System and method for determining the minimum paste addition amount

Abstract

The goal is to maximize concrete performance while minimizing costs. [Solution] This disclosure relates to a system, a non-temporary computer-readable medium, and a method for determining the minimum amount of paste to be added to a concrete mixture. This involves measuring a plurality of n particles and, for each particle i, determining the particle volume (I), particle surface area (II), maximum particle size (III), spherical volume (IV) and spherical surface area (V) related to a sphere having a diameter equal to this maximum particle size (III), and residual free surface S i This includes determining the following: Next, for multiple particles, the residual free surface S i Based on the particle volume (I) for all n particles, a shape disorder coefficient is determined. This shape disorder coefficient is used to determine the minimum amount of paste to combine with multiple n particles to produce a concrete mixture with good workability, and a control signal is sent to the concrete preparation system. [Math 1] JPEG2026521671000047.jpg6128

Description

[Technical Field]

[0001] This disclosure relates in general to the mixing of cement paste and concrete aggregate. [Background technology]

[0002] Concrete is the second most consumed substance on Earth (by mass) and accounts for 7-8% of global CO2 emissions. The material properties of concrete are inconsistent due to significant variability in its constituent materials (e.g., aggregates) and processing. This material inconsistency necessitates a large safety margin for a given performance level, leading to material overuse. Advances in concrete preparation are desired that can optimize the use of locally available materials to maximize concrete performance while minimizing costs, in both traditional and unconventional concrete compositions. [Overview of the project]

[0003] In general, this disclosure relates to a system, a non-transient computer-readable medium, and a method for determining the minimum amount of paste to be added to a concrete mixture. This involves measuring a plurality of n particles and, for each particle i, the particle volume

[0004]

number

[0005]

number

[0006]

number

[0007]

number

[0008] [Number] and the sphere surface area

[0009] [Number] as well as the residual free surface S including determining. Next, for a plurality of particles, the residual free surface S i , and the particle volume for all n particles

[0010] [Number] Based on, determine the shape irregularity coefficient η. Using this shape irregularity coefficient, determine the minimum amount of paste for combination with a plurality of n particles to generate a workable concrete mixture, and further send a control signal to the concrete preparation system to add at least the minimum amount of paste to the concrete mixture containing a plurality of n particles.

[0011] The implementation form can appropriately include one or more of the following features.

[0012] In some cases, for each particle i, determining the residual free surface S i includes

[0013] [Number] calculating.

[0014] In some cases, determining the shape disorder coefficient η is necessary for all n particles.

[0015]

number

[0016] In some cases, determining the minimum amount of paste to combine with multiple n particles involves calculating (1-ρ)+ρη, where ρ is the packing density of the multiple n particles. In some cases, the packing density ρ is the ratio of the volume occupied by the multiple n particles to the volume of the n particles themselves.

[0017] In some cases, determining the minimum amount of paste to combine with multiple n particles involves applying a correction factor to the shape disorder coefficient, which is based on the density difference between the multiple n particles and the paste.

[0018] In some cases, measuring multiple n particles involves scanning each particle with an optical scanner and generating a three-dimensional mesh model for each particle.

[0019] In some cases, the maximum particle size represents the longest straight-line length of particle i.

[0020] In some cases, a concrete mixture with good workability is one with a slump in the range of 2 to 8 inches, and the slump is measured according to ASTM C143.

[0021] In some cases, sending control signals to the concrete preparation system controls the operation of the conveying mechanism to add a minimum or greater volume of paste to the mixing container of the concrete preparation system.

[0022] Details of one or more embodiments relating to the subject matter of this specification are described in the accompanying drawings and the following description. Other features, aspects, and advantages of the subject matter will become apparent from the description, drawings, and claims. [Brief explanation of the drawing]

[0023] This disclosure relates to determining the minimum amount of paste necessary to achieve workable concrete with a given aggregate. [Figure 1] An exemplary concrete preparation system is shown. [Figure 2] This is a block diagram of an exemplary control system. [Figure 3] This flowchart illustrates one example process for sending control signals to a concrete preparation system. [Figure 4] This is a schematic diagram of a computer system. [Modes for carrying out the invention]

