Ready-mix concrete quality prediction method, quality prediction program, ready-mix concrete manufacturing method, ready-mix concrete quality prediction device, and ready-mix concrete manufacturing system

Abstract

A ready-mix concrete quality prediction method according to the present invention includes: a construction step for constructing a prediction model through machine learning based on training data including a plurality of datasets; an acquisition step for acquiring a plurality of feature amounts obtained when manufacturing ready-mix concrete by using a mixer; and a prediction step for acquiring, by utilizing the prediction model, a predicted value for quality corresponding to the plurality of feature amounts acquired in the acquisition step. In each of the plurality of datasets, the plurality of feature amounts are associated with a quality ground truth, the plurality of feature amounts include at least one load feature amount related to the power load value of the mixer when mixing a concrete material or stirring a ready-mix concrete. In the construction step, the prediction model is constructed such that one or more feature amounts selected from the plurality of feature amounts and the predicted value for quality are associated with one another by using a decision tree.

Description

Method for predicting quality of ready-mixed concrete, quality prediction program, manufacturing method of ready-mixed concrete, device for predicting quality of ready-mixed concrete, and manufacturing system of ready-mixed concrete

[0001] The present disclosure relates to a quality prediction method for ready-mixed concrete, a quality prediction program, a manufacturing method for ready-mixed concrete, a quality prediction device for ready-mixed concrete, and a manufacturing system for ready-mixed concrete.

[0002] Patent Documents 1 and 2 disclose methods for predicting the quality of ready-mixed concrete using a prediction model.

[0003] JP 2020-144099 A JP 2021-124304 A

[0004] The present disclosure provides a ready-mixed concrete quality prediction method, a quality prediction program, a ready-mixed concrete manufacturing method, a ready-mixed concrete quality prediction device, and a ready-mixed concrete manufacturing system that are useful for improving prediction accuracy when predicting the quality of ready-mixed concrete using a prediction model.

[0005] [1] A quality prediction method for predicting the quality of ready-mixed concrete, comprising: a construction step of constructing a prediction model by machine learning based on training data including a plurality of data sets; an acquisition step of acquiring a plurality of feature quantities obtained when producing ready-mixed concrete using a mixer; and a prediction step of using the prediction model constructed in the construction step to acquire a predicted value of the quality corresponding to the plurality of feature quantities acquired in the acquisition step, wherein, for each of the plurality of data sets, the plurality of feature quantities are associated with a correct value of the quality, and the plurality of feature quantities include one or more load feature quantities related to a power load value of the mixer when mixing concrete materials or stirring ready-mixed concrete, and in the construction step, the prediction model is constructed so that one or more feature quantities selected from the plurality of feature quantities are associated with the predicted value of the quality using a decision tree.

[0006] [2] The method for predicting the quality of ready-mixed concrete described in [1] above, wherein in the construction step, the prediction model is constructed by machine learning including ensemble learning.

[0007] [3] The method for predicting the quality of ready-mixed concrete according to [2] above, wherein in the construction step, the prediction model is constructed by machine learning using boosting.

[0008] [4] The method for predicting the quality of ready-mixed concrete according to [2] or [3] above, wherein in the construction step, the prediction model is constructed by machine learning using gradient boosting.

[0009] [5] The method for predicting the quality of ready-mixed concrete according to any one of [1] to [4] above, wherein the one or more load feature amounts include at least one of a value at a predetermined time point in time-series data of the power load value of the mixer when mixing concrete materials or stirring ready-mixed concrete, and a statistical amount obtained from the time-series data, and the statistical amount is one or more values ​​selected from the group consisting of the difference between the maximum value and the minimum value, the difference between the maximum value and the final value, the sum, the mean value, the standard deviation, the coefficient of variation, the median, the first quartile, the third quartile, kurtosis, and skewness.

