternate positioning rather than on the Axle 810. Within an embodiment of the invention the Strain Sensor 820 may require temperature compensation wherein in addition to the Strain Sensor 820 a temperature sensor is disposed with the Strain Sensor 820.

[0153] Now referring to Figure 9 there is depicted a method of obtaining the load of the construction material within the drum of the truck via a string potentiometer(s) between a truck bed and truck axle(s) according to an embodiment of the invention. First and second Images 900A and 900B depict a Simplified Schematic (Sketch) and photograph of a potentiometer supporting embodiments of the invention. A string potentiometer is a transducer used to detect and measure linear position and velocity using a flexible cable and spring-loaded spool. Typically, composed of four main parts, a measuring cable, a spool, a torsion spring, and a rotational sensor. Inside the transducer's housing, a stainless steel cable is wound on a precisely machined constant diameter cylindrical spool that turns as the measuring cable reels and unreels. The torsion spring is coupled to the spool and the spool is coupled to the shaft of a rotational sensor (a potentiometer or rotary encoder) such that as the transducer's cable extends along with a movable object, it causes the spool and sensor shafts to rotate thereby generating an electrical signal proportional to the cable's linear extension or velocity.

[0154] Accordingly, in second Schematic 900B a string potentiometer is depicted comprising a Potentiometer Body 920 within which are housed the spool, sensor etc. with or without associated electronics etc. A Cable 940 extends from the spool within the Potentiometer Body 920 and terminates at its distal end with an Attachment 935 which is attached to the object that moves relative to the Housing 920. As depicted in first Schematic 900A the Attachment 935 is attached to a Fitting 930 upon an Axle 910 with the Potentiometer Body 920 attached to the Truck Bed 950 where the Cable 940 thereby extends between the Axle 910 and the Truck Bed 950 such that motion of these elements relative to one another is converted to a sensor output from the string potentiometer.

[0155] In order to support a retro-fitting the Potentiometer Body 920 and Fitting 930 may be magnetically attached to the Truck Bed 950 and Axle 910 respectively. Within otherembodiments of the invention the Potentiometer Body 920 and Fitting 930 may be glued, welded or otherwise mechanically attached to the Truck Bed 950 and Axle 910 respectively. Initial experiments performed by the inventors indicate a deflection range of the Truck Bed 950 relative to the Axle 910 of approximately 45mm between an empty load and a full load of concrete within a concrete truck. Within other embodiments of the invention

[0156] Referring to Figure 10 there is depicted a method of obtaining the load of the construction material within the drum of the truck via ride height sensor(s) according to an embodiment of the invention. Second Image 1000B depicts a Ride Height Sensor (RHS) 1030 whilst first Image 1000 A depicts exemplary first and second Installations 1010 and 1020 respectively. A RHS 1030 has a sensor body attached to the chassis, second Installation 1010, and a rod linked to the axle, wishbone or control arm of the truck, first Installation 1020. Accordingly, when the vehicle is moving or stationary the load results in rotational movement of the rod and therein its movement relative to the sensor body. An RHS 1030 is an angle sensor wherein as the sensor rod moves, an output signal (voltage) is generated that is proportional to its rotation angle. This signal is then transmitted to an electronics unit, e.g. Hub 740 which is not depicted for clarity.

[0157] Now referring to Figure 11 there is depicted a method of obtaining the load of the construction material within the drum of the truck via measuring deflection of shock absorber(s) according to an embodiment of the invention. First and second Images 1100 A and 1100B depict a Simplified Schematic (Sketch) and photograph of a shock Absorber 1110 potentiometer supporting embodiments of the invention. Accordingly, a Sensor 1120, e.g. a strain sensor, strain gauge, string potentiometer or other transducer which allows the movement of the Upper and Lower Absorber Portions 1110A and 1110B respectively relative to one another to be established. This signal from the Sensor 1120 is then transmitted to an electronics unit, e.g. Hub 740 which is not depicted for clarity.

[0158] Within an embodiment of the invention the Sensor 1120 may a slide potentiometer integrated with a linear slider where the ends are attached to the Upper and Lower Absorber Portions 1110A and 1110B respectively via clamps, e.g. metal hose clamps, welding, etc. or are attached directly to the screws that fix the shock absorber to the truck via an adapter piece.

[0159] Referring to Figure 12 there is depicted a method of obtaining the load of the construction material within the drum of the truck via rotational deflection measurements on spring(s) on the axle(s) of the truck according to an embodiment of the invention. As depicted in Image 1200 Sensors 1220 are disposed with respect to the Leaf Springs (springs) 1210 and Axles 1230 of the Truck 710. The Sensors 1220 measure the rotational deflection of the LeafSpring 1210 relative to the Axle 1230 which due to the orientation and placement of the Drum 720 on the Truck 710 will vary with loading of the material within the Drum 720.

