Method for predicting the quality of ready-mixed concrete, method for manufacturing ready-mixed concrete, and system for manufacturing ready-mixed concrete

Abstract

To simply grasp the quality of ready mixed concrete.SOLUTION: According to one aspect of the present disclosure, a ready mixed concrete prediction method includes an acquisition step, and a prediction step. The acquisition step acquires input information including vibration information showing the magnitude of vibrations caused by falling or flowing down of ready mixed concrete manufactured by a mixer for kneading concrete materials. The prediction step predicts the quality of the ready mixed concrete on the basis of a prediction model constructed in advance by machine learning so as to output quality information showing the quality of the ready mixed concrete according to an input of input information, and the input information acquired in the acquisition step.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a method for predicting the quality of fresh concrete, a method for manufacturing fresh concrete, and a manufacturing system for fresh concrete.

Background Art

[0002] Patent Document 1 discloses a quality prediction method for predicting the quality of fresh concrete. In this quality prediction method, the quality of fresh concrete is predicted using a plurality of image data continuously captured inside a mixer while water and materials of fresh concrete other than water are being kneaded, and a prediction model.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present disclosure provides a method for predicting the quality of fresh concrete, a method for manufacturing fresh concrete, and a manufacturing system for fresh concrete, which can easily grasp the quality of fresh concrete.

Means for Solving the Problems

[0005] The method for predicting the quality of fresh concrete according to one aspect of the present disclosure includes an acquisition step and a prediction step. In the acquisition step, input information including vibration information indicating the magnitude of vibration caused by the fall or flow of fresh concrete produced by a mixer that kneads concrete materials is acquired. In the prediction step, the quality of fresh concrete is predicted based on a prediction model pre-constructed by machine learning so as to output quality information indicating the quality of fresh concrete in response to the input of the input information, and the input information acquired in the acquisition step.

[0006] The above vibration information may include information indicating the magnitude of vibrations in two or more directions.

[0007] The above vibration information may also indicate the magnitude of vibration in a hopper, which is located below the mixer and capable of containing ready-mixed concrete, when ready-mixed concrete is discharged from the mixer.

[0008] The above vibration information may include information indicating the magnitude of hopper vibration in a direction intersecting the hopper's side wall.

[0009] The above vibration information may further include information indicating the magnitude of hopper vibration in the direction along the side wall of the hopper.

[0010] The above vibration information may be the result of calculating at least one value selected from the group consisting of the maximum value, minimum value, mean value, and standard deviation of the moving average value of the hopper acceleration during an evaluation period set to include the time when the ready-mixed concrete is discharged from the mixer to the hopper.

[0011] The above acquisition process may include the steps of: calculating the time at which the absolute value of the hopper acceleration in a direction intersecting the hopper's side wall or along the hopper's side wall is maximum in the time-series data of the hopper's acceleration obtained during a predetermined data acquisition period is set as the reference time; and setting the period from a time before the reference time to a time after the reference time within the data acquisition period as the evaluation period.

[0012] In the above prediction process, the amount of air contained in the fresh concrete may also be predicted.

[0013] In the above prediction process, at least one of the slump and slump flow of the ready-mixed concrete may be predicted.

[0014] The above input information may further include at least one of the following: information indicating the mix design conditions for the concrete material, and information indicating the conditions or state of the concrete material during mixing in the mixer.

[0015] The above quality prediction method may further include a model building step for constructing a prediction model. The model building step may include a step of preparing learning input information, which includes learning vibration information indicating the magnitude of vibrations caused by the dropping or flow of test ready-mixed concrete produced by a mixer; and a step of constructing a prediction model by machine learning using a neural network based on the learning input information and the actual information.

[0016] A method for manufacturing ready-mixed concrete relating to one aspect of this disclosure includes a mixing step, an acquisition step, and a prediction step. In the mixing step, concrete materials are mixed in a mixer. In the acquisition step, input information is acquired, including vibration information indicating the magnitude of vibrations caused by the dropping or flow of the ready-mixed concrete produced in the mixing step. In the prediction step, the quality of the ready-mixed concrete is predicted based on a prediction model pre-built by machine learning to output quality information indicating the quality of the ready-mixed concrete in response to the input information, and the input information acquired in the acquisition step.

[0017] A ready-mixed concrete manufacturing apparatus relating to one aspect of this disclosure comprises a mixer, a sensor, an information acquisition unit, a model holding unit, and a quality prediction unit. The mixer mixes concrete materials. The sensor detects the magnitude of vibrations caused by the dropping or flow of ready-mixed concrete produced in the mixer. The information acquisition unit acquires input information, including vibration information indicating the magnitude of vibrations, based at least on the detection results from the sensor. The model holding unit stores a prediction model pre-built by machine learning to output quality information indicating the quality of the ready-mixed concrete in response to the input information. The quality prediction unit predicts the quality of the ready-mixed concrete based on the prediction model stored by the model holding unit and the input information acquired by the information acquisition unit. [Effects of the Invention]

[0018] According to the present disclosure, there are provided a method for predicting the quality of fresh concrete, a method for manufacturing fresh concrete, and a manufacturing system for fresh concrete, which can easily grasp the quality of fresh concrete.

Brief Description of the Drawings

[0019] [Figure 1] FIG. 1 is a schematic diagram showing an example of a manufacturing system for fresh concrete. [Figure 2] FIG. 2(a) is a side view schematically showing an example of a loading hopper. FIG. 2(b) is a bottom view schematically showing an example of the loading hopper. [Figure 3] FIG. 3 is a block diagram showing an example of the functional configuration of a control device. [Figure 4] FIG. 4(a) is a graph showing an example of a detection value by a vibration detection sensor. FIG. 4(b) is a graph showing an example of a detection value when fresh concrete drops. [Figure 5] FIG. 5 is a graph showing an example of a moving average value of a detection value. [Figure 6] FIG. 6 is a block diagram showing an example of the hardware configuration of a control device. [Figure 7] FIG. 7 is a flowchart showing an example of a series of processes executed in a learning phase. [Figure 8] FIG. 8 is a flowchart showing an example of a series of processes executed in an evaluation phase. [Figure 9] FIG. 9 is a graph showing an example of a verification result of a prediction model using vibration information. [Figure 10] FIG. 10 is a graph showing an example of a verification result of a prediction model using vibration information. [Figure 11] FIG. 11 is a graph showing an example of a verification result of a prediction model using vibration information.

Embodiments for Carrying Out the Invention

[0020] An embodiment will be described below with reference to the drawings. In this description, the same elements or elements having the same function will be denoted by the same reference numeral, and redundant descriptions will be omitted.

[0021] [Ready-mix concrete manufacturing system] Figure 1 schematically shows a ready-mix concrete manufacturing system according to one embodiment. Manufacturing system 1 shown in Figure 1 is a system for manufacturing ready-mix concrete. Manufacturing system 1 produces ready-mix concrete by mixing concrete materials. Concrete materials include cement, admixtures, coarse aggregate (e.g., gravel), fine aggregate (e.g., sand), water, and other admixtures.

[0022] Manufacturing system 1 loads the manufactured ready-mix concrete onto transport vehicle C. After loading the ready-mix concrete, transport vehicle C transports the ready-mix concrete to the site where it will be used (for example, a construction site). Examples of transport vehicle C include an agitator truck (mixer truck) and a dump truck. Manufacturing system 1 may manufacture ready-mix concrete from concrete materials to meet the target quality (required quality) set for each site. Manufacturing system 1 comprises a manufacturing device 10, a vibration detection sensor 40 (sensor), and a control device 50.

[0023] (Equipment for manufacturing ready-mixed concrete) The manufacturing apparatus 10 is a device that manufactures ready-mixed concrete based on operation instructions from the control device 50. The manufacturing apparatus 10 includes, for example, a storage jar 12, a measuring jar 14, a collection hopper 16, a mixer 20, and a loading hopper 30 (hopper). In Figure 1, the vertical direction is indicated by "direction D1" and "direction D2". Direction D1 is vertically upward, and direction D2 is vertically downward. In the manufacturing apparatus 10, the storage jar 12, measuring jar 14, collection hopper 16, mixer 20, and loading hopper 30 are arranged in this order from top to bottom.