[0010] [6] The method for predicting the quality of ready-mixed concrete according to any one of [1] to [5] above, wherein the plurality of feature amounts further include one or more time feature amounts related to a time when the mixer mixes the concrete materials, and the one or more time feature amounts include at least one of the date and time when the mixer mixes the concrete materials, the cumulative number of batches mixed by the mixer in one day, the time from an arbitrarily set reference time point to the time when the mixer mixed the concrete materials, and the cumulative number of batches mixed by the mixer from an arbitrarily set reference time point.

[0011] [7] A quality prediction program that causes a computer to execute the quality prediction method according to any one of [1] to [6] above.

[0012] [8] A method for producing ready-mixed concrete, comprising: a production process for producing ready-mixed concrete; and a quality prediction process for predicting the quality using the quality prediction method described in any one of [1] to [6] above, after or during the production process.

[0013] [9] A quality prediction device for predicting the quality of ready-mixed concrete, comprising: a model construction unit that constructs a prediction model by machine learning based on training data including a plurality of data sets; an acquisition unit that acquires a plurality of feature quantities obtained when producing ready-mixed concrete using a mixer; and a prediction unit that uses the prediction model constructed by the model construction unit to acquire a predicted value of the quality according to the plurality of feature quantities acquired by the acquisition unit, wherein, for each of the plurality of data sets, the plurality of feature quantities are associated with a correct value of the quality, and the plurality of feature quantities include one or more load feature quantities related to the power load value of the mixer when mixing concrete materials or stirring ready-mixed concrete, and the model construction unit constructs the prediction model so that one or more feature quantities selected from the plurality of feature quantities are associated with the predicted value of the quality using a decision tree.

[0014]

[10] A ready-mixed concrete manufacturing system comprising a manufacturing apparatus for manufacturing ready-mixed concrete and the quality prediction device described in [9] above, wherein the quality prediction device predicts the quality of the ready-mixed concrete manufactured by the manufacturing apparatus.

[0015] According to the present disclosure, there are provided a ready-mixed concrete quality prediction method, a quality prediction program, a ready-mixed concrete manufacturing method, a ready-mixed concrete quality prediction device, and a ready-mixed concrete manufacturing system that are useful for improving the prediction accuracy when predicting the quality of ready-mixed concrete using a prediction model.

[0016] FIG. 1 is a schematic diagram showing an example of a ready-mix concrete manufacturing system. FIG. 2 is a block diagram showing an example of the functional configuration of a control device. FIG. 3 is a diagram for explaining an example of a prediction model. FIGS. 4(a) and 4(b) are diagrams for explaining an example of a process for building a prediction model. FIG. 5 is a diagram for explaining an example of a process for building a prediction model. FIGS. 6(a) and 6(b) are diagrams for explaining an example of a process for building a prediction model. FIGS. 7(a) and 7(b) are diagrams for explaining an example of a process for building a prediction model. FIGS. 8(a) and 8(b) are diagrams illustrating data related to power load values. FIG. 9 is a diagram for explaining an example of an expression format for dates and times. FIG. 10 is a block diagram showing an example of the hardware configuration of a control device. FIG. 11(a) is a flowchart showing an example of a series of processes in a training phase. FIG. 11(b) is a flowchart showing an example of a series of processes in an evaluation phase. FIGS. 12(a) and 12(b) are graphs showing results of verifying the prediction accuracy of a prediction model. 13(a) and 13(b) are graphs showing the results of verifying the prediction accuracy of the prediction model.

[0017] An embodiment will be described below with reference to the drawings. In the description, identical elements or elements having identical functions are designated by the same reference numerals, and duplicated explanations will be omitted. Figure 1 schematically shows a ready-mixed concrete manufacturing system equipped with a quality prediction device according to an embodiment.