[0160] Now referring to Figure 13 there is depicted a method of obtaining the load of the construction material within the drum of the truck via strain measurements on spring(s) on the axle(s) of the truck according to an embodiment of the invention. As depicted in Image 1300 first to third Sensors 1320(A) to 1320(C) are disposed with respect to the first and third Leaf Springs (springs) 1310(A) to 1310(C) respectively upon the axles of the Truck 710. The first to third Sensors 1320(A) to 1320(C) measure the strain / deflection of the first and third Leaf Springs (springs) 1310(A) to 1310(C) respectively. Accordingly, the Drum 720 on the Truck 710 will apply varying load the first and third Leaf Springs (springs) 1310(A) to 1310(C) respectively with loading of material within the Drum 720. Within an embodiment of the invention the first to third Sensors 1320(A) to 1320(C) may require temperature compensation wherein in addition to the first to third Sensors 1320(A) to 1320(C) a temperature sensor is disposed with each of the first to third Sensors 1320(A) to 1320(C).

[0161] Referring to Figure 14 there is depicted a method of obtaining the load of the construction material within the drum of the truck via resonance of the drum of the truck according to an embodiment of the invention. As depicted in Image 1400 an Exciter 1410 and Accelerometer 1420 are attached to a Drum 720 and therein to a Hub 740. The underlying concept being that a resonant frequency of the Drum 720 will vary according to the filling level of concrete and to other extents on other factors such as density of the load, temperature, etc. Accordingly, the Exciter 1410 generates mechanical vibrations within the Drum 720 which are detected by the Accelerometer 1420. If the Exciter 1410 generates vibrations at a range of frequencies then the output of the Accelerometer 1420 may be processed, for example with a Fourier Transform, to identify the dominant resonance frequency which will vary with the loading of the material within the Drum 720. If the Exciter 1410 generates vibrations at a discrete or narrow range of frequencies then Exciter 1410 may be swept over a defined frequency and the output of the Accelerometer 1420 may be processed, for example over time discretely or with multiple Fourier Transforms, to identify the dominant resonance frequency.

[0162] Within another embodiment of the invention the excitation may be achieved sufficiently through the rotation of the Drum 720 which may be varied under control of the Hub 740, for example, so that different excitation conditions are established. Optionally, the Hub 740 may execute both varying rotation rates, pulsed rotations, etc. of the Drum 720 and trigger the Exciter 1420. As varying rotation rate has been employed, as described above in- 1 -respect of Figure 5 for measuring slump of a concrete load, the varying rotation of the Drum 720 may provide slump and load weight measurements.

[0163] Now referring to Figure 15 there is depicted a method of obtaining the load of the construction material within the drum of the truck via employing the drum of the truck as a Helmholtz resonator according to an embodiment of the invention. This concept exploits the varying volume of air within the Drum 720 under varying loading conditions as a Helmholtz resonator. A Helmholtz resonator works in principle with a pipe (forming part of Sensor Assembly 1510 as depicted in Schematic 1500 in Figure 15 which is coupled to Hub 740) connected to a larger air cavity (internal volume of Drum 720) where the air inside the air cavity acts as a spring and the air inside the tube acts as a mass. Together, these form a system which has a resonance frequency fHgiven by Equation (1). As evident the resonant frequency fHvaries with the volume of the air cavity Fo. As the end of the Drum 720 to the rear of the truck is usually open then the Sensor Assembly 1510 should provide for opening / closing of the exit aperture of the Drum 720 wherein the in open position the Drum 720 can be loaded / unloaded and in the closed position the Sensor Assembly 1510 can establish the resonance frequency of the air within the Drum 720. As evident in the insert as the load increases the resonant frequency increases as the air volume decreases. Within another embodiment of the invention the Sensor Assembly 1510 may be a sensor module attached to an opening to the open end of the chute or other non-rotating part of the Drum 720. Within another embodiment of the invention the Sensor Assembly 1510 may be a sensor module attached to the Drum 720 with an opening into the side of the Drum 720.

[0164] Referring to Figure 16 there is depicted a method of obtaining the load of the construction material within the drum of the truck via contact force of the drum to the truck bed of the truck according to an embodiment of the invention. Schematic 1600 in Figure 16 depicts a Load Cell 1610 which is disposed between the Drum 720 and Truck Bed 750 and connected to the Hub 740. The Load Cell 1610 measures the weight of the Drum 720. The Load Cell 1610 may be coupled to the Drum 720 directly or via a support element of the Drum 720. The Load Cell 1610 may be a single load cell or multiple load cells. Where the Load Cell 1610 is coupled to the Drum 720 directly an intermediate buffer may be provided to allow for the system to operate under drum rotation. Alternatively, the Load Cell 1610 may be moved into and out of contact with the Drum 720, e.g. when the Drum 720 is temporarily non-rotating.Finally, the load cell may be placed in between the hydraulic drum motor and the Truck Bed 750.