[0024] The storage jar 12 temporarily stores various concrete materials. Various concrete materials are transported to the storage jar 12 from aggregate storage areas, cement silos, and water tanks by conveying equipment such as belt conveyors. The storage jar 12 is configured to store each type of concrete material individually. Hereafter, "concrete materials" may be simply referred to as "materials." The various materials stored in the storage jar 12 are supplied to the measuring jar 14 as needed.

[0025] The measuring bottle 14 is located below the storage bottle 12. The measuring bottle 14 operates based on operation instructions from the control device 50 and weighs various materials individually. When the measuring bottle 14 detects the target amount of material instructed by the control device 50, it supplies that material to the collection hopper 16. When water is supplied to the measuring bottle 14, an admixture may be mixed into the water. The collection hopper 16 is located below the measuring bottle 14. The collection hopper 16 collects the various materials discharged from the measuring bottle 14 and supplies the collected materials to the mixer 20.

[0026] The mixer 20 is located below the collective hopper 16. The mixer 20 is a device that produces ready-mixed concrete by mixing aggregate, cement, water, and admixtures. The mixer 20 may be a tilting mixer, a horizontal single-axis mixer, a horizontal double-axis mixer, or a pan-type mixer. The mixer 20 includes, for example, two stirring members 21 and a mixer drive unit 22. In a tilting mixer, it is difficult to capture images inside the mixer, so quality prediction using vibration according to this embodiment can be suitably utilized.

[0027] The two agitators 21 are components that agitate the various materials supplied to the mixer 20. The two agitators 21 are arranged side by side inside the main body (container) of the mixer 20 and are rotatably mounted. Each of the two agitators 21 includes a rotation axis that extends in one horizontal direction. The mixer drive unit 22 rotates the rotation axes of each of the two agitators 21 based on operation instructions from the control device 50. The mixer drive unit 22 includes a drive source, such as a motor, that provides driving force to the agitators 21. The bottom wall of the main body of the mixer 20 is provided with an opening for discharging the manufactured ready-mixed concrete into the loading hopper 30.

[0028] The loading hopper 30 is located below the mixer 20 and is capable of accommodating ready-mixed concrete. The loading hopper 30 receives the ready-mixed concrete discharged from the mixer 20 (the opening of the mixer 20) and temporarily stores the concrete. When ready-mixed concrete is discharged from the mixer 20 to the loading hopper 30, it falls into the loading hopper 30. For example, when discharged from the mixer 20, almost all of the ready-mixed concrete produced in the mixer 20 falls into the loading hopper 30. After temporarily storing the ready-mixed concrete produced in the mixer 20, the loading hopper 30 supplies the ready-mixed concrete to the transport vehicle C.

[0029] The loading hopper 30 may have any shape as long as it can accommodate ready-mix concrete. The loading hopper 30 may be truncated cone-shaped or truncated square pyramidal. For example, the loading hopper 30 is truncated square pyramidal and has a bottom wall 32 and side walls 34a, 34b, 34c, and 34d, as shown in Figures 2(a) and 2(b). The bottom wall 32 is positioned horizontally, and in plan view (viewed from vertically above), the outer edge of the bottom wall 32 is square. Each of the side walls 34a, 34b, 34c, and 34d is connected to one edge of the bottom wall 32 and is formed to extend in a vertically oblique direction. Each side wall is inclined vertically so as it moves upward, it moves away from the bottom wall 32.

[0030] The bottom wall 32 and the side walls 34a, 34b, 34c, and 34d form the storage space within the loading hopper 30. The top of the storage space within the loading hopper 30 is open. In the storage space within the loading hopper 30, the area in the horizontal plane decreases from the top towards the bottom wall 32. The bottom wall 32 is provided with an opening (not shown) for discharging ready-mix concrete to the transport vehicle C. Figure 2(b) illustrates the positional relationship between the loading hopper 30 and the opening 24 provided in the bottom wall of the main body of the mixer 20 when viewed from vertically below the loading hopper 30.

[0031] The opening 24 of the mixer 20 may be formed to extend along one horizontal direction. The opening 24 may be positioned between the pair of stirring members 21 described above and may be formed to extend along the axis of rotation of the stirring members 21. The loading hopper 30 may be positioned such that the pair of outer edges of the bottom wall 32 are aligned with the direction of extension of the opening 24. The side walls 34a and 34b are positioned to sandwich the bottom wall 32 in the direction of extension of the opening 24. The side walls 34c and 34d are positioned to sandwich the bottom wall 32 and the opening 24 in one horizontal direction perpendicular to the direction of extension of the opening 24.

[0032] In the direction of extension of the opening 24, the length of the opening 24 may be longer than the bottom wall 32, and may be shorter than the length (shortest distance) between the upper end of the side wall 34a and the upper end of the side wall 34b. In this case, when fresh concrete is discharged from the opening 24, the fresh concrete may fall not only onto the upper surface of the bottom wall 32, but also onto the inner surfaces of the side walls 34a and 34b. The fresh concrete that falls onto the inner surface of the side wall 34a or the inner surface of the side wall 34b flows down along these inner surfaces toward the bottom wall 32.

[0033] (Vibration detection sensor) The vibration detection sensor 40 is a sensor that detects vibrations (magnitude of vibrations) caused by the dropping or flow of ready-mixed concrete produced by the mixer 20. Vibrations caused by dropping or flowing include vibrations caused by both dropping and flowing of ready-mixed concrete. The vibration detection sensor 40 may be capable of detecting the magnitude of vibrations in two or more directions caused by the dropping or flowing of ready-mixed concrete. The vibration detection sensor 40 detects, for example, the magnitude of vibrations in the loading hopper 30. The vibration detection sensor 40 may be installed on either side wall of the loading hopper 30. The vibration detection sensor 40 may be installed on one of the side walls 34a and 34b from which ready-mixed concrete discharged from the mixer 20 can fall. In the example shown in Figures 2(a) and 2(b), the vibration detection sensor 40 is provided on the outer surface of side wall 34a.

[0034] The vibration detection sensor 40 may detect the magnitude of vibration of the loading hopper 30 in a direction intersecting the side wall 34a. In addition to vibration in a direction intersecting the side wall 34a, the vibration detection sensor 40 may also detect the magnitude of vibration of the loading hopper 30 in a direction along the side wall 34a. For example, the vibration detection sensor 40 detects the magnitude of vibration of the loading hopper 30 in a direction perpendicular to the side wall 34a, and the magnitude of vibration in two directions that are along the side wall 34a and perpendicular to each other. In this disclosure, the direction perpendicular to the side wall 34a is defined as the "Z-axis," and the horizontal and vertical directions when viewing the side wall 34a from a direction perpendicular to the side wall 34a are defined as the "X-axis" and "Y-axis," respectively.

[0035] The vibration detection sensor 40 may be of any type as long as it is capable of detecting the magnitude of vibration of the loading hopper 30. For example, the vibration detection sensor 40 is a sensor that detects the acceleration of the loading hopper 30. The vibration detection sensor 40 may detect the acceleration of the loading hopper 30 in the X-axis direction, the acceleration of the loading hopper 30 in the Y-axis direction, and the acceleration of the loading hopper 30 in the Z-axis direction.

[0036] The vibration detection sensor 40 may detect the acceleration of each axis at a predetermined sampling period. The sampling period may be approximately 0.1 seconds to 1.0 second. Instead of acceleration, the vibration detection sensor 40 may be a sensor that detects the speed, angular velocity, position information, displacement, or both acceleration and angular velocity of the loading hopper 30. The sensor that detects position information may be a GPS sensor. The vibration detection sensor 40 may continuously detect physical quantities indicating the magnitude of vibration, such as acceleration, while the manufacturing system 1 (manufacturing equipment 10) is in operation. The vibration detection sensor 40 outputs the detected values, such as acceleration, to the control device 50.