[0024] This disclosure describes a system and method for determining and adding cement paste to aggregates in a concrete mixture to achieve desired workability. Concrete is a mixture of various components, many of which contain particles of various sizes and shapes. Ready-mix concrete needs to be workable, that is, it needs to be molded or formed into the desired final shape before hardening. In other words, the workability of concrete determines its usability in operations such as pumping, pouring, spreading, compacting, or pouring into formwork or fixtures. Non-spherical particles in the aggregate exert shear forces on each other, which can result in a mixture that lacks "flowability," i.e., has poor workability. One way to improve workability is to increase the amount of smaller particles, such as cement paste, in the mixture to minimize the interaction of large aggregate particles. However, since cement paste is relatively expensive and heavy, it is often desirable to coat the aggregates and include the minimum amount of cement paste necessary to achieve the desired workability.

[0025] By measuring and modeling each large aggregate particle, it is possible to approximate the "roughness" or "jaggedness" of the aggregate-forming particle group, and use this to calculate the minimum paste amount required to achieve the desired workability. In some implementations, the workability of concrete is measured using the slump test (ASTM C143). In ASTM C143, concrete is filled into a cone according to a specific procedure, compacted, then the cone is removed, and the height difference as the concrete loosens is measured in inches to determine the slump. In some implementations, when measured using ASTM C143, the desired slump is between 2 and 8 inches.

[0026] Figure 1 shows an exemplary concrete preparation system 100. During operation, the concrete preparation system 100 measures the properties of the raw materials of the concrete mixture. Based on the measured or predicted properties of the raw materials, the concrete preparation system 100 adaptively adjusts the proportion of raw materials added to the concrete mixture to more accurately achieve the desired structural properties in the final cured concrete. The operation of the concrete preparation system 100 will be described in more detail below with reference to Figures 2 and 3.

[0027] The concrete preparation system 100 includes a control system 102. The control system 102 receives input from a particle analysis system 104 and a concrete mixture sensor 106. Based on the analysis of data acquired from either or both of the particle analysis system 104 and the concrete mixture sensor 106, the control system 102 can control the operation of one or more component weighing systems 108.

[0028] The concrete preparation system 100 includes raw material storage bays or hoppers 112a to 112n. The component weighing system 108 transports the raw materials from the storage bays 112a to 112n to the mixing container 110. For example, the component weighing system 108 may include a series of conveyors and augers for transferring the raw materials from the storage bays 112a to 112n into the mixing container 110. In some implementations, the raw materials include various aggregates such as crushed stone, sand, gravel, or combinations thereof, cement mixtures, water, admixtures, ash, metal density enhancers, or other raw materials. In some implementations, storage bay 112a stores coarse aggregate, which has a particle size 10 to 100 times larger than fine aggregate, and the fine aggregate itself has a particle size larger than that of cement paste (e.g., a mixture of cement and water).

[0029] In some implementations, the component weighing system 108 may include a weighing hopper 114 between the component weighing system 108 and the mixing container 110. The weighing hopper 114 may be used to collect and measure (e.g., weigh) the raw materials before they enter the mixing container 110. For example, the weight of the components measured by the weighing hopper 114 can be passed to the control system 102, which can monitor the weight of the components being measured in real time. The control system 102 may then be able to make on-site adjustments to the amount of components to be added to the concrete mixture based on real-time particle analysis of the components from the particle analysis system 104. In some implementations, the concrete preparation system 100 can be installed in a conventional ready-mix concrete plant. For example, by adding the concrete preparation system 100 to a ready-mix plant, the ready-mix plant may be able to more accurately prepare the concrete mixture to suit specific applications and work sites.

[0030] The particle analysis system 104 measures each particle emerging from storage bays 112a to 112n. Several implementations may include a series of sieves for separating component particles by size. In such implementations, an optical sensor (e.g., within the particle analysis system 104) can be positioned close to each sieve to capture images of particles passing through the sieves. These images can then be used, for example, in conjunction with a computer imaging system to generate a three-dimensional mesh model of each particle in a specific gradient across the series of sieves. The three-dimensional mesh model may include wireframe geometry representing the particles and can be used to determine the geometric characteristics of each particle, such as the maximum dimensions along each of three orthogonal axes, as well as the particle surface area. In some implementations, the separated particles can be recombined and then added to the mixing vessel 110. In some implementations, the optical sensor is positioned to capture images of the particles as they pass along a conveyor from one sieve to the final mixer, e.g., the mixing vessel 110.