[0018] [Ready-mixed concrete manufacturing system] First, an overview of the ready-mixed concrete manufacturing system will be described. The manufacturing system 1 (ready-mixed concrete manufacturing system) shown in Fig. 1 is a system for manufacturing ready-mixed concrete. The manufacturing system 1 manufactures ready-mixed concrete through at least a process of mixing concrete materials. The concrete materials used in the manufacturing system 1 include cement, admixtures, coarse aggregate, fine aggregate, water, and admixtures.

[0019] Examples of coarse aggregate include natural coarse aggregate and artificial coarse aggregate. Examples of coarse aggregate include gravel, crushed stone, slag coarse aggregate, lightweight coarse aggregate, recycled coarse aggregate, recovered coarse aggregate, and mixtures thereof. Examples of gravel include mountain gravel, land gravel, river gravel, and sea gravel. Examples of slag coarse aggregate include blast furnace slag aggregate, ferronickel slag aggregate, electric furnace oxidizing slag aggregate, and coal gasification slag aggregate. Examples of lightweight coarse aggregate include natural lightweight aggregate, by-product lightweight aggregate, and artificial lightweight aggregate. Examples of coarse aggregate include crushed rock or limestone crushed stone.

[0020] Examples of fine aggregates include natural aggregates and artificial aggregates. Examples of fine aggregates include sand, crushed sand, slag fine aggregate, lightweight fine aggregate, recycled fine aggregate, recovered fine aggregate, and mixtures of these. Examples of sand include mountain sand, land sand, river sand, and sea sand. Examples of slag fine aggregates include blast furnace slag aggregate, ferronickel slag aggregate, copper slag aggregate, electric furnace oxidized slag aggregate, and coal gasification slag aggregate. Examples of lightweight fine aggregates include natural lightweight aggregate, by-product lightweight aggregate, and artificial lightweight aggregate.

[0021] Examples of rock types for crushed stone and crushed sand include igneous rocks, sedimentary rocks, metamorphic rocks, quartz, limestone, dolomite, and peridotite. Igneous rocks include granite, diorite, gabbro, porphyrite, diabase, rhyolite, andesite, basalt, and serpentine. Sedimentary rocks include conglomerate, sandstone, shale, slate, and tuff. Metamorphic rocks include gneiss and crystalline schist. The coarse aggregate and fine aggregate may be obtained by mixing two or more of the materials exemplified above.

[0022] At least a portion of the manufacturing system 1 is installed, for example, in a location (e.g., a factory) different from the location (site) where the ready-mixed concrete is used. After the ready-mixed concrete is loaded onto the transport vehicle 200, the transport vehicle 200 transports the ready-mixed concrete to the site (e.g., a construction site) where the ready-mixed concrete is to be used. Examples of the transport vehicle 200 include an agitator vehicle (mixer vehicle) or a dump truck. The manufacturing system 1 may manufacture ready-mixed concrete from concrete materials so as to satisfy a target quality (required quality) set for each site. For example, an operator of the manufacturing system 1 determines the mix of concrete materials so as to satisfy the target quality and inputs operating instructions to the manufacturing system 1.

[0023] In one example, ready-mixed concrete is produced in the production system 1, and the quality of the ready-mixed concrete is controlled (inspected, etc.) before shipping so that the target quality of the ready-mixed concrete when in use, which is set for each site, is met. The time when ready-mixed concrete is used corresponds to the time when the ready-mixed concrete is received at the site. In order to control the quality of the ready-mixed concrete before shipping, the target quality of the ready-mixed concrete when shipped may be set based on the target quality of the ready-mixed concrete when in use. The quality of ready-mixed concrete includes fresh properties. Specific examples of fresh properties include slump, slump flow, and air content.

[0024] The manufacturing system 1 includes, for example, a manufacturing apparatus 100 and a quality prediction apparatus 10. The manufacturing apparatus 100 is an apparatus that mixes concrete materials to manufacture ready-mixed concrete. The manufacturing apparatus 100 may be installed in a factory or the like that is different from the site where the ready-mixed concrete is used. The manufacturing apparatus 100 includes, for example, a material storage area 101, a transport device 104, a storage bottle 111, a measuring bottle 112, a collecting hopper 113, a mixer 114, and a loading hopper 115.