[0037] (Control device) The control device 50 is a device that controls the manufacturing apparatus 10. The control device 50 is composed of one or more control computers. An input / output device 52 may be connected to the control device 50 (see Figure 3). The input / output device 52 is a device that inputs information indicating instructions from an operator, etc., to the control device 50 and outputs information from the control device 50 to the operator, etc. The input / output device 52 may include a keyboard, operation panel, or mouse as an input device, and may include a monitor (e.g., a liquid crystal display) as an output device. The input / output device 52 may be a touch panel in which the input and output devices are integrated. The control device 50 and the input / output device 52 may be integrated.

[0038] The control device 50 may control the manufacturing apparatus 10 according to predetermined operating conditions. At least part of the operating information may be determined by instructions from an operator or the like. In addition to controlling the manufacturing apparatus 10, the control device 50 may be configured to predict the quality of the ready-mixed concrete produced by the manufacturing apparatus 10. In this case, the control device 50 constitutes a quality prediction device that predicts the quality of the ready-mixed concrete. The control device 50 (quality prediction device) is configured to perform at least the following: acquire input information including vibration information indicating the magnitude of vibrations caused by the dropping or flow of ready-mixed concrete produced in the mixer 20; predict the quality of the ready-mixed concrete based on a prediction model pre-built by machine learning to output quality information indicating the quality of the ready-mixed concrete in response to the input information; and the acquired input information.

[0039] As shown in Figure 3, the control device 50 has the following functional components (hereinafter referred to as "functional modules"): an operating condition holding unit 62, an operating control unit 64, an operating data acquisition unit 66, a detection data acquisition unit 68, a vibration data calculation unit 70, a quality prediction unit 72, a model holding unit 74, a model construction unit 76, and a notification unit 78. The processing performed by these functional modules corresponds to the processing performed by the control device 50.

[0040] The operating condition holding unit 62 holds (stores) information indicating the operating conditions for operating the manufacturing apparatus 10. The operating conditions held by the operating condition holding unit 62 may be predetermined by the operator or the like. The operator or the like may adjust (change) the operating conditions according to the target quality of the ready-mixed concrete. The operation control unit 64 controls the manufacturing apparatus 10 according to the operating conditions held by the operating condition holding unit 62. The operating conditions may include the concrete material mix conditions, the conditions of the materials used, and the conditions during mixing of the concrete materials in the mixer 20.

[0041] The operation data acquisition unit 66 acquires operation information used to predict the quality of ready-mixed concrete. The operation information includes, for example, condition information indicating the operation conditions for operating the manufacturing apparatus 10, state information indicating the state of the apparatus or materials when the manufacturing apparatus 10 is actually operating, and environmental information indicating the environment when the manufacturing apparatus 10 is operating. The operation information may also include at least one of the following: information indicating the mix design conditions for the concrete material, and information indicating the conditions or state when the concrete material is mixed in the mixer 20.

[0042] The operational information may include at least one of the following types of information: information regarding the mixing conditions of various materials, information regarding the materials used, information regarding mixing in the mixer 20, and information regarding the environment (external environment). Information regarding the mixing conditions of various materials may include, for example, nominal strength, mix strength, target slump, target slump flow, target air content, water-binder ratio, fine aggregate ratio, and unit amounts [kg / m³] of various materials (water, cement, admixtures, fine aggregate, coarse aggregate, and admixtures, etc.). 3 ] or unit volume [L / m³ 3 It includes at least one type of information from the following categories.

[0043] Information regarding the materials used includes, for example, the type, brand, manufacturing date, and density [g / cm³] of each material. 3 This includes, for example, at least one of the following types of information regarding aggregates (fine aggregates and coarse aggregates): water absorption rate, moisture content, surface moisture content, actual volume, coarseness ratio, particle size distribution, and maximum dimensions. The conditions or state of mixing concrete materials in the mixer 20 include, for example, at least one of the following types of information: the amount of each material to be mixed (total amount), the set value for mixing time, the measured value for mixing time, and the power load value of the mixer 20.

[0044] The operational information may include, in lieu of or in addition to the information described above, at least one of the following: information about cement and information about cement clinker. Information about cement may include, for example, at least one of the following: type of cement, chemical composition, mineral composition, wet f.CaO, loss on ignition, Blaine specific surface area, particle size distribution, sieve test residue, color, mineralogical and crystallographic properties of each mineral contained in the cement, and the hemihydrated ratio of gypsum contained in the cement.

[0045] Information regarding cement clinker includes, for example, the chemical composition, hydraulic coefficient, sieve test residue, Blaine specific surface area (powderness), and ignition loss of the raw materials used in the cement clinker formulation; the mineral composition, chemical composition, wet f.CaO (free lime), and volumetric gravity of the cement clinker; the crystallographic properties (lattice constant or crystallite size, etc.) of each mineral contained in the cement clinker; and the ratio of two or more mineral compositions contained in the cement clinker, with at least one type of information selected from this group.

[0046] The power load value of the mixer 20 may be measured in the mixer drive unit 22. The information indicating the power load value of the mixer 20 may be continuous time-series data while the mixer 20 is operating, statistical values ​​obtained from the time-series data, or power load values ​​at a few representative points. The information indicating the power load value may be the initial power load value at the start of operation of the mixer 20 (immediately after start), the maximum power load value during operation of the mixer 20, and the final power load value at the end of operation of the mixer 20 (immediately before end). The difference between the initial and maximum power load values ​​of the mixer 20, and the final power load value of the mixer 20, are thought to be affected by the hardness (softness) of the ready-mixed concrete, etc.

[0047] Environmental (external environment) information includes, for example, at least one of the following: ambient temperature, humidity, temperature inside mixer 20, temperature of various materials, and temperature of the containers storing the various materials. From the viewpoint of predicting quality accuracy, one or more pieces of information selected from the group consisting of the density of various materials, set and measured surface moisture content of fine aggregate, nominal strength, mix strength, water-binder ratio, fine aggregate ratio, unit amount of each material, mixing amount of materials, mixing time, initial value, maximum value, and ultimate value of power load, and ambient temperature may be used as operational information. The various operational information described above is just an example, and any information that can affect the quality of ready-mixed concrete may be included.

[0048] The detection data acquisition unit 68 acquires detection information indicating the vibration detection result from the vibration detection sensor 40. For example, the detection data acquisition unit 68 acquires detection information from the vibration detection sensor 40 indicating the acceleration of the loading hopper 30 in the X, Y, and Z axes. The detection data acquisition unit 68 may continuously acquire detection information indicating acceleration from the vibration detection sensor 40 while the manufacturing apparatus 10 is in operation.

[0049] The vibration data calculation unit 70 calculates (acquires) vibration information indicating the magnitude of vibration caused by the dropping or flow of ready-mixed concrete produced by the mixer 20 from the detection information acquired by the detection data acquisition unit 68. For example, the vibration data calculation unit 70 calculates vibration information indicating the magnitude of vibration in two or more directions caused by the dropping or flow of ready-mixed concrete from the above detection information. The vibration data calculation unit 70 may also calculate vibration information indicating the magnitude of vibration of the loading hopper 30 when ready-mixed concrete is discharged from the mixer 20 to the loading hopper 30.

[0050] The vibration information calculated by the vibration data calculation unit 70 may include information indicating the magnitude of vibration of the loading hopper 30 in the Z-axis direction. In addition to the Z-axis direction, the vibration information calculated by the vibration data calculation unit 70 may also include information indicating the magnitude of vibration of the loading hopper 30 in at least one of the X-axis and Y-axis directions. If the vibration detection sensor 40 is an acceleration sensor, the vibration data calculation unit 70 may obtain information indicating the acceleration of the loading hopper 30 in the X-axis, Y-axis, and Z-axis directions from the detection information obtained by the detection data acquisition unit 68. The vibration data calculation unit 70 may calculate statistical values ​​(representative values) as the above vibration information from the acceleration detection information detected by the vibration detection sensor 40 in each of the X-axis, Y-axis, and Z-axis directions.