[0031] The concrete mixture sensor 106 provides the control system 102 with rheometric measurements of the concrete mixture. For example, the concrete mixture sensor 106 can measure various properties of the concrete mixture that can be used to estimate or calculate the rheumatic properties of the concrete mixture in real time. Examples of concrete mixture sensors 106 include, but are not limited to, viscosity sensors, rheometers, temperature sensors, moisture sensors, ultrasonic sensors (e.g., ultrasonic pulse velocity sensors), electrical property sensors (e.g., electrodes, electrical resistance probes), electromagnetic sensors (e.g., short-pulse radar), or other sensors (e.g., vibrators, accelerometers). Examples of concrete mixture sensors 106 include, but are not limited to, hydrophobicity, water content, XRD spectrum, XRF spectrum, static yield stress, acoustic impedance, p-wave velocity, dynamic yield stress, static modulus, Young's modulus, bulk modulus, shear modulus, dynamic modulus of elasticity (DME), Poisson's ratio, density, resonance frequency, nuclear magnetic resonance (NMR), dielectric constant, electrical resistivity, polarization potential, and capacitance.

[0032] For example, viscosity, humidity, and temperature sensors can be installed in the mixing vessel 110. These sensors can be used to measure the rheological properties of the concrete mixture, such as the change in viscosity over time at different water content levels and temperatures. As will be described in more detail below, the control system 102 can use the rheometric measurements to determine whether and how much additional components should be added to the concrete mixture to obtain the desired concrete properties.

[0033] Figure 2 is a block diagram of an exemplary control system 102 of the concrete preparation system 100. The control system 102 includes a computing system 202 that communicates with a weighing control system 208 capable of controlling the operation of a concrete mixture sensor 106, a particle analysis sensor 204, and a component weighing system 108. The computing system 202 is configured to control various aspects of the concrete preparation process. For example, the computing system 202 can store and execute one or more sets of computer instructions for controlling the execution of aspects of the concrete preparation process described herein. The computing system 202 may include a system of one or more computing devices. The computing devices may be, for example, a system of one or more servers. For example, a first server may be configured to receive and process data from the concrete mixture sensor 106 and the particle analysis sensor 204. Another server may interface with the weighing control system 208 and be configured to issue control commands based on the analysis results from the first server.

[0034] In some implementations, the computing system 202 can be operated or controlled from a user computing device 203. The user computing device 203 may be a computing device, such as a desktop computer, laptop computer, tablet computer, or other portable or stationary computing device.

[0035] In short, the computing system 202 can control the overall concrete preparation system 100 to prepare the concrete mixture. The computing system 202 can characterize the concrete components as they are added to the concrete mixture using a particle analysis sensor 204. The computing system 202 obtains rheometric measurements from the mixture sensor 106 as the concrete mixture is mixed in the mixing container 110. The system compares the rheometric measurements with estimated rheometric measurements to determine, for example, whether the concrete mixture meets the desired post-hardening mechanical properties or whether additional components should be added.

[0036] In some implementations, the computing system 202 may include a set of operation modules 210 for controlling different aspects of the concrete additive manufacturing process. The operation modules 210 may be provided as one or more computer-executable software modules, hardware modules, or a combination thereof. For example, one or more of the operation modules 210 may be implemented as a block of software code having instructions that cause one or more processors of the computing system 202 to perform the operations described herein. In addition, or alternatively, one or more operation modules may be implemented in electronic circuits such as programmable logic circuits, field-programmable logic arrays (FPGAs), or application-specific integrated circuits (ASICs). The operation modules 210 may include a component addition controller 212, a paste calculation engine 216, and a surface area engine 220.

[0037] The component addition controller 212 interfaces with the metering control system 208 to control the addition of components to the concrete mixing container 110. For example, the component addition controller 212 can send commands from the computing system 202 to the metering control system 208 to control the addition of components to the concrete mixture in the mixing container 110 by increasing the speed of the conveyor or auger, opening or closing a sluice gate, or otherwise changing the addition rate of one or more components.