[0025] The material storage yard 101 is a place where concrete materials are stored. The material storage yard 101 includes a plurality of silos 102. The plurality of silos 102 are containers that store at least a portion of the concrete materials by material type. The plurality of silos 102 include, for example, a silo 102 that stores coarse aggregate, a silo 102 that stores fine aggregate, and a silo 102 that stores cement.

[0026] The transporting device 104 is a device that transports the concrete materials stored in the multiple silos 102 to the storage bins 111. The transporting device 104 includes, for example, a belt conveyor that transports the concrete materials. The transporting device 104 may transport the concrete materials by type at different times. In one example, based on an operation instruction from a control device provided in the manufacturing system 1, a specific material from among the various concrete materials is transferred to the transporting device 104 and transported to the storage bins 111.

[0027] The storage bottles 111 temporarily store various types of concrete materials. The various types of concrete materials are transported (conveyed) to the storage bottles 111 from the material storage area 101 by the transport device 104. The storage bottles 111 are configured to individually store various types of concrete materials. Hereinafter, "concrete materials" may be simply referred to as "materials." The various materials stored in the storage bottles 111 are supplied to the measuring bottles 112 as needed.

[0028] The measuring bottle 112 is disposed below the storage bottle 111. The measuring bottle 112 operates based on operational instructions from a control device provided in the manufacturing system 1, and individually measures various materials. When the measuring bottle 112 detects the target amount of material instructed by the control device, it supplies the material to the collecting hopper 113. When water is supplied to the measuring bottle 112, an admixture may be mixed into the water. The collecting hopper 113 is disposed below the measuring bottle 112. The collecting hopper 113 collects the various materials discharged from the measuring bottle 112 and supplies the collected various materials to the mixer 114. Note that the manufacturing apparatus 100 does not necessarily have to be provided with the collecting hopper 113, and the various materials may be supplied to the mixer 114 from the measuring bottle 112.

[0029] The mixer 114 is disposed below the collecting hopper 113. The mixer 114 is a device for mixing concrete materials. The mixer 114 produces ready-mixed concrete by mixing (kneading) aggregate, cement, water, admixtures, etc. The ready-mixed concrete is discharged from the bottom of the mixer 114 into the loading hopper 115. The mixer 114 may be a tilting mixer, a horizontal single-shaft mixer, a horizontal twin-shaft mixer, or a pan-type mixer. The mixer 114 includes, for example, two stirring members 114a and a mixer drive unit 114b.

[0030] The agitating members 114a are members that agitate the various materials supplied to the mixer 114. The two agitating members 114a are arranged side by side inside the main body (container portion) of the mixer 114 and are rotatable. Each of the two agitating members 114a includes a rotation shaft that extends horizontally in one direction. The mixer driving unit 114b rotates the rotation shafts of the two agitating members 114a based on operation instructions from a control device provided in the manufacturing system 1. The mixer driving unit 114b includes, for example, a driving source such as a motor that applies driving force to the agitating members 114a. An opening and closing port is provided at the bottom of the main body of the mixer 114 for discharging the produced ready-mixed concrete into the loading hopper 115.

[0031] The loading hopper 115 is disposed below the mixer 114 and temporarily stores the ready-mixed concrete. The loading hopper 115 supplies the temporarily stored ready-mixed concrete to the transport vehicle 200.

[0032] The manufacturing apparatus 100 described above is an example of a ready-mixed concrete manufacturing apparatus, and the ready-mixed concrete manufacturing apparatus may be configured in any manner as long as it is capable of manufacturing ready-mixed concrete through a process of kneading concrete materials using a mixer. In the present disclosure, the manufacturing of ready-mixed concrete may include not only obtaining ready-mixed concrete by mixing concrete materials, but also transporting the obtained ready-mixed concrete and stirring the ready-mixed concrete during transportation. In one example, the manufacturing system 1 also includes a transport vehicle 200.