[0165] In addition to monitoring the load within the drum it would be beneficial to verify the volume or mass of material delivered whilst it is delivered. This may be used in conjunction with the initial loading data from the batching plant to define the remaining load or used in conjunction with these to verify the loading measurements and the difference before / after a delivery. Accordingly, referring to Figure 17 there is depicted a method of obtaining the load of the construction material dispensed via thermal mass flow measurements on chute(s) of the dispensing system according to an embodiment of the invention. First Schematic 1700A depicts deployment of a first Measurement Sensor 1720 A upon a down Pipe 1710A of the dispensing system or s second Measurement Sensor 1720B upon the dispensing Chute 1710B.

[0166] Second Schematic 1700B depicts a system such as may be employed for first Measurement Sensor 1720 where first and second Temperature Sensors 1730A and 1730B are disposed within the pipe before and after (with respect to flow direction) a Heater Element 1760. The Heater Element 1760 being coupled to a Power Source 1750 which is coupled to a Controller 1740 which is also coupled to the first and second Temperature Sensors 1730A and 1730B. Accordingly, based upon the temperature measurements from first and second Temperature Sensors 1730A and 1730B with the Heater Element 1760 active then the relationship between flow rate and cooling effect is employed for direct measurement of the concrete mass flow.

[0167] Third Schematic 1700C depicts a system such as may be employed for second Measurement Sensor 1730 where first and second Temperature Sensors 1780A and 1780B are disposed beneath the Chute 1710B before and after (with respect to flow direction) a Heater Element 1770. Alternate means of measuring the concrete volume dispensed may include, but are not limited to, an ultrasonic flow meter underneath the Chute 1710B, a paddlewheel, etc.

[0168] Referring to Figure 18 there is depicted a method of obtaining the load of the construction material within the drum of the truck via engine power monitoring and acceleration of the truck according to an embodiment of the invention. As depicted in first and second Schematics 1800A and 1800B an Accelerometer 1810 is attached to the Drum 720. Whilst the Accelerometer 1810 is depicted attached to the Drum 720 the Accelerometer 1810 may be disposed upon the Truck 710.

[0169] The underlying principle is that the Truck 710 will accelerate less quickly fully loaded than empty. Accordingly, by measuring the engine power of the Truck 710 and acceleration of the Truck 710 the load status can be established by the Hub 740. TheAccelerometer 1810 when attached to the Drum 720 can be employed with multiple methods of establishing the load as described and depicted within this specification. Within newer trucks engine parameters such as engine revolutions per minute (RPM), fuel consumption etc. can be established from the engine management system of the truck or established from other sensors etc.

[0170] Now referring to Figure 19 there is depicted a method of obtaining the load of the construction material within the drum of the truck via measuring resonance of the truck bed according to an embodiment of the invention. This concept measures the resonance frequency of the truck suspension via first and second Sensors 1910 by acquiring data relating to motion of the Truck 710. The resonance frequency will decrease with growing truck mass via Equation (2) where k is the suspension stiffness and m which is the concrete mass and truck mass. The first and second Sensors 1910 may be accelerometers which acquire motion data and with Fourier transforming the dominant frequency component may be established. c 1 [k f = — - (2)’ 2n m

[0171] Referring to Figure 20 there is depicted a method of obtaining the load of the construction material within the drum of the truck via fill level monitoring within the drum according to an embodiment of the invention. This concepts exploits a Sensor System 2010, e.g. infrasonic, sonic or ultrasonic, for non-invasive determination of a fill level within the Drum 720 as the Drum 720 rotates. The Sensor System 2010 can be determined where the concrete is located inside the Drum 720 as it rotates.

[0172] Now referring to Figure 21 there is depicted a method of obtaining the load of the construction material within the drum of the truck via fill monitoring etc. via a replaced hatch of the drum according to an embodiment of the invention. As depicted in Figure 21 the hatch of the Drum 720 is replaced with Hatch 2110, which may alternatively be installed at initial manufacturing of the Drum 720. The Hatch 2110 provides access to the inside of the concrete Drum 720 which means that the concrete level within the Drum 720 may be established by one or more sensors forming part of the Hatch 2110 or attached to the Hatch 2110 and accessing the inside of the Drum 720 via openings / flanges etc. This may include a sensor measuring hydrostatic pressure while it is located on the bottom of the drum.

[0173] Referring to Figure 22 there is depicted a method of obtaining the load of the construction material within the drum of the truck via measuring drum deflection relative to the truck bed according to an embodiment of the invention. As depicted in Figure 22 a Magnet2210 is disposed upon the Drum 720 whilst a Hall Sensor 2220 is disposed upon the Truck 710 wherein the strength of the interaction upon the Hall Sensor 2220 from the Magnet 2210 is dependent upon the gap between the Magnet 2210 and Hall Sensor 2220. The Hall Sensor 2220 being coupled to Hub 740.