[0038] The surface area engine 220 can receive measured parameters related to aggregate or other large particles and determine the surface area of ​​each particle. For example, the surface area engine 220 can receive a three-dimensional mesh model of N large particles. In some implementations, the three-dimensional mesh model is formed from several vertices that define several triangles. For each mesh model, the surface area engine 220 can calculate the total surface area by summing the areas of all triangles in the mesh. For example, given any triangle defined by points A, B, and C, the area of ​​that triangle is:

[0039]

number

[0040]

number

[0041]

number

[0042] The paste calculation engine 216 can determine the amount of paste that needs to be added to a given group of large particles (e.g., aggregate) based on the volume and residual free surface area of ​​each particle determined by the surface area engine 220. This amount of paste is sometimes called the free paste demand or shape irregularity coefficient, and is calculated by summing the free residual surface areas of each particle in the large particle group.

[0043]

number

[0044]

number

[0045] Figure 3 is a flow diagram illustrating an exemplary process 300 of sending a control signal to a concrete preparation system. Process 300 can be executed, for example, by any suitable system, environment, software, and hardware, or, if necessary, by a combination of system, environment, software, and hardware. It should be understood that in some cases, process 300 can be executed by the system described in FIG. 1, or a part thereof, and the system further described in FIG. 2, as well as other components or functions described elsewhere in this specification. In other cases, process 300 can be executed by a plurality of connected components or systems. Any suitable system, architecture, or application can be used to perform the exemplary operations.

[0046] At 302, for each particle, its volume v p , surface area s p , longest dimension a p , spherical volume v s , spherical surface area s s , and the residual free surface area S are measured for the particle group. These measurements can be taken for each particle in the particle group, for example, using a camera and photogrammetry as the particles pass over a conveyor system or pass through a detection system (e.g., the measurement system 108 of FIG. 1). The spherical volume v s and spherical surface s s area are based on a sphere that completely encloses the particle or a sphere with a diameter equal to the measured longest dimension a p . The residual free surface area S compares the surface area measured for the particle with the spherical surface area and is given by the formula

[0047]

number

[0048] In step 304, the shape disorder coefficient or residual free surface η is determined. The shape disorder coefficient η is determined based on the residual free surface area of ​​all particles in the particle group and the volume of those particles, and is given by the formula

[0049]

number

[0050]

number

[0051]

number

[0052] In 306, the particle group shape disorder coefficient η can be used to determine the amount of paste required to adequately coat the larger particles in the mixture, minimize interactions between the larger particles, and obtain a workable mixture. This minimum amount of paste can be determined by calculating (1-ρ)+ρη, where ρ is the packing density of the particle group. The packing density ρ can be calculated by dividing the total volume occupied by the particle group by the sum of their individual volumes. In some implementations, the packing density of typical concrete aggregates is in the range of 0.48 to 0.52. When calculating the minimum amount of paste, the term (1-ρ) represents the void volume that needs to be filled with paste. The term pη represents the amount of paste required to coat the larger particles, reduce or eliminate shear forces between the larger particles, and make the mixture workable. In some implementations, workability is measured using a slump test, such as ASTM C143, and a mixture is considered workable if it has a slump of 2 to 8 inches.

[0053] In some implementations, 304 may be performed separately from 306. For example, aggregate can be produced during mining operations by extracting rock, crushing it, and granulating it through a series of sieves. At this point, the shape irregularity coefficient η can be determined by analyzing the granular aggregate. Then, batches of aggregate (e.g., bags, trucks, containers, etc.) may be associated with the η value before they are transported to a processing facility, where 306 is performed.

[0054] In 308, a control signal is sent to the concrete preparation system to add at least a determined minimum amount of paste to the concrete mixture containing the particle group. The control signal can activate or modify the operation of a conveying system that applies the paste (e.g., cement paste) to the aggregate. For example, the control signal can open or close a valve. It can accelerate or decelerate an auger or conveyor. In some implementations, the control signal can be operated to change the pressure, for example, if the paste is conveyed via an injection system or pipe network using air or hydraulics as the driving force.

[0055] Figure 4 is a schematic diagram of computer system 400. System 400 can be used to perform the operations described in relation to any of the computer implementation methods described herein, according to several implementations. In several implementations, the computing systems and devices and functional operations described herein can be implemented as digital electronic circuits, as tangibly embodied computer software or firmware, as computer hardware including the structures disclosed herein and their structural equivalents, or as a combination of one or more thereof. System 400 is intended to include various forms of digital computers, such as laptops, desktops, workstations, servers, blade servers, mainframes, and other suitable computers. System 400 may also include mobile devices such as personal digital assistants, mobile phones, smartphones, and other similar computing devices. Furthermore, the system may include portable storage media, such as Universal Serial Bus (USB) flash drives. For example, a USB flash drive may store an operating system and other applications. The USB flash drive may include input / output components such as wireless transducers or USB connectors, which may be inserted into a USB port of another computing device.