[0051] The following describes an example of calculating statistical values ​​as vibration information from acceleration detection information. In the example below, the control device 50 (vibration data calculation unit 70) calculates vibration information in a situation where it does not know the timing of when the ready-mixed concrete is discharged from the mixer 20 to the loading hopper 30. The unit of ready-mixed concrete produced in one mixing cycle in the mixer 20 and loaded onto the transport vehicle C via the loading hopper 30 is defined as "1 batch". In one example, 1 to 3 batches of ready-mixed concrete are loaded onto one transport vehicle C. For example, if 2 batches of ready-mixed concrete are loaded onto one transport vehicle C, the mixing of the concrete material is performed twice in the mixer 20 at different timings (sequentially).

[0052] The vibration data calculation unit 70 first acquires data (hereinafter referred to as "time-series data") showing the time change of acceleration obtained during a predetermined data acquisition period in the X, Y, and Z axis directions from the detection information obtained by the detection data acquisition unit 68. The predetermined data acquisition period is set in advance to include the timing of one batch of ready-mixed concrete discharged from the mixer 20. In one example, the predetermined data acquisition period is set to the period from the start of production of one batch of ready-mixed concrete until the completion of loading onto the transport vehicle C.

[0053] Figure 4(a) shows a graph illustrating an example of the time change in acceleration in the Z-axis direction when the manufacturing apparatus 10 continues to operate and produces and loads dozens of batches of ready-mixed concrete. In the graph in Figure 4(a), an example of the data acquisition period is indicated by "Ta". As ready-mixed concrete is discharged from the mixer 20 to the loading hopper 30, the concrete falls into the loading hopper 30, causing a large fluctuation in the acceleration in the Z-axis direction in the negative direction (towards the outside of the loading hopper 30). The vibration data calculation unit 70 calculates the time at which the acceleration in the Z-axis direction shows the minimum value (the absolute value of the acceleration in the Z-axis is maximum) in the time-series data of acceleration obtained during the data acquisition period as the reference time ts.

[0054] Next, the vibration data calculation unit 70 sets the evaluation period to the time before the reference time ts and the time after the reference time ts within the data acquisition period. The evaluation period is the period for calculating the statistical values ​​of acceleration. The vibration data calculation unit 70 calculates the statistical values ​​of acceleration based on the detected acceleration values ​​during the evaluation period. The evaluation period is set to include the time when the ready-mixed concrete is discharged from the mixer 20 to the loading hopper 30 (the time when it falls). The method for setting the evaluation period is predetermined.

[0055] The vibration data calculation unit 70 extracts data for the evaluation period from the time-series data during the data acquisition period. Figure 4(b) shows a graph representing the time change of acceleration in the Z-axis direction during the evaluation period. In the example shown in Figure 4(b), the disclosure point of the evaluation period is set to 5 seconds before the reference time ts, and the end point of the evaluation period is set to 5 seconds after the reference time ts. 5 seconds is just an example; the evaluation period may be set to a period of several seconds to several tens of seconds around the reference time ts. The vibration data calculation unit 70 may also set the evaluation period such that the time between the reference time ts and the disclosure point is different from the time between the reference time ts and the end point.

[0056] The vibration data calculation unit 70 may calculate moving average values ​​for the acceleration of the loading hopper 30 in the X, Y, and Z axes during the evaluation period. The interval and number of data points used to calculate one value of the moving average can be set in any way. The vibration data calculation unit 70 may also calculate the average of the acceleration in the current sampling period, the acceleration in the previous period, and the acceleration in the period two periods prior for each sampling period. Depending on the sampling period, the vibration data calculation unit 70 may use detected acceleration values ​​from outside the evaluation period to calculate the moving average value for the evaluation period.

[0057] Figure 5 shows a graph representing the time change of the moving average value during the evaluation period. The vibration data calculation unit 70 may obtain as vibration information the result of calculating at least one selected from the group consisting of a maximum value a1, a minimum value a2, a mean value μ, and a value based on the standard deviation σ for the moving average value of the acceleration of the loading hopper 30 during the evaluation period in each of the X, Y, and Z axis directions. The value based on the standard deviation σ is the standard deviation σ itself and a value obtained by performing a predetermined operation on the standard deviation σ (for example, variance σ 2 The vibration data calculation unit 70 calculates, for each of the three directions, the maximum value a1, minimum value a2, mean value μ, and variance σ of the moving average value of acceleration during the evaluation period, as shown in Figure 5. 2 This may be calculated as vibration information. In this case, the vibration information obtained in the production of one batch of ready-mixed concrete consists of 12 data points (3 axes x 4 types of data).

[0058] Returning to Figure 3, the quality prediction unit 72 predicts the quality of the ready-mixed concrete based on the input information, including the vibration information calculated by the vibration data calculation unit 70. The input information is the information that the quality prediction unit 72 uses to predict the quality of the ready-mixed concrete. The quality prediction unit 72 may also acquire information as the input information that includes the vibration information calculated by the vibration data calculation unit 70 and the motion information acquired by the motion data acquisition unit 66. In this case, the motion data acquisition unit 66 and the vibration data calculation unit 70 constitute an information acquisition unit that acquires input information including vibration information and motion information.

[0059] The quality of the ready-mixed concrete predicted by the quality prediction unit 72 includes, for example, at least one of the following: slump, slump flow, time to reach 50 cm flow, V-funnel flow time, air content, setting start time, setting end time, strength (nominal strength, compressive strength, and flexural strength, etc.), Young's modulus, chloride content, durability, and color tone. The quality of the ready-mixed concrete may also include the quality of the hardened body formed when the ready-mixed concrete hardens.

[0060] The quality prediction unit 72 may predict the amount of air contained in the ready-mixed concrete as a quality of the ready-mixed concrete. Alternatively, the quality prediction unit 72 may predict at least one of the slump (slump value) and slump flow (slump flow value) of the ready-mixed concrete as a quality of the ready-mixed concrete, in addition to or instead of the amount of air. The strength, slump, slump flow, air content, and chloride content of the ready-mixed concrete can be measured by the test methods specified in "JIS A 5308:2019 (Ready-Mixed Concrete)".

[0061] The quality prediction unit 72 predicts the quality of ready-mixed concrete based on a prediction model pre-built by machine learning to output quality information indicating the quality of ready-mixed concrete in response to input information including vibration information, and the input information acquired by the information acquisition unit. The input information input to the prediction model may be multiple types of input data (numerical values), and the quality information output from the prediction model may be data (numerical values) indicating at least one type of quality. The quality prediction unit 72 may select a model corresponding to the type of quality to be predicted from multiple prediction models pre-built for multiple quality types, and then predict the quality of ready-mixed concrete using the prediction model for each quality type.

[0062] Machine learning is a method in which a machine (computer) autonomously discovers laws or rules by iteratively learning based on given information. The predictive model described above can be constructed using algorithms and data structures. For example, a predictive model can be realized using a neural network, which is an information processing model that mimics the structure of the human brain. The specific machine learning algorithm used when constructing a predictive model is not particularly limited.

[0063] The model storage unit 74 stores the prediction model. The model construction unit 76 constructs the prediction model by performing machine learning based on the input information for learning to construct the prediction model. The model construction unit 76 may autonomously construct a prediction model to predict quality by performing machine learning using, for example, data given as input to machine learning and ground truth data (measured quality values) from the output of machine learning. The input to machine learning is various datasets of input information. At least one piece of information contained in the input information differs from each other in the various datasets of input information. The output of machine learning is data (numerical value) indicating the quality of ready-mixed concrete. The model construction unit 76 iteratively learns a prediction model that outputs quality information indicating the quality of ready-mixed concrete using multiple combinations of the datasets of input information and the ground truth data of quality.

[0064] When the model building unit 76 builds a prediction model, it prepares learning input information, which includes learning vibration information indicating the magnitude of vibrations caused by the dropping or flow of test ready-mixed concrete produced by the mixer 20, and actual performance information indicating the quality of the test ready-mixed concrete associated with the learning input information. When the prediction model is built, for example, the control device 50 receives input from multiple combinations (multiple datasets) of learning input information and actual performance information. The model building unit 76 may also build the prediction model by machine learning using a neural network based on the learning input information and actual performance information.