[0174] As described above in respect of Figures 1 to 4 systems relating to the specification, modification and management of a construction material are described whilst Figures 5 to 22 describe embodiments of the invention with respect to transportation and delivery of the construction material. As noted the load of the construction material, e.g. concrete, is required post batching plant during transportation, delivery etc. in order to establish the volume(s) of fluid(s) such as water and / or admixtures to add or the weight of other materials and / or admixtures in powder or slurry form etc. Accordingly, as noted with respect to Figure 5 a slump measurement over time can be established although other parameters may be directly measured and / or established from sensors such as MBEDSENs. Accordingly, data is acquired over time which may include data relating to location such that data may be processed according to one or more geofences, such as a geo-fence defining the batching plant and another defining the delivery location for example.

[0175] It would be evident that based upon the acquired data with respect to a batch of material that one or more parameters of the material may trigger the requirement for an adjustment such as the addition of water and / or admixtures. Within the prior art such material additions are manually defined which may or may not be sufficient for the required compensation or may trigger the material to an unacceptable status. Accordingly, for an automatic system it is necessary for the system to know the mass of the load to define the water and / or admixture quantity to be added. Accordingly, by linking a Hub 740 to such as system the mass of the load is established at each point the adjustment is performed. This may be from the batch plant to an initial delivery location, from the initial delivery location to a subsequent delivery location or from the initial delivery location back to the batch plant for an additional load to be added etc.

[0176] Accordingly, referring to Figure 23 there is depicted a system for dispensing a fluid and additive(s) to a load of construction material within the drum of a truck according to an embodiment of the invention First and second Images 2300A and 2300B depict a Dispensing System for addition to a Truck 710 to add water and / or admixtures to the Drum 720 of the Truck 710. The Dispensing System 2310 being added to the Truck 710 whilst the Water Tank 2320 is already part of the Truck 710. The Dispensing System 2310 comprises an Inlet 2380 which is coupled to the Water Tank 2320 and an Outlet 2370 which is coupled to the Drum720. The Dispensing System 2310 has first to third Additive Tanks 2330(A) to 2330(C) respectively which are each coupled to first to third Valves 2340(A) to 2340(C) respectively and therein to Flow Meter 2350 which is coupled to Bypass Valve 2360 and Outlet 2370. The Bypass Valve 2360 is coupled to Flow Meter 2350 and Auxiliary Outlet 2390 which allows for connection of a water hose or other water source for washing out the Drum 720. The Flow Meter 2350 is also coupled to Water Valve 2330 which is coupled to Inlet 2380 to control flow of water from the Water Tank 2320. Flow Meter 2350 allows for control of the flow independent of the pressure. The Water Valve 2330, first to third Valves 2340(A) to 2340(C) respectively, Flow Meter 2350 and Bypass Valve 2360 are coupled to a controller, not depicted for clarity, so that the Dispensing System 2310 automatically delivers the required volume(s) of water and admixture(s) based upon the known load of the Drum 720. The controller may store an administration database which stores for each admixture data comprising, for example, low dose limit, high dose limit and efficiency of the admixture (i.e. 8Var / 8Dose). The admixture may be, for example, a water reducer admixture, a set control admixture, a hardening accelerator and a durability enhancer. Within other embodiments of the invention a dry dispensing system may be added to dispense concrete reinforcing fibers, for example. This dry dispensing system may be added to the Outlet 2370 such that the dry admixture is mixed with the fluid(s) before being added to the Drum 720. Optionally, the dry dispensing system may add to the mixture as it is dispensed from the Drum 720.

[0177] Referring to Figure 24 there is depicted schematically an adjustment process for a construction material as established by an artificial intelligence / machine learning system according to an embodiment of the invention with geographically or project defined margin(s). Image 2400 depicts a plot of material stress versus time with Acquired Data 2410, Specification 2440 and Target 2430 where Target - Specification equates to a margin defined by the concrete producer, architect etc. Alternatively, the margin may be defined based upon geographical location of the deployment where for example with a specification stress value S and a standard deviation cr of the stress value for the concrete mix then a deployment within the United States typically sets the target value T as Sus= T + 1.65cr whilst a Canadian deployment has a target value Sus= T + 1.45cr. Accordingly, using an AI / ML model such as described within Figures 3A and 4, the admixture(s) may be defined in order to set the target stress at a new target value 2450. This information may also be fed back to the batch plant such that the mix for a subsequent batch is adjusted so that is stress value is initially closer to the target value allowingfor factors such as distance I time between batching plant and deployment location, ambient environment, accessible admixture(s) etc.