[0056] System 400 includes a processor 410, memory 420, storage device 430, and input / output devices 440. Each of components 410, 420, 430, and 440 is interconnected using a system bus 450. The processor 410 is capable of processing instructions to be executed within System 400. The processor may be designed using one of several architectures. For example, the processor 410 may be a CISC (Complex Instruction Set Computer) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor.

[0057] In one implementation, the processor 410 is a single-threaded processor. In another implementation, the processor 410 is a multi-threaded processor. The processor 410 can process instructions stored in memory 420 or storage device 430 to display graphical information for a user interface on input / output device 440.

[0058] Memory 420 stores information within the system 400. In one implementation, memory 420 is a computer-readable medium. In another implementation, memory 420 is a volatile memory unit. In yet another implementation, memory 420 is a non-volatile memory unit.

[0059] The storage device 430 can provide large-capacity storage for the system 400. In one implementation, the storage device 430 is a computer-readable medium. In various different implementations, the storage device 430 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device.

[0060] The input / output device 440 provides input / output operation for the system 400. In one implementation, the input / output device 440 includes a keyboard and / or a pointing device. In another implementation, the input / output device 440 includes a display unit for displaying a graphical user interface.

[0061] The described features can be implemented in digital electronic circuits, or in computer hardware, firmware, software, or a combination thereof. The device can be implemented in a computer program product tangibly embodied in an information carrier, e.g., a machine-readable memory device, for execution by a programmable processor, and the method steps can be executed by a programmable processor executing a program of instructions that perform the functions of the described implementation by manipulating input data to produce output. The described features can be advantageously implemented in one or more computer programs executable on a programmable system, including at least one programmable processor coupled to receive and transmit data and instructions to a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used directly or indirectly in a computer to perform a particular activity or to produce a particular result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a standalone program or as modules, components, subroutines, or other units suitable for use in a computing environment.

[0062] Processors suitable for executing instruction programs include, as an example, both general-purpose and dedicated microprocessors, as well as one or more processors in any type of computer. Generally, a processor will receive instructions and data from read-only memory, random-access memory, or both. Essential elements of a computer are a processor for executing instructions, and one or more memories for storing instructions and data. Generally, a computer also includes, or is operablely coupled to, one or more mass storage devices for storing data files, such devices include magnetic disks, magneto-optical disks, and optical disks, such as internal hard disks and removable disks. Storage devices suitable for materially realizing computer program instructions and data include, as an example, semiconductor memory devices such as EPROMs, EEPROMs, and flash memory devices, magnetic disks, magneto-optical disks, such as internal hard disks and removable disks, and all forms of non-volatile memory, including CD-ROMs and DVD-ROMs. Processors and memory can be complemented by or incorporated into ASICs (Application-Specific Integrated Circuits). Machine learning models can be run on graphics processing units (GPUs) or custom machine learning inference accelerator hardware.

[0063] To provide user interaction, this feature can be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user, and a pointing device such as a keyboard and mouse or trackball that allows the user to provide input to the computer. In addition, such activities can be implemented via a touchscreen flat panel display and other suitable mechanisms.

[0064] Such features can be implemented in computer systems that include backend components such as data servers, middleware components such as application servers or internet servers, or frontend components such as client computers with graphical user interfaces or internet browsers, or any combination thereof. The components of the system can be connected by digital data communication in any form or medium, such as a communication network. Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), peer-to-peer networks (with ad-hoc or static members), grid computing infrastructure, and the internet.

[0065] A computer system can include clients and servers. Clients and servers are generally remote from each other and typically interact over a network, such as the one described. The client-server relationship arises from computer programs running on each computer that have a client-server relationship with each other.