[0033] In the present disclosure, a unit of ready-mixed concrete produced by one mixing in the mixer 114 and loaded onto the transport vehicle 200 is defined as "one batch." The process performed by the manufacturing system 1 to produce one batch of ready-mixed concrete is defined as "batch processing." The number of times a batch process has been performed since a certain reference point in time (the cumulative number of times it has been performed) is referred to as the "cumulative batch number." At the reference point in time, the cumulative batch number is reset to zero. For example, the cumulative batch number is reset to zero each day when the manufacturing apparatus 100 starts operating. In this case, the cumulative batch number for the batch process performed the kth time in a day (k is an integer greater than or equal to 1) is k.

[0034] One to three batches of ready-mixed concrete may be loaded onto one transport vehicle 200. For example, when two batches of ready-mixed concrete are loaded onto one transport vehicle 200, two batch processes according to the same production conditions are carried out at different times (in different orders). During a certain period of one day, multiple batch processes according to the same production conditions may be carried out consecutively in order.

[0035] <Quality Prediction Device> The quality prediction device 10 is a device that predicts the quality of ready-mixed concrete produced by the manufacturing apparatus 100. The quality prediction device 10 is configured with one or more computers. When the quality prediction device 10 is configured with multiple computers, these multiple computers are connected to each other so that they can communicate with each other. The quality prediction device 10 may have a function to control the manufacturing apparatus 100 in addition to the function of predicting quality. For example, the quality prediction device 10 is configured as a part of a control device that controls the manufacturing apparatus 100. The quality prediction device 10 may control the manufacturing apparatus 100 in accordance with set operating conditions. At least a part of the operating conditions may be determined by instructions from an operator such as a worker.

[0036] An input device 12 and a monitor 14 may be connected to the quality prediction apparatus 10. The input device 12 is a device that inputs information indicating instructions from a worker or the like to the quality prediction apparatus 10. The input device 12 may be any device that can input desired information, such as a keyboard (keypad), an operation panel, or a mouse. The monitor 14 is a device that displays information from the quality prediction apparatus 10 to a worker or the like. The monitor 14 may be any device that can display graphics, such as a liquid crystal display. The input device 12 and the monitor 14 may be integrated, such as a touch panel. The quality prediction apparatus 10, the input device 12, and the monitor 14 may be integrated, such as a tablet computer (tablet terminal).

[0037] The quality of ready-mixed concrete to be predicted by the quality prediction device 10 includes not only the quality of the ready-mixed concrete itself but also the quality of the ready-mixed concrete after hardening (hardened concrete). Predicting the quality of ready-mixed concrete means calculating a predicted value of an index value representing the quality.

[0038] The quality prediction device 10 predicts, for example, the fresh properties of ready-mixed concrete as the quality of ready-mixed concrete. The quality prediction device 10 may predict, as the quality of ready-mixed concrete, the fresh properties of ready-mixed concrete after it has been obtained by mixing in the manufacturing device 100 and before it is shipped to a site from a factory or the like in which the manufacturing device 100 is installed. Alternatively, the quality prediction device 10 may predict, as the quality of ready-mixed concrete, the fresh properties of ready-mixed concrete after it has been shipped from a factory or the like and before it is poured at a site. In other words, the quality prediction device 10 may predict the fresh properties of ready-mixed concrete during transportation, or may predict the fresh properties of ready-mixed concrete at the time of unloading.