[0178] Now referring to Figure 25 there is depicted schematically an adjustment process for a construction material as established by an artificial intelligence / machine learning system according to an embodiment of the invention. Image 2500 depicts an Improvement Cone comprising Upper Edge 2510 and Lower Edge 2520 wherein the factor K = (Measured — Specif icatioii) / StandardDeviation is plotted versus sample count. As discussed above K being, for example, 1.45 for Canada and 1.65 for United States. The aim being that with increasing samples the variability of the mix reduces and iterates towards a value slightly above the target value of K, Target 2530. First Point 2540 represents a measured K for a first mix which is currently being produced with a standard deviation whilst second Point 255- depicts the measured K for a second mix which is currently being produced with a standard deviation cr2. Accordingly, an adjustment for mix 1 is to reduce the measured K whereas the adjustment for mix 2 is to improve the measured K. In the scenario that cq < cr2then the adjustment for mix 1 can be made with increased confidence to towards the Target 2530 whereas the adjustment for mix 2 cannot be made with the same level of confidence such that it is likely that the compensation will over-shoot requiring additional corrections. Accordingly, using an AI. / ML based process the behaviour of the mixes can be assessed relative to other similar mixes, i.e. similar compositions, other batching plants etc. so that rather than waiting for a minimum number of samples, #N, the adjustments to a mix can be initiated earlier than within the prior art. This is particularly important where a mix is not manufactured that frequently as this allows the adjustment process to be completed within a shorter period of time.

[0179] Within the description above methods of obtaining the load of the construction material within the drum of the truck have been described. Referring to Figures 26 to 28 there is depicted another method of obtaining the load according to an embodiment of the invention using an acoustic sensor. Whilst in Figure 26 the Acoustic Sensor 2600 is depicted as a discrete element it would be evident that within other embodiments of the invention the Acoustic Sensor 2600A may be deployed in conjunction with another module or as part of another module. The another module may be another sensor module, such as Sensor Module 520 in Figure 5 which is depicted upon the exterior of a Drum 510, wherein the another module provides one or more other sensors relating to measurements associated with the drum and / or the load of construction material including, for example, a gyroscope or accelerometer for determining a rate of rotation, speed of rotation, etc. of the Drum 510.

[0180] As depicted in Figure 26 the Acoustic Sensor 2600 comprises a Housing 2610 which is attached to a Base 2640 and forms one or more regions within which are disposed an Acoustic Element 2630 and a Circuit 2670. The region between the Housing 2610, Base 2640 within which the Acoustic Element 2630 and Circuit 2660 are disposed being filled with a Filler 2620. The Filler 2620 may, for example, be a material such as a silicone or rubber for example. The Acoustic Element 2630 may be a discrete microphone or discrete piezoelectric element within embodiments of the invention or within others it may combine both or it may comprise two or more microphones and / or two or more discrete piezoelectric elements. The Acoustic Sensor 2600 is attached to the Drum 510 via a Glue 2650 within Figure 26 but it may, within other embodiments of the invention, be attached magnetically or via mechanical fittings such as bolts for example.

[0181] The Acoustic Sensor 2600 is either employed in conjunction with another module, such as Sensor Module 520 in Figure 5, or comprises additional elements which are similarly coupled to the Circuit 2660. As such the Acoustic Sensor 2600 discretely or in combination with the another module provides acoustic monitoring of the drum of a truck together with one or more of an inertial measurement unit (IMU) for acceleration of the drum, orientation of the drum, etc., a temperature sensor and a humidity sensor. The inventors have established that by deploying multiple Acoustic Sensors 2600 a determination of the angle of entry and exit of the construction material within the drum, e.g. concrete, in a non-invasive manner can be performing allowing for subsequent determination of a load size of the construction material from the filling level established in dependence upon the acoustic sensor outputs. The determined load is then employed in conjunction with other data, such as that determined from pressure measurements and the IMU as described above in respect of Figure 5, to establish a slump measurement of the construction material.

[0182] As will become evident from the description below in respect of Figures 27 and 28 the Acoustic Sensors 2600 provide a lost cost approach to the determination of the load of the drum, optionally within the same unit as the IMU for rotation - orientation information, allowing for the variations within a slump monitoring method employing, for example the drum rotation rate and hydraulic pressure, to be compensated for. Accordingly, the dependencies of drum rotation rate and hydraulic pressure with load can be corrected for within the slump measurement. The acoustic determination of the load within a rotating drum being as noted low complexity and rugged making it compatible with deployments upon concrete trucks etc. whilst offering the corrections required for slump measurements, temperature measurementsetc. of the material load. The overall combination of acoustic load determination with accelerometer - pressure based slump being novel relative to the prior art.