[0065] The stage in which the above prediction model is autonomously manufactured corresponds to the learning phase. In the production phase or evaluation phase, the quality prediction unit 72 uses the prediction model stored in the model storage unit 74 to predict the quality of the ready-mixed concrete produced when the input information was obtained, based on input information whose quality is unknown. The above learning phase may be performed in the early stages of the production phase in which ready-mixed concrete is produced. The learned prediction model can be transferred between computers. Therefore, the control device 50 may acquire a prediction model built on another computer and store that prediction model in the model storage unit 74. In this case, the control device 50 may execute the production phase (evaluation phase) without executing the learning phase.

[0066] The notification unit 78 notifies the operator or others of the prediction results from the quality prediction unit 72. The notification unit 78 outputs quality information indicating the quality of the ready-mixed concrete predicted by the quality prediction unit 72 to the input / output device 52. The notification unit 78 may also display the above quality information on the monitor of the input / output device 52. After the operator or others confirm the quality information notified to the input / output device 52, they may decide whether or not the quality information meets the target quality. Alternatively, the control device 50 (for example, the quality prediction unit 72) may decide whether or not the predicted quality information meets the target quality, and then the notification unit 78 may output information indicating the result of that decision to the input / output device 52.

[0067] As shown in Figure 6, the control device 50 has a circuit 91. The circuit 91 includes one or more processors 92, a memory 93, a storage 94, an input / output port 95, and a timer 96. The storage 94 has a computer-readable storage medium, such as a non-volatile semiconductor memory. The storage 94 stores a program that causes the control device 50 to control the manufacturing equipment 10, the vibration detection sensor 40, and the input / output device 52 according to a preset control procedure. For example, the storage 94 stores a program for configuring each of the above-mentioned functional modules.

[0068] Memory 93 temporarily stores the program loaded from the storage medium of storage 94 and the calculation results from processor 92. Processor 92 works in cooperation with memory 93 to execute the above program, thereby configuring each functional module of control device 50. Input / output ports 95 input and output electrical signals to and from the manufacturing equipment 10, vibration detection sensor 40, and input / output devices 52, etc., according to commands from processor 92. Timer 96 measures elapsed time, for example, by counting reference pulses of a fixed period. Note that circuits 91 are not necessarily limited to those that configure each function by program. For example, circuits 91 may configure at least some of their functions by dedicated logic circuits or ASICs (Application Specific Integrated Circuits) that integrate them.

[0069] [Method of manufacturing ready-mixed concrete] Next, an example of a ready-mix concrete manufacturing method performed in manufacturing system 1 will be described. The ready-mix concrete manufacturing method includes a process for manufacturing ready-mix concrete and a process for predicting the quality of the ready-mix concrete. The process for manufacturing ready-mix concrete (hereinafter referred to as the "manufacturing process") includes, for example, a transport process, a weighing process, a input process, a mixing process, a discharge process, and a loading process. In the manufacturing process, these processes may be performed sequentially for each batch of ready-mix concrete produced. While the input process, mixing process, discharge process, and loading process are being performed, the transport process and weighing process for the next batch may be performed.

[0070] In the conveying process, various concrete materials are transported to the storage bottle 12 by a conveying device such as a belt conveyor, and each material is supplied to the storage bottle 12 individually. In the weighing process, each material is supplied individually from the storage bottle 12 to the weighing bottle 14, and each material is weighed in the weighing bottle 14. In the weighing process, when the measured amount of each material reaches the set amount for one batch, that material is discharged into the collection hopper 16. In the input process, after all types of materials have been collected in the collection hopper 16, one batch of material from the collection hopper 16 is input (supplied) into the mixer 20.

[0071] In the mixing process, the concrete material is mixed in the mixer 20. During the mixing process, the control device 50 may control the mixer drive unit 22 to drive the two stirring members 21 according to predetermined operating conditions. In the discharge process, after the mixing of the concrete material in the mixer 20 is completed, the fresh concrete produced is discharged from the mixer 20 to the loading hopper 30. For example, the control device 50 determines that mixing in the mixer 20 is complete when it determines that the power load value of the mixer 20 has stabilized (in one example, when the time change of the power load value falls below a predetermined value). In the loading process, after one batch of fresh concrete is discharged from the mixer 20 to the loading hopper 30, the fresh concrete is loaded from the loading hopper 30 onto the transport vehicle C.

[0072] The process of predicting the quality of ready-mixed concrete (hereinafter referred to as the "quality prediction process") is carried out during a period that overlaps with at least a portion of the execution period of the above-mentioned manufacturing process. The quality prediction process includes a model building process in the learning phase and a quality evaluation process in the production phase or evaluation phase. An example of the model building process and an example of the quality evaluation process will be described below. The explanation will be based on the example where one quality evaluation is performed each time two batches of ready-mixed concrete are manufactured.

[0073] (Model building process) Figure 7 is a flowchart showing an example of a series of processes performed in the model building process. This model building process is performed in parallel with the repeated production of test ready-mixed concrete in the manufacturing apparatus 10. The test ready-mixed concrete may be ready-mixed concrete that will not actually be shipped, or it may be ready-mixed concrete that is manufactured in the early stages of the production phase and will actually be shipped. While the model building process is being performed, the detection data acquisition unit 68 of the control device 50 continues to detect acceleration values ​​from the vibration detection sensor 40 at predetermined sampling intervals.

[0074] In the model building process, the control device 50 first executes step S11. In step S11, for example, the control device 50 waits until a predetermined evaluation timing is reached. The predetermined evaluation timing may be set to the timing when two batches of ready-mixed concrete, manufactured to the same target quality, are loaded onto the transport vehicle C. In this case, data for model building is evaluated and collected for each production of two batches of test ready-mixed concrete.

[0075] Next, the control device 50 executes step S12. In step S12, for example, the vibration data calculation unit 70 acquires data (the above-mentioned time-series data) showing the time change of acceleration obtained during a predetermined data acquisition period in each of the X-axis, Y-axis, and Z-axis directions from the detection information obtained by the detection data acquisition unit 68, batch by batch. That is, the vibration data calculation unit 70 extracts the time-series data for the data acquisition period corresponding to the first batch and the time-series data for the data acquisition period corresponding to the second batch from the above-mentioned detection information.

[0076] Next, the control device 50 executes step S13. In step S13, for example, the vibration data calculation unit 70 calculates the vibration information from the time-series data for each batch. In one example, the vibration data calculation unit 70 calculates the time at which the acceleration in the Z-axis direction shows the minimum value in the time-series data of acceleration obtained during the data acquisition period as the reference time ts. The vibration data calculation unit 70 then sets the period from before the reference time ts to after the reference time ts within the data acquisition period as the evaluation period. The evaluation period is set to be shorter than the data acquisition period.

[0077] After setting the evaluation period, the vibration data calculation unit 70 calculates a moving average value for the acceleration of the loading hopper 30 during the evaluation period in the X, Y, and Z axis directions. Then, for the moving average value of the acceleration of the loading hopper 30 during the evaluation period in the X, Y, and Z axis directions, the vibration data calculation unit 70 calculates the maximum value a1, minimum value a2, mean value μ, and variance σ. 2The vibration data is calculated as vibration information. The vibration data calculation unit 70 calculates the reference time ts, sets the evaluation period, calculates the moving average value, and calculates the maximum value a1 for each batch. In this case, the vibration information obtained in the production of two batches of ready-mixed concrete consists of 24 data points (3 axes × 4 types × 2 batches). The information obtained in step S13 corresponds to the vibration information for learning.

[0078] Next, the control device 50 executes step S14. In step S14, for example, the operation data acquisition unit 66 acquires the operation information described above. In one example, the operation data acquisition unit 66 acquires information regarding the blending conditions of various materials, information regarding the materials used, information regarding mixing in the mixer 20, and information regarding the environment (external environment) as the operation information. The operation data acquisition unit 66 may acquire information for two batches regarding mixing (amount of material to be mixed, mixing time, and power load value of the mixer 20). The information obtained in step S14 corresponds to the operation information for learning.