[0039] The quality prediction device 10 may predict one or more index values ​​of slump, slump flow, and air content as fresh properties of ready-mixed concrete. The quality prediction device 10 may predict two or more index values ​​of slump, slump flow, and air content as fresh properties of ready-mixed concrete. The quality prediction device 10 may predict index values ​​other than slump, slump flow, and air content as fresh properties of ready-mixed concrete. Indicators of fresh properties other than slump, slump flow, and air content include, for example, 500 mm flow time, flow stop time, presence or absence of material segregation, temperature, bleeding amount, bleeding rate, setting time, unit water content, plastic viscosity, yield value, funnel flow time, compactibility, deformability, fillability, and gap passability.

[0040] The quality prediction device 10 may predict at least one of an index representing strength and an index representing durability as the quality of hardened fresh concrete. Examples of strength indexes include compressive strength, flexural strength, splitting tensile strength, bond strength, static modulus of elasticity, resilience, and ductility coefficient. Examples of durability indexes include length change rate, expansion rate, mass reduction rate, carbonation depth, chloride ion penetration depth, chloride ion diffusion coefficient, air permeability coefficient, water permeability coefficient, freeze-thaw resistance, electrical resistivity, dynamic modulus of elasticity, Poisson's ratio, and creep coefficient. Hereinafter, "quality of fresh concrete" may be simply referred to as "quality."

[0041] To provide an overview of the functions of the quality prediction device 10, the quality prediction device 10 is configured to at least construct a prediction model by machine learning based on training data including multiple data sets, and to acquire multiple feature quantities obtained when ready-mixed concrete is produced using the mixer 114. The quality prediction device 10 is further configured to use the prediction model to acquire a predicted value of quality corresponding to the acquired multiple feature quantities. The quality prediction device 10 constructs the prediction model such that one or more feature quantities selected from the multiple feature quantities are associated with the predicted value of quality using a decision tree.

[0042] 2 shows an example of functional components (hereinafter referred to as "functional blocks") included in the quality prediction device 10. The quality prediction device 10 has, for example, the following functional blocks: an operation control unit 22, a feature acquisition unit 24, a model construction unit 26, a model holding unit 28, a prediction unit 30, and an output unit 32. The processes executed by these functional blocks correspond to the processes executed by the quality prediction device 10. Below, each functional block will be explained using an example in which the quality to be predicted is the fresh properties of ready-mixed concrete (more specifically, the fresh properties of ready-mixed concrete after being obtained by mixing in the mixer 114 and before being shipped).

[0043] The operation control unit 22 controls the manufacturing apparatus 100 to manufacture ready-mixed concrete in accordance with predetermined operating conditions. At least a portion of the operating conditions may be determined by an operator, such as a worker, each time ready-mixed concrete is manufactured. The operation control unit 22 may control the mixer driving unit 114b of the mixer 114 so that the rotation speed of the mixer driving unit 114b follows a target rotation speed defined in the operating conditions. When controlling the mixer driving unit 114b, the operation control unit 22 may adjust the power (e.g., current value) supplied to the mixer driving unit 114b. If the ready-mixed concrete to be manufactured is hard, the power load value tends to be large, and if the ready-mixed concrete to be manufactured is soft, the power load value tends to be small.

[0044] The feature acquisition unit 24 (acquisition unit) acquires multiple feature quantities obtained when ready-mixed concrete is produced using the mixer 114. Hereinafter, each of the multiple feature quantities acquired by the feature acquisition unit 24 may be referred to as feature quantity F1, feature quantity F2, ..., and feature quantity FN (N is an integer of 2 or greater). The feature quantities F1 to FN are input data to a prediction model for predicting fresh properties. At least some of the feature quantities F1 to FN (multiple feature quantities) may be feature quantities representing the results of the operation of the mixer 114 or feature quantities representing the state of the mixer 114 during operation. The feature quantities F1 to FN may include feature quantities representing the conditions of production using the mixer 114 (for example, the operating conditions described above).