[0183] Referring to Figure 27 there are depicted first and second Images 2700A and 2700B respectively. Within first Image 2700A four acoustic sensors, first to fourth Acoustic Sensors 2710A to 2710D respectively, are attached to a Drum 510 of a truck. The first to fourth Acoustic Sensors 2710A to 2710D respectively transmitting data to a Hub 740 forming part of the truck such as described above. The first to fourth Acoustic Sensors 2710A to 2710D respectively may periodically transmit data to the Hub 740 or they continuously transmit to the Hub 740. The signals transmitted from the first to fourth Acoustic Sensors 2710A to 2710D respectively to the Hub 740 may be wireless, wired, optical or a combination thereof. The first to fourth Acoustic Sensors 2710A to 2710D when periodically transmitting data to the Hub 740 may do so based upon a trigger, e.g. from the Hub 740, from another module associated with each or some of the first to fourth Acoustic Sensors 2710A to 2710D, or from a circuit forming part of each of the first to fourth Acoustic Sensors 2710A to 2710D for example. The Hub 740 may for example be DAQ 540 as depicted in Figure 5 or it may be another PED or electronic device forming part of the truck, supported by the truck or associated with the truck.

[0184] Accordingly, within the Drum 510 is a Load 2730 which moves as the Drum 510 rotates in dependence upon factors such as the rotation rate of the Drum 510 and the fluidic properties of the Load 2730, e.g. for concrete this is typically defined by a slump measurement which measures the consistency of fresh concrete, namely its ability to flow (fluidity). Accordingly, the first to fourth Acoustic Sensors 2710A to 2710D detect acoustic signals (which according to the design of the acoustic element within the first to fourth Acoustic Sensors 2710A to 2710D may be ultrasound (defined as sound having frequencies 20kHz or higher by American National Standards Institute (ANSI)), acoustic (typically defined as sound having frequencies within the range 20 Hz - 20 kHz) or infrasound (typically defined as sound having frequencies below 20 Hz). The inventors establishing that the acoustic signal from the Drum 510 varies as the Load 2730 moves from not being the other side of the drum wall of the Drum 510 to the acoustic sensor, to being the other side and then not again as the Drum 510 rotates.

[0185] Second Image 2700B depicts a plot of the acoustic signal detected by an acoustic sensor, such as one of the first to fourth Sensors 2710A to 2710D, as the Drum 510 rotates and the portion of the Drum 510 on the other side of the drum wall to the sensor passes from being above the Load 2730, into the Load 2730, through the Load 2730 and then back out above the Load 2730. Second Image 2700B shows the repetitive nature of the monitored acoustic signalas the drum rotates with a single rotation D identified. The inventors have established that the Load 2730 “dampens” when in contact with the drum wall of the Drum 510 such that the region Pl with low signal amplitude is that portion of the drum rotation with the Load 2730 in contact with the drum wall and the region P2 is the region where the Load 2730 breaks contact from the drum wall where the acoustic sensor is positioned, there is no Load 2730 in contact with the drum wall and then the Load 2730 comes back into contact with the drum wall where the acoustic sensor is positioned. Accordingly, based upon at least the region Pl (or P2) and their position along the length of the Drum 510 the load level or fill of the Drum 510 with the Load 2730 may be determined from the design of the Drum 510. It would evident that each of the first to fourth Sensors 2710A to 2710D generates a signal curve such as depicted in second Image 2700B. Accordingly, a single sensor may be employed but multiple sensors provide increased signal curves and analysis which can be beneficial given that rotations rates for the Drum 510 may be slow such that the duration of single rotation may be many seconds, or even longer.

[0186] With more detailed segmentation and analysis of the acoustic signal trace(s) then additional information may be extracted from the acoustic signals relating to the Load 2730. Within embodiments of the invention the acoustic signals and the properties of the Load 2730 may be employed to train one or more ML models (or algorithms - processes) and / or one or more Al models (or algorithms - processes) such that additional data can be extracted by using the trained models of acquired data from loads during transportation.

[0187] Now referring to Figure 28 there are depicted first to sixth Acoustic Sensors 2810(A) to 2860(A) together with first to sixth Traces 2810(B) to 2860(B) respectively to simulate the signals from the first to sixth Acoustic Sensors 2810(A) to 2860(A). As depicted the first to sixth Acoustic Sensors 2810(A) to 2860(A) are disposed essentially in pairs at different positions along the length of the Drum 510 so that the load determination is based upon timing data from different positions along the length of the Drum 510. This may be beneficial when the Drum 510 is tilted forward or rear as the truck of which the Drum 510 forms part is on a sloped surface. The first to sixth Acoustic Sensors 2810(A) to 2860(A) again providing signals to a Hub 740. The placement of multiple sensors along the drum may be defined in dependence upon the design of the drum. At each point along the length a discrete sensor may be employed, a pair of sensors or multiple sensors. The benefit of a low cost passive sensor such as that depicted in Figure 26 is that multiple sensors can be employed without significant cost but with enhanced accuracy for load determination and accordingly slump and load performance etc.