[0079] Next, the control device 50 executes step S15. In step S15, for example, the model building unit 76 acquires measured values ​​of the quality of the ready-mixed concrete as ground truth data. In one example, after two batches of ready-mixed concrete are loaded onto the transport vehicle C, some of the ready-mixed concrete is extracted by a worker or the like. The slump, slump flow, and air content of the extracted ready-mixed concrete are then measured by the worker or the like, and these measured values ​​are input to the control device 50.

[0080] The measured values ​​of the ready-mixed concrete quality obtained in step S15 correspond to actual performance information. The model construction unit 76 stores the learning input information, which includes the learning vibration information obtained in step S13 and the learning motion information obtained in step S14, after associating it with the actual performance information (quality measured values) obtained in step S15.

[0081] Next, the control device 50 executes step S16. In step S16, for example, the control device 50 determines whether a predetermined number of datasets have been acquired. If it is determined in step S16 that a predetermined number of datasets have not been acquired (step S16: NO), the control device 50 returns to step S11, and the control device 50 repeats steps S11 to S16. This process of repeating steps S11 to S16 continues until the number of datasets, which are associated with learning input information and performance information, reaches a predetermined number.

[0082] If it is determined in step S16 that a predetermined number of datasets have been acquired (step S16: YES), the control device 50 proceeds to step S17. In step S17, for example, the model building unit 76 constructs a prediction model (e.g., a regression prediction model) using machine learning with a neural network, using multiple combinations of input information for learning and actual information. The neural network has at least an input layer and an output layer. The neural network may also include one or more intermediate layers. Including intermediate layers allows for the construction of a more complex prediction model, improving the accuracy of quality prediction. The model building unit 76 may construct a prediction model for each type of quality. The model storage unit 74 stores the prediction model constructed in step S17.

[0083] (Quality evaluation process) Figure 8 is a flowchart showing an example of a series of processes performed in the quality evaluation process. The quality evaluation process is performed in parallel with the production of ready-mixed concrete to be actually shipped, which is repeatedly carried out in the manufacturing apparatus 10, after the model construction process described above. While the production of ready-mixed concrete to be actually shipped continues, the detection data acquisition unit 68 of the control device 50 may continue to detect acceleration values ​​from the vibration detection sensor 40 at predetermined sampling cycles.

[0084] In the quality evaluation process, the control device 50 executes steps S21, S22, S23, and S24, similar to steps S11, S12, S13, and S14. In step S23, evaluation vibration information is calculated, and in step S24, evaluation operation information is acquired. That is, by executing steps S23 and S24, evaluation input information including evaluation vibration information and evaluation operation information is acquired. "For evaluation" means that the quality of the ready-mixed concrete is unknown.

[0085] Next, the control device 50 executes step S25. In step S25, for example, the quality prediction unit 72 inputs the evaluation input information obtained in steps S23 and S24 into the prediction model held by the model holding unit 74, and predicts the quality of the ready-mixed concrete to be evaluated by acquiring the quality information output from that prediction model. Unlike step S15 in the model construction process, the quality of the ready-mixed concrete is not measured in step S25. The quality prediction unit 72 may predict the quality for each quality type using a prediction model appropriate to that type.

[0086] The quality prediction unit 72 predicts the slump of the ready-mixed concrete to be evaluated using the input information for evaluation and a prediction model for slump. The quality prediction unit 72 predicts the slump flow of the ready-mixed concrete to be evaluated using the input information for evaluation and a prediction model for slump flow. The quality prediction unit 72 predicts the air content of the ready-mixed concrete to be evaluated using the input information for evaluation and a prediction model for air content.

[0087] Next, the control device 50 executes step S26. In step S26, for example, the notification unit 78 outputs the quality prediction result obtained in step S25 to the input / output device 52. With this, one quality evaluation process, which is performed every two batches, is completed. The control device 50 may also execute the series of processes from steps S21 to S26 each time two batches of ready-mixed concrete are manufactured (loaded).

[0088] [Differentiation] The series of processes shown in Figures 7 and 8 are examples and can be modified as appropriate. In the above series of processes, the control device 50 may execute one step and the next step in parallel, or it may execute each step in a different order than the example described above. The control device 50 may also execute steps with different content than the example described above.

[0089] Unlike the example above, if one batch of ready-mixed concrete is loaded onto one transport vehicle C, the control device 50 may perform quality prediction for each batch of ready-mixed concrete produced. Furthermore, even if two or more batches of ready-mixed concrete are loaded onto transport vehicle C, the control device 50 may perform quality prediction for each batch produced (loaded). Regardless of the amount loaded onto transport vehicle C, the control device 50 may perform quality prediction for each batch of ready-mixed concrete produced. If ready-mixed concrete with different compositions or physical properties is produced, quality prediction may be performed using data from a larger number of batches to improve prediction accuracy.

[0090] In the example described above, the time at which the absolute value of acceleration in the Z-axis is maximum is calculated as the reference time ts, but the method for calculating the reference time ts is not limited to this. As ready-mixed concrete is discharged from the mixer 20 to the loading hopper 30, the ready-mixed concrete falls into the loading hopper 30, causing large fluctuations in acceleration in the X and Y axes. The vibration data calculation unit 70 may calculate the time at which the absolute value of acceleration in the X-axis direction or the Y-axis direction is maximum in the time-series acceleration data obtained during the above data acquisition period as the reference time ts.

[0091] In the example above, the evaluation period is set based on the reference time ts, but the method of setting the evaluation period is not limited to this. The timing when the outlet of the mixer 20 is opened and the timing when the loading hopper 30 vibrates significantly coincide. Therefore, if the control device 50 can detect the timing when ready-mixed concrete is discharged from the mixer 20, the vibration data calculation unit 70 may set the evaluation period to include the period before and after the detected discharge timing. The control device 50 may also set the evaluation period to include the period before and after the timing when a command to open the outlet of the mixer 20 is sent, or the timing when a signal indicating that the outlet of the mixer 20 has been received. If a sensor for detecting the presence of ready-mixed concrete is provided in the loading hopper 30, the control device 50 may also set the evaluation period to include the period before and after the timing when the sensor detects the presence of ready-mixed concrete.

[0092] The vibration data calculation unit 70 may calculate at least one of the median, mode, and number of peaks as vibration information, instead of or in addition to values ​​based on the maximum, minimum, mean, and standard deviation of the moving average value of acceleration during the evaluation period. The vibration data calculation unit 70 may calculate the statistical value of the acceleration itself during the evaluation period as vibration information instead of the moving average value of acceleration. The vibration data calculation unit 70 may calculate the result of calculating at least one of the values ​​based on the maximum, minimum, mean, and standard deviation of acceleration during the evaluation period as vibration information. The input information may include vibration information but not motion information. In this case, a prediction model is constructed from the vibration information, and quality is predicted based on the vibration information and the prediction model.

[0093] The vibration data calculation unit 70 may calculate vibration information based on the frequency spectrum obtained by frequency transformation (e.g., Fast Fourier Transform) of the data acquired by the vibration detection sensor 40. The vibration data calculation unit 70 may also calculate vibration information based on frequency information, feature quantities, or statistics obtained from the frequency spectrum. The control device 50 may acquire image data of a graph (waveform) showing the measured values ​​acquired by the vibration detection sensor 40 as vibration information.

[0094] A separate computer (quality prediction device) from the control device 50 for controlling the manufacturing apparatus 10 may predict the quality of the ready-mixed concrete using a prediction model. In this case, instead of the control device 50, the separate computer may have a functional module that performs quality prediction processing, such as a quality prediction unit 72. The vibration information used for quality prediction may be information indicating vibrations caused by the dropping or flow of ready-mixed concrete in locations other than the loading hopper 30.

[0095] [Verification of prediction models using vibration information] To verify quality prediction using vibration information, we compared the prediction results of a comparative model without vibration information with the prediction results of the above prediction model with vibration information. Specifically, we prepared 83 to 100 training datasets and 10 validation datasets. The training datasets were prepared by associating training input information (training vibration information and motion information) with measured values ​​of slump, slump flow, and air volume.