[0045] Each of the feature quantities F1 to FN is data represented by a numerical value. The feature quantities F1 to FN may include data representing a classification (type), in which case a different numerical value (for example, a numerical value of 0 or 1) is assigned to each classification. In one example, a value of 0 can be used to indicate that the data does not fall into that classification, and a value of 1 can be used to indicate that the data does fall into that classification. The feature quantities F1 to FN do not include image data that is configured by combining coordinate information and pixel values. However, the feature quantities F1 to FN may also include feature quantities (one-dimensional numerical data) obtained from images related to such image data.

[0046] The feature quantities F1 to FN include one or more feature quantities related to the power load value of the mixer 114. Hereinafter, the one or more feature quantities related to the power load value among the feature quantities F1 to FN will be referred to as "load feature quantity FL" to distinguish them from other types of feature quantities. The load feature quantity FL related to the power load value of the mixer 114 is, for example, a feature quantity related to the power load value of the mixer 114 when mixing concrete materials. The power load value of the mixer 114 may be a value indicating the power (W) itself supplied to the mixer 114, or may be a value indicating the current value (A) supplied to the mixer 114. Alternatively, the power load value of the mixer 114 may be a value indicating the load hydraulic pressure (Pa) or a value indicating the torque (N m) instead of the power or current value.

[0047] The one or more load feature values ​​FL include, for example, at least one of a value at a predetermined time point in time-series data of the power load value of the mixer 114 when mixing concrete materials and a statistic obtained from the time-series data. The time-series data of the power load value may be a data group (a data group showing changes in the power load value over time) consisting of continuous power load values ​​for at least a part of the period during which the mixer 114 is operating during the processing of one batch. Specific examples of the feature values ​​F1 to FN and the load feature value FL will be described later.

[0048] The model construction unit 26 constructs a prediction model by machine learning based on training data including multiple data sets. Hereinafter, the training data including multiple data sets will be referred to as "training data TD," and the prediction model constructed by the model construction unit 26 will be referred to as "prediction model M." The prediction model M is a model for predicting the fresh properties of ready-mixed concrete when the feature quantities F1 to FN are obtained. The fresh properties predicted by the prediction model M are, for example, slump, slump flow, or air content.

[0049] The model construction unit 26 constructs a prediction model M that shows the relationship between the feature quantities F1 to FN and the freshness properties (predicted values) through machine learning. The prediction model M is configured to output predicted values ​​of the freshness properties in response to input of the feature quantities F1 to FN. The feature quantities F1 to FN can be referred to as explanatory variables, and the freshness properties can be referred to as a target variable.

[0050] Machine learning is a technique in which a machine (computer) autonomously discovers laws or rules by repeatedly learning based on given information. A predictive model M can be constructed using an algorithm and a data structure. A predictive model M is realized using a decision tree, which is a method of analyzing data using a tree structure (tree diagram). A predictive model M outputs a predicted value by sequentially setting conditions for given input data and branching to expected results.

[0051] The model construction unit 26 constructs a prediction model M so that one or more feature quantities selected from the feature quantities F1 to FN are associated with predicted values ​​of fresh properties using a decision tree (decision tree algorithm). The one or more feature quantities used for associating with fresh properties in the prediction model M are selected from the feature quantities F1 to FN by machine learning. LightGBM (Light Gradient Boosting Machine) may be used as the decision tree algorithm. Note that instead of LightGBM, XGBoost (eXtreme Gradient Boosting) or RandomForest may be used as the decision tree algorithm.

[0052] The model construction unit 26 may autonomously construct a prediction model M by performing machine learning using data provided as input for machine learning and correct data (correct values ​​of fresh properties) output from the machine learning. The input for machine learning is the features F1 to FN. The output of the machine learning is data (numerical values) indicating the fresh properties of ready-mixed concrete. The model construction unit 26 iteratively learns a model that outputs predicted values ​​of fresh properties using multiple combinations of the features F1 to FN and the correct values ​​of the fresh properties (the training data TD). In each of the multiple data sets that make up the training data TD, the features F1 to FN are associated with the correct values ​​of the fresh properties.