[0066] This specification includes details of many specific implementations, but these should not be interpreted as limitations on the scope of any invention or what may be claimed, but rather as descriptions of features specific to a particular implementation of a particular invention. Certain features described herein in the context of separate implementations may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented separately or in any suitable secondary combination in multiple implementations. Furthermore, features are described above as functioning in a particular combination and may initially be claimed as such, but in some cases one or more features from the claimed combination may be excluded from the combination, and the claimed combination may cover a sub-combination or a variation of a sub-combination.

[0067] Similarly, although the operations are depicted in a specific order in the diagrams, this should not be understood as requiring that such operations be performed in a specific or sequential order shown, or that all exemplified operations be performed, in order to achieve the desired result. In certain circumstances, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above-described implementations should not be understood as requiring such separation in all implementations, and the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0068] Therefore, a specific implementation of this subject matter has been described. Other implementations are within the scope of the following claims. In some examples, the operations described in the claims can be performed in a different order and still achieve the desired results. Furthermore, the processes shown in the accompanying drawings do not necessarily require the specific order or sequence shown to achieve the desired results. In certain implementations, multitasking and parallel processing may be advantageous.

[0069] The foregoing descriptions are provided in the context of one or more specific implementations. Various modifications, changes, and substitutions of the disclosed embodiments can be made without departing from the scope of this disclosure. Thus, this disclosure is not limited to the embodiments described or illustrated, but is intended to allow the broadest possible scope consistent with the principles and features disclosed herein.

[0070] In other words, the disclosure has been described in terms of certain embodiments and generally related methods, but modifications and substitutions of these embodiments and methods will be obvious to those skilled in the art. Accordingly, the above description of exemplary embodiments does not define or limit the disclosure. Other modifications, substitutions, and alterations are possible without departing from the spirit and scope of the disclosure.

Claims

1. It is a method, We measure multiple n particles, and for each particle i, Particle volume [Math 1] particle surface area [Math 2] Maximum particle size [Math 3] The maximum particle size [Math 4] Spherical volume related to a sphere with a diameter equal to . [Math 5] and surface area of ​​the sphere [Math 6] and residual free surface S ｉ To decide, For the plurality of particles, the residual free surface S ｉ , and the particle volume relating to all n particles [Number 7] Based on this, the shape disorder coefficient η is determined, From the aforementioned shape irregularity coefficient η, the minimum amount of paste to be combined with the plurality of n particles is determined in order to produce a concrete mixture with good workability. A control signal is sent to the concrete preparation system to cause it to add at least the minimum amount of paste to the concrete mixture containing the plurality of n particles. Methods that include...

2. For each particle i, the residual free surface S ｉ To decide is [Number 8] The method according to claim 1, which includes calculating [a certain value].

3. Determining the aforementioned shape disorder coefficient η is done for all n particles, [Number 9] The method according to any one of claims 1 or 2, which includes calculating .

4. The method according to any one of claims 1 to 3, wherein determining the minimum amount of paste to be combined with the plurality of n particles comprises calculating (1 - ρ) + ρη, where ρ is the packing density of the plurality of n particles.

5. The method according to claim 4, wherein the packing density ρ is the ratio of the volume occupied by the plurality of n particles to the volume of the n particles.

6. The method according to any one of claims 1 to 5, wherein determining the minimum amount of paste to be combined with the plurality of n particles comprises applying a correction factor to the shape irregularity coefficient, the correction factor being based on the density difference between the plurality of n particles and the paste.

7. The method according to any one of claims 1 to 6, wherein measuring the plurality of n particles includes scanning each particle with an optical scanner and generating a three-dimensional mesh model for each particle.

8. The method according to any one of claims 1 to 7, wherein the maximum particle size represents the longest straight-line length of the particle i.

9. The method according to any one of claims 1 to 8, wherein the workable concrete mixture is a concrete mixture having a slump in the range of 2 to 8 inches, the slump being measured according to ASTM C143.

10. The method according to any one of claims 1 to 9, wherein sending the control signal to the concrete preparation system includes controlling the operation of a transport mechanism to add a volume of paste greater than or equal to the minimum amount to the mixing container of the concrete preparation system.