[0096] As operational information among the input data, we prepared data on the density of various materials, the set value and measured value of the surface moisture content of the fine aggregate, nominal strength, mix strength, water-binder ratio, fine aggregate ratio, unit amount of each material, mixing amount of materials for two batches, mixing time for two batches, initial value, maximum value, and ultimate value of the power load for two batches, and ambient temperature. As vibration information among the input data, we prepared the maximum value a1, minimum value a2, mean value μ, and variance σ of the moving average values ​​of the acceleration for two batches for the X, Y, and Z axes, respectively. 2 I prepared data on this topic.

[0097] In the training dataset, the above comparison model was constructed using motion information and quality measurements, without using vibration information. In the training dataset, the above prediction model was constructed using vibration information, motion information, and quality measurements. Comparison and prediction models were constructed for slump, slump flow, and air volume. In other words, the following six models were created. Comparative Model 1: A model that predicts slumps without using vibration information. Prediction Model 1: A model that predicts slumps using vibration information. Comparative Model 2: A model that predicts slump flow without using vibration information. Prediction Model 2: A model that predicts slump flow using vibration information. Comparative Model 3: A model that predicts air volume without using vibration information. Prediction Model 3: A model that predicts air volume using vibration information.

[0098] A first-order regression prediction model was constructed as both a comparison model and a prediction model, using various input data as variables to output a single quality (metric value). The first-order regression prediction model is shown by equation (1) below.

number

[0099] In equation (1), "y" is an output representing a quality index value, and represents slump, slump flow, or air volume. "i" is a natural number between 2 and N, and "N" represents the number of input data points. "w" is a weight (coefficient), and "b" is a bias term (coefficient). Using a training dataset, a regression prediction model was constructed for each quality type by determining the weight w and bias term b in equation (1). The value of the weight w was determined by calculating the squared error between the output y value when a certain value is input to the weight w and the measured quality value which is the ground truth data, and updating the value of the weight w using mini-batch gradient descent.

[0100] Note that the regression prediction model shown in equation (1) is a model constructed for validation purposes, and the prediction models in this disclosure are not limited to the regression prediction model shown above. When a prediction model (regression prediction model) is constructed using a neural network including hidden layers, that regression prediction model is shown, for example, by equation (2) below. In equation (2), f() represents a predetermined function.

number

[0101] After constructing the regression prediction model using equation (1) above, we compared the predicted values ​​with the measured values ​​using 10 validation datasets for both the comparison model without vibration information and the prediction model with vibration information. We compared the predicted and measured values ​​for slump, slump flow, and air volume.

[0102] Figure 9 is a graph showing the relationship between the predicted value of slump and the measured value of slump. In the graph shown in Figure 9, the horizontal axis represents the measured value of slump, and the vertical axis represents the predicted value of slump. The prediction results of comparison model 1, which does not use vibration information, are plotted with triangles, and the prediction results of prediction model 1, which uses vibration information, are plotted with circles. A line is drawn where the measured value and the predicted value coincide, and the closer the plotted result is to this line, the higher the prediction accuracy. Regarding slump, when the arithmetic mean of the absolute difference between the measured value and the predicted value (hereinafter referred to as the "mean absolute difference") was calculated, the mean absolute difference without using vibration information was 1.1 cm, and the mean absolute difference with using vibration information was also 1.1 cm. From this, it can be seen that even when using vibration information, a prediction accuracy of the same quality as when not using vibration information can be obtained.

[0103] Figure 10 is a graph showing the relationship between predicted and measured slump flow values. In the graph shown in Figure 10, the horizontal axis represents the measured slump flow value, and the vertical axis represents the predicted slump flow value. Prediction results from comparison model 2, which does not use vibration information, are plotted with triangles, and prediction results from prediction model 2, which uses vibration information, are plotted with circles. When the absolute average difference for slump flow was calculated, the absolute average difference without vibration information was 4.0 cm, and the absolute average difference with vibration information was 4.2 cm. Slump flow is measured in units of 0.5 cm, and it can be seen that even when vibration information is used, a prediction accuracy of a similar quality to that without vibration information can be obtained.

[0104] Figure 11 is a graph showing the relationship between predicted and measured air volume. In the graph shown in Figure 11, the horizontal axis represents the measured air volume, and the vertical axis represents the predicted air volume. Prediction results from comparative model 3, which does not use vibration information, are shown with triangles, and prediction results from prediction model 3, which uses vibration information, are plotted with circles. When the absolute difference average was calculated for air volume, the absolute difference average without vibration information was 0.53%, and the absolute difference average with vibration information was 0.27%. The absolute difference average decreased when vibration information was used.

[0105] Regarding air volume, the effect of using vibration information was evaluated from another perspective. In the graph shown in Figure 11, an upper limit line shifted vertically by +0.5% from the line where the measured value and the predicted value coincide, and a lower limit line shifted vertically by -0.5% are drawn. Predicted results that fall within the region between the upper and lower limits were defined as the correct answer, and the accuracy rate of 10 prediction results was evaluated. The allowable error for air volume in the JIS standard is ±1.5%, and considering that the quality may change during transportation to the site, the correct answer line was set to ±0.5%. The accuracy rate without using vibration information was 60%, and the accuracy rate with using vibration information was 80%. From the above, it can be seen that the prediction accuracy of air volume is improved when vibration information is used compared to when vibration information is not used.

[0106] [Effects of the Embodiment] The ready-mixed concrete quality prediction method described above includes an acquisition step and a prediction step. In the acquisition step, input information is acquired that includes vibration information indicating the magnitude of vibrations caused by the dropping or flow of ready-mixed concrete produced by the mixer 20 that mixes the concrete materials. In the prediction step, the quality of the ready-mixed concrete is predicted based on a prediction model pre-built using machine learning to output quality information indicating the quality of the ready-mixed concrete in response to the input information, and the input information acquired in the acquisition step. In this quality prediction method, information including vibration information is acquired and the quality is predicted using the prediction model without actually measuring the quality of the ready-mixed concrete produced. Therefore, it is possible to easily grasp the quality of ready-mixed concrete.

[0107] In the quality prediction method described above, the vibration information may include information indicating vibrations in two or more directions. Depending on the quality of the ready-mixed concrete, the state of vibration caused by the dropping or flow of the ready-mixed concrete may vary. In this method, quality is predicted based on vibration information indicating the magnitude of vibrations in two or more directions, making it possible to improve prediction accuracy.

[0108] The vibration information described above may also indicate the magnitude of vibration in a hopper, which is located below the mixer and capable of holding ready-mixed concrete, when the ready-mixed concrete is discharged from the mixer. In this case, vibration information can be easily obtained by installing a vibration-detecting sensor in the hopper. Therefore, it becomes possible to understand the quality of the ready-mixed concrete even more easily.

[0109] The above vibration information may include information indicating the magnitude of hopper vibration in a direction intersecting the hopper's side wall. In the side wall of a hopper located below the mixer, vibration in a direction intersecting the side wall can vary depending on the hardness or weight of the ready-mixed concrete produced. Therefore, by obtaining vibration information from the hopper vibration in a direction intersecting the side wall, the quality of the ready-mixed concrete can be predicted with high accuracy. Thus, it becomes possible to easily grasp the quality while improving the accuracy of predicting the quality of ready-mixed concrete.

[0110] The above vibration information may further include information indicating the magnitude of hopper vibration in the direction along the side wall of the hopper. In the side wall of a hopper located below the mixer, vibration in the direction along the side wall may also vary depending on the hardness or weight of the ready-mixed concrete produced. By obtaining vibration information from both vibrations intersecting the side wall and vibrations along the side wall, it becomes possible to predict quality with high accuracy.

[0111] The above vibration information may be the result of calculating at least one value selected from the group consisting of the maximum value, minimum value, mean value, and standard deviation (e.g., variance) of the moving average value of the hopper acceleration over an evaluation period set to include the time when ready-mixed concrete is discharged from the mixer to the hopper. By calculating the moving average value of acceleration, noise caused by the sensor itself can be reduced, and data processing becomes easier by using the statistical value of the moving average value. Therefore, it becomes possible to easily grasp the quality while improving the accuracy of quality prediction.