[0188] Referring to Figure 29 there are depicted first and second Plots 2910 and 2920 from an acoustic sensor for high and low loadings of the Drum 510 respectively. The high loading having a longer portion of low signal amplitude due to the damping of the load against the drum for a longer time.

[0189] Within other embodiments of the invention some or all of the sensors, such as Acoustic Sensor 2600 or first to fourth Acoustic Sensors 2710A to 2710D respectively may generate an acoustic signal in addition to monitoring for acoustic signals. Where the sensor incorporates a microphone then an acoustic transducer is also provided, e.g. a loudspeaker or piezoelectric element, which generates an acoustic signal under control from a controller, e.g. Hub 740, either all the time the system is active or under predetermined conditions determined locally or remotely. Where the sensor incorporates a piezoelectric acoustic sensor then this may be employed to generate the acoustic signal before “listening” or another piezoelectric element may be employed to generate acoustic signals whilst the piezoelectric element “listens.”

[0190] Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it is understood that the embodiments may be practiced without these specific details. For example, circuits may be shown in block diagrams in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0191] Implementation of the techniques, blocks, steps, and means described above may be done in various ways. For example, these techniques, blocks, steps, and means may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described above and / or a combination thereof.

[0192] Also, it is noted that the embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process is terminated when its operations are completed, but there may be additional steps not included in the figure(s). A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination corresponds to a return of the function to the calling function or the main function.

[0193] Furthermore, embodiments may be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages and / or any combination thereof. When implemented in software, firmware, middleware, scripting language and / or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium, such as a storage medium. A code segment or machine-executable instruction may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures and / or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters and / or memory content. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

[0194] For a firmware and / or software implementation, the methodologies may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. Any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein. For example, software codes may be stored in a memory. Memory may be implemented within the processor or external to the processor and may vary in implementation where the memory is employed in storing software codes for subsequent execution to that when the memory is employed in executing the software codes. As used herein the term “memory” refers to any type of long term, short term, volatile, nonvolatile, or other storage medium and is not to be limited to any particular type of memory or number of memories, or type of media upon which memory is stored.

[0195] Moreover, as disclosed herein, the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and / or other machine readable mediums for storing information. The term “machine-readable medium” includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels and / or various other mediums capable of storing, containing, or carrying instruction(s) and / or data.

[0196] The methodologies described herein are, in one or more embodiments, performable by a machine which includes one or more processors that accept code segments containing instructions. For any of the methods described herein, when the instructions are executed bythe machine, the machine performs the method. Any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine are included. Thus, a typical machine may be exemplified by a typical processing system that includes one or more processors. Each processor may include one or more of a CPU, a graphicsprocessing unit, and a programmable DSP unit. The processing system further may include a memory subsystem including main RAM and / or a static RAM, and / or ROM. A bus subsystem may be included for communicating between the components. If the processing system requires a display, such a display may be included, e.g., a liquid crystal display (LCD). If manual data entry is required, the processing system also includes an input device such as one or more of an alphanumeric input unit such as a keyboard, a pointing control device such as a mouse, and so forth.

[0197] The memory includes machine-readable code segments (e.g., software or software code) including instructions for performing, when executed by the processing system, one of more of the methods described herein. The software may reside entirely in the memory, or may also reside, completely or at least partially, within the RAM and / or within the processor during execution thereof by the computer system. Thus, the memory and the processor also constitute a system comprising machine-readable code.

[0198] In alternative embodiments, the machine operates as a standalone device or may be connected, e.g., networked to other machines, in a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer or distributed network environment. The machine may be, for example, a computer, a server, a cluster of servers, a cluster of computers, a web appliance, a distributed computing environment, a cloud computing environment, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. The term “machine” may also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0199] The foregoing disclosure of the exemplary embodiments of the present invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many variations and modifications of the embodiments described herein will be apparent to one of ordinary skill in the art in light of the above disclosure. The scope of the invention is to be defined only by the claims appended hereto, and by their equivalents.

[0200] Further, in describing representative embodiments of the present invention, the specification may have presented the method and / or process of the present invention as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. As one of ordinary skill in the art would appreciate, other sequences of steps may be possible. Therefore, the particular order of the steps set forth in the specification should not be constmed as limitations on the claims. In addition, the claims directed to the method and / or process of the present invention should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the scope of the present invention.

Claims

CLAIMSWhat is claimed is:

1. A method comprising: establishing a measurement with respect to a load of a material disposed within a drum.

2. The method according to claim 1 , wherein establishing a measurement with respect to a load of a material disposed within a drum comprises: disposing one or more acoustic sensors upon an exterior surface of the drum containing the load of the material; acquiring an electrical output from each of the one or more acoustic sensors during rotation of the drum; and processing the acquired electrical outputs from the one or more acoustic sensors to establish a load of the material within the drum.