11. It is a system, A concrete preparation system configured to add components to a mixing container using a conveying mechanism, A controller is provided, and the controller is We measure multiple n particles, and for each particle i, Particle volume [Number 10] particle surface area [Math 11] Maximum particle size [Math 12] The maximum particle size [Number 13] Spherical volume related to a sphere with a diameter equal to . [Number 14] and surface area of ​​the sphere [Number 15] and residual free surface S ｉ To decide, For the plurality of particles, the residual free surface S ｉ , and the particle volume relating to all n particles [Number 16] Based on this, the shape disorder coefficient η is determined, From the aforementioned shape irregularity coefficient η, the minimum amount of paste to be combined with the plurality of n particles is determined in order to produce a concrete mixture with good workability. A control signal is transmitted to the concrete preparation system to cause it to add at least the minimum amount of paste to the concrete mixture containing the plurality of n particles. A system configured to perform the following actions.

12. The system according to claim 11, wherein the conveying mechanism comprises at least one of a valve, a conveyor belt, or an auger.

13. For each particle i, the residual free surface S ｉ To decide is [Number 17] A system according to any one of claims 11 or 12, comprising calculating .

14. Determining the aforementioned shape disorder coefficient η is done for all n particles, [Number 18] A system according to any one of claims 11 to 13, comprising calculating .

15. The system according to any one of claims 11 to 14, wherein determining the minimum amount of paste to be combined with the plurality of n particles comprises calculating (1 - ρ) + ρη, where ρ is the packing density of the plurality of n particles.

16. The system according to claim 15, wherein the packing density ρ is the ratio of the volume occupied by the plurality of n particles to the volume of the n particles.

17. The system according to any one of claims 11 to 16, wherein determining the minimum amount of paste to be combined with the plurality of n particles includes applying a correction factor to the shape irregularity coefficient, the correction factor being based on the density difference between the plurality of n particles and the paste.

18. The system according to any one of claims 11 to 17, wherein measuring the plurality of n particles includes scanning each particle with an optical scanner and generating a three-dimensional mesh model for each particle.

19. The system according to any one of claims 11 to 18, wherein the maximum particle dimension represents the longest straight-line length of the particle i.

20. The system according to any one of claims 11 to 19, wherein the workable concrete mixture is a concrete mixture having a slump in the range of 2 to 8 inches, and the slump is measured according to ASTM C143.

21. The system according to any one of claims 11 to 20, wherein sending the control signal to the concrete preparation system includes controlling the operation of a transport mechanism to add a volume of paste greater than or equal to the minimum amount to the mixing container of the concrete preparation system.

22. It is a method, For multiple n particles, and for each particle i, Particle volume [Number 19] particle surface area [Number 20] Maximum particle size [Math 21] The maximum particle size [Number 22] Spherical volume related to a sphere with a diameter equal to . [Number 23] and surface area of ​​the sphere [Number 24] and residual free surface S ｉ Receiving and For the plurality of particles, the residual free surface S ｉ , and the particle volume relating to all n particles [Number 25] Based on this, the shape disorder coefficient η is determined, Based on the aforementioned shape irregularity coefficient, the minimum amount of paste to be combined with the plurality of particles is determined, A method comprising providing the minimum amount of paste as output to another system.

23. The residual free surface S of each particle i ｉ is [Number 26] The method according to claim 22, which is equivalent to the method described in claim 22.

24. Determining the aforementioned shape disorder coefficient η is done for all n particles, [Number 27] The method according to any one of claim 22 or 23, comprising calculating .

25. The method according to any one of claims 22 to 24, wherein determining the minimum amount of paste to be combined with the plurality of n particles comprises calculating (1 - ρ) + ρη, where ρ is the packing density of the plurality of n particles.

26. The method according to claim 25, wherein the packing density ρ is the ratio of the volume occupied by the plurality of n particles to the volume of the n particles.

27. The method according to any one of claims 22 to 26, wherein determining the minimum amount of paste to be combined with the plurality of n particles includes applying a correction factor to the shape irregularity coefficient, the correction factor being based on the density difference between the plurality of n particles and the paste.

28. The method according to any one of claims 22 to 27, wherein measuring the plurality of n particles includes scanning each particle with an optical scanner and generating a three-dimensional mesh model for each particle.

29. The method according to any one of claims 22 to 28, wherein the maximum particle size represents the longest straight-line length of the particle i.

30. A non-temporary computer-readable storage medium for storing instructions, wherein, when an instruction is executed by at least one processor, the non-temporary computer-readable storage medium causes the at least one processor to execute the method according to any one of claims 1 to 10 or 22 to 29.

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