[0112] The above acquisition process may include the steps of: calculating the time at which the absolute value of the hopper's acceleration is maximum in the time-series data of hopper acceleration obtained during a predetermined data acquisition period, in a direction intersecting the hopper's side wall or along the hopper's side wall, as the reference time; and setting the period from before the reference time to after the reference time within the data acquisition period as the evaluation period. When fresh concrete falls into the hopper, the absolute value of the hopper's acceleration tends to be larger than during other periods. Therefore, vibration information at the time the fresh concrete falls contains a lot of information attributable to the quality of the fresh concrete. With the above method, even if the time at which the fresh concrete falls into the hopper is not known, the time at which the fresh concrete falls can be identified from the vibration information. Thus, it becomes possible to easily grasp the quality while improving the accuracy of quality prediction.

[0113] In the above prediction process, the amount of air contained in the fresh concrete may also be predicted. The amount of air contained in the fresh concrete can affect the state of vibration caused by the falling or flowing of the fresh concrete. This method makes it possible to easily determine the amount of air by using vibration information and a prediction model.

[0114] In the above prediction process, at least one of the slump and slump flow of the ready-mixed concrete may be predicted. In this case, at least one of the slump and slump flow can be easily determined by using input information including vibration information and a prediction model.

[0115] The above input information may further include at least one of the following: information indicating the mix design conditions for the concrete material, and information indicating the conditions or state of the concrete material during mixing in the mixer. The mix design conditions and the conditions or state during mixing affect the quality of the ready-mixed concrete. By further using information indicating the mix design conditions, etc., it is possible to improve the accuracy of quality prediction.

[0116] The above quality prediction method may further include a model building step for constructing a prediction model. The model building step may include a step of preparing learning input information, which includes learning vibration information indicating the magnitude of vibrations caused by the dropping or flow of test ready-mixed concrete produced by a mixer; actual information indicating the quality of the test ready-mixed concrete associated with the learning input information; and a step of constructing a prediction model by machine learning using a neural network based on the learning input information and the actual information. In this case, since the model is constructed by machine learning using a neural network based on the actual information associated with the input information, the predicted value by the model can be brought closer to the measured value. Therefore, it is possible to improve the prediction accuracy when predicting the quality of ready-mixed concrete. [Explanation of symbols]

[0117] 1...Ready-mixed concrete manufacturing system, 10...Ready-mixed concrete manufacturing equipment, 20...Mixer, 30...Loading hopper, 40...Vibration detection sensor, 50...Control device, 66...Operation data acquisition unit, 70...Vibration data calculation unit, 72...Quality prediction unit, 74...Model holding unit, 76...Model construction unit.

Claims

1. An acquisition step that acquires input information including vibration information indicating the magnitude of vibration caused by the dropping or flow of ready-mixed concrete produced by a mixer for mixing concrete materials, and motion information, The system includes a predictive model pre-built by machine learning to output quality information indicating the air content of the ready-mixed concrete in response to the input of the aforementioned input information, and a prediction step that predicts the air content of the ready-mixed concrete based on the input information acquired in the acquisition step. The vibration information is information indicating the magnitude of vibration of the hopper when the ready-mixed concrete is discharged from the mixer, with respect to the hopper which is located below the mixer and capable of containing the ready-mixed concrete. The vibration information is the result of calculating at least one value selected from the group consisting of the maximum value, minimum value, mean value, and standard deviation of the moving average value of the acceleration of the hopper during an evaluation period set to include the time when the ready-mixed concrete is discharged from the mixer to the hopper. A method for predicting the quality of ready-mixed concrete, wherein the operation information includes at least one type of information from among condition information indicating the conditions for operating a manufacturing apparatus that produces the ready-mixed concrete using the mixer, state information indicating the state of the apparatus or materials when the manufacturing apparatus is actually in operation, and environmental information indicating the environment when the manufacturing apparatus is in operation.

2. The method for predicting the quality of ready-mixed concrete according to claim 1, wherein the vibration information includes information indicating the magnitude of vibrations in two or more directions.

3. The method for predicting the quality of ready-mixed concrete according to claim 1 or 2, wherein the vibration information includes information indicating the magnitude of vibration of the hopper in a direction intersecting the side wall of the hopper.

4. The method for predicting the quality of ready-mixed concrete according to claim 3, wherein the vibration information further includes information indicating the magnitude of vibration of the hopper in a direction along the side wall of the hopper.

5. The acquisition process described above is: A step of calculating, as a reference time, the time at which the absolute value of the acceleration of the hopper is maximum in the time-series data of the acceleration of the hopper obtained during a predetermined data acquisition period, in a direction intersecting the side wall of the hopper or in a direction along the side wall of the hopper; A method for predicting the quality of ready-mixed concrete according to any one of claims 1 to 4, comprising the step of setting the period from a time before the reference time to a time after the reference time within the data acquisition period as the evaluation period.

6. The method for predicting the quality of ready-mixed concrete according to any one of claims 1 to 5, wherein the operation information includes at least one of information indicating the mixing conditions of the concrete material and information indicating the conditions or state when the concrete material is mixed in the mixer.

7. The process further includes a model building step for constructing the aforementioned prediction model, The aforementioned model building process is: A step of preparing learning input information including learning vibration information indicating the magnitude of vibration caused by the dropping or flow of test ready-mixed concrete produced by the mixer, and actual information indicating the amount of air in the test ready-mixed concrete associated with the learning input information, A method for predicting the quality of ready-mixed concrete according to any one of claims 1 to 6, comprising the step of constructing the prediction model by machine learning using a neural network based on the input information for learning and the performance information.

8. The mixing process involves mixing concrete materials in a mixer, An acquisition step that acquires input information including vibration information indicating the magnitude of vibration caused by the dropping or flow of the ready-mixed concrete produced in the mixing step, and motion information. The system includes a predictive model pre-built by machine learning to output quality information indicating the air content of the ready-mixed concrete in response to the input of the aforementioned input information, and a prediction step that predicts the air content of the ready-mixed concrete based on the input information acquired in the acquisition step. The vibration information is information indicating the magnitude of vibration of the hopper when the ready-mixed concrete is discharged from the mixer, with respect to the hopper which is located below the mixer and capable of containing the ready-mixed concrete. The vibration information is the result of calculating at least one value selected from the group consisting of the maximum value, minimum value, mean value, and standard deviation of the moving average value of the acceleration of the hopper during an evaluation period set to include the time when the ready-mixed concrete is discharged from the mixer to the hopper. A method for manufacturing ready-mixed concrete, wherein the operation information includes at least one of the following types of information: condition information indicating the conditions for operating a manufacturing apparatus that manufactures ready-mixed concrete using the mixer; state information indicating the state of the apparatus or materials when the manufacturing apparatus is actually in operation; and environmental information indicating the environment when the manufacturing apparatus is in operation.

9. A manufacturing apparatus comprising a mixer for mixing concrete materials, and a hopper positioned below the mixer and capable of containing the ready-mixed concrete produced in the mixer, A sensor for detecting the magnitude of vibrations caused by the falling or flow of the ready-mixed concrete, An information acquisition unit that acquires input information including vibration information indicating the magnitude of the vibration and operation information, based at least on the detection results from the sensor, A model holding unit stores a predictive model that has been pre-built using machine learning to output quality information indicating the air content of the ready-mixed concrete in response to the input of the aforementioned input information. The system includes a quality prediction unit that predicts the air content of the fresh concrete based on the prediction model stored by the model holding unit and the input information acquired by the information acquisition unit, The vibration information is information indicating the magnitude of vibration of the hopper when the ready-mixed concrete is discharged from the mixer to the hopper. The vibration information is the result of calculating at least one value selected from the group consisting of the maximum value, minimum value, mean value, and standard deviation of the moving average value of the acceleration of the hopper during an evaluation period set to include the time when the ready-mixed concrete is discharged from the mixer to the hopper. A ready-mix concrete manufacturing system, wherein the operation information includes at least one type of information from among condition information indicating the conditions for operating the manufacturing apparatus, state information indicating the state of the apparatus or materials when the manufacturing apparatus is actually operating, and environmental information indicating the environment when the manufacturing apparatus is operating.

Images