3. The method according to claim 2, further comprising establishing an adjusted slump measurement for the material in the drum in dependence upon the established load of the material in the drum.

4. The method according to claim 2, further comprising establishing a slump measurement of the material within the drum in dependence upon data acquired from a gyroscope attached to the drum during the rotation of the drum and other data relating to hydraulic pressure within a drive system rotating the drum; and establishing an adjusted slump measurement for the material in the drum in dependence upon the established slump measurement and the established load of the material in the drum.

5. The method according to claim 1, further comprising establishing the measurement with respect to the load of a material disposed within the drum comprises: disposing a sensor with respect to one of the drum and a truck comprising the drum where the drum contains the load of material; acquiring data from the sensor;processing the acquired data from the sensor to establish a load of the material within the drum; and establishing an adjusted slump measurement for the material in the drum in dependence upon the established load of the material in the drum and a slump measurement of the material within the drum.

6. The method according to claim 5, wherein the slump measurement of the material within the drum is established in dependence upon data acquired from a gyroscope attached to the drum during the rotation of the drum and other data relating to hydraulic pressure within a drive system rotating the drum; and establishing an adjusted slump measurement for the material in the drum in dependence upon the established slump measurement and the established load of the material in the drum.

7. The method according to claim 5, wherein the sensor is one of a set of sensors where each sensor of the set of sensors is one of a strain gauge and a strain sensor; and each sensor of the one or more sensors is attached to a defined support of the drum.

8. The method according to claim 5, wherein the sensor is one of a set of sensors where each sensor of the set of sensors is one of a strain gauge, a strain sensor, a ride height sensor and a string potentiometer; and each sensor of the one or more sensors is disposed with respect to a predetermined axle of the truck.

9. The method according to claim 5, wherein one of: the sensor is associated with a shock absorber of the truck and measures deflection of the shock absorber; the sensor is associated with a set of leaf springs of the truck and measures rotational deflection of the set of leaf springs; and the sensor is associated with a set of leaf springs of the truck and measures strain on a leaf spring of the set of leaf springs.

10. The method according to claim 5, further comprising an accelerometer attached to the drum; wherein the sensor is an exciter attached to the drum and which generates vibrations within the drum over a defined range of frequencies; and the accelerometer detects the vibrations excited within the drum which are processed by a processor to establish a resonance frequency of the drum.

11. The method according to claim 9, wherein either: the exciter generates vibrations over a defined range of frequencies; and the exciter generates vibrations over a frequency range less than the defined range of frequencies and is swept over the defined range of frequencies.

12. The method according to claim 5, wherein the sensor forms part of a sensor assembly disposed at an exit aperture of the drum; the sensor assembly provides for opening and closing of the exit aperture of the drum; the sensor establishes a resonant frequency of a Helmholtz resonator comprising at least the drum.

13. The method according to claim 5, further comprising acquiring engine power measurements of the truck; wherein the sensor is an accelerometer attached to the drum; and the load of the material is established in dependence upon an acceleration of the truck established in dependence upon the data from the accelerometer and the acquired engine power measurement.

14. The method according to claim 5, wherein the sensor is one of a set of sensors where each sensor of the set of sensors15. The method according to claim 5, wherein the sensor is one of a set of sensors where each sensor of the set of sensors is disposed in a defined position upon the truck; each sensor of the set of sensors is an accelerometer; andthe load of the material is determined in dependence upon a resonance frequency of a suspension system of the truck during at least one of rotation of the drum and motion of the truck.

16. The method according to claim 5, wherein the sensor is attached to the drum; and the sensor establishes a fill level of the drum during rotation of the drum by a non-invasive means selected from the group comprising an infrasonic means, an acoustic means and an ultrasonic means.

17. The method according to claim 5, wherein the sensor is a hydrostatic pressure sensor disposed within the drum.

18. The method according to claim 17, wherein the hydrostatic pressure sensor is one of part of a hatch of the drum and attached to another hatch of the drum.

19. The method according to claim 5, further comprising providing a magnet attached to the other of the drum and the truck in a defined positional relationship to the sensor; wherein the sensor is a Hall sensor; and load is determined in dependence upon a deflection of the drum established in dependence upon the data from the Hall sensor.

20. The method according to claim 1, further comprising establishing the measurement with respect to the load of a material disposed within the drum comprises: disposing a first temperature sensor in a first position with respect to chute employed in dispensing the load of material from the drum; disposing a second temperature sensor in a second position with respect to chute employed in dispensing the load of material from the drum; disposing a heater element between the first temperature sensor and the second temperature sensor; andestablishing a mass flow of the load of material being dispensed from the drum in dependence upon the temperature measurements from the first temperature sensor and the second temperature sensor.

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