[0053] The stage in which the prediction model M is autonomously constructed corresponds to the training phase (learning phase). The training phase may be performed before the production phase in which ready-mix concrete is manufactured, or may be performed at the beginning of the production phase. The stage in which the fresh properties are predicted using the prediction model M from the feature quantities F1 to FN (combinations of the values ​​of the feature quantities F1 to FN) for which the fresh properties are unknown corresponds to the evaluation phase. In the following description, the terms "for training" and "for evaluation" may be used to distinguish between the training phase and the evaluation phase, or in which phase the data is used. The same types of feature quantities F1 to FN are used between the training phase and the evaluation phase.

[0054] Here, an example of the analysis process in the prediction model M will be described with reference to FIG. 3 . FIG. 3 shows a tree diagram visualizing an example of the analysis process in the prediction model M. Note that a simplified example will be used for easier understanding of the contents of the present disclosure. In the algorithm using the decision tree exemplified below, one data group is divided into two data groups in stages according to a certain condition, and the stages are referred to in descending order as "first stage," "second stage," and "third stage." Furthermore, the location where division or branching occurs based on a condition is referred to as a "node (condition node)." In FIG. 3 , a combination of "G" such as "G1" and a numerical value represents the name of a data group. Furthermore, a combination of "Th" such as "Th1" represents a threshold value.

[0055] The model construction unit 26 prepares or acquires training data TD consisting of multiple data sets each having a different combination of values ​​of feature quantities F1 to FN. The data group G1 is, for example, all data sets included in the training data TD. In the first stage, the data group G1 is divided into two data groups, data group G21 and data group G22, based on one feature quantity (feature quantity F2 in the example shown in FIG. 3) selected by machine learning from the feature quantities F1 to FN. In the first stage, the data group G1 is divided into two data groups based on whether the feature quantity F2 is smaller than a threshold value Th1 or greater than or equal to the threshold value Th1.

[0056] The data group G21 is composed of a plurality of data sets from the data group G1 whose feature F2 is smaller than the threshold Th1, and the data group G22 is composed of the remaining plurality of data sets from the data group G1 whose feature F2 is equal to or greater than the threshold Th1. The number of data sets in the data group G21 and the number of data sets in the data group G22 may be the same or different. In the first stage, the feature F2 is used to separate the data into two groups, but the values ​​of features other than the feature F2 are also retained in the data group G21 and the data group G22. The threshold Th1 is also set autonomously by machine learning.

[0057] In the second stage, as in the first stage, each of the data groups G21 and G22 is divided into two data groups using a feature selected by machine learning and a threshold. The feature selected in the second stage may be different from or the same as the feature selected in the first stage. The feature selected to divide each of the data groups G21 and G22 into two may be different from or the same as the feature selected in the first stage. In the example shown in FIG. 3 , a feature F1 is selected to divide the data group G21 into two, and a feature F2 is selected to divide the data group G22 into two. In the second stage, the data group G21 is divided into a data group G31 and a data group G32, and the data group G22 is divided into a data group G33 and a data group G34.

[0058] In the example shown in FIG. 3 , in the third stage, the data groups G31, G32, and G34 are not divided into two data groups. That is, the data groups G31, G32, and G34 each correspond to a "leaf" (or "end node") in the tree diagram. In the third stage, the data group G33, which is not a leaf, is divided into two data groups using a feature selected by machine learning and a threshold value, as in the previous stage. A feature F10 is selected to divide the data group G33 into two data groups, and the data group G33 is divided into a data group G41 and a data group G42. The data groups G41 and G42 each correspond to a leaf. As illustrated in FIG. 3 , the leaf depths (levels) may differ among at least some of the multiple leaves in the tree diagram. Alternatively, unlike the example shown in FIG. 3 , the depths of the leaves may be the same.

[0059] For each data group corresponding to a leaf, the predicted value of the fresh property can be determined based on the correct value of the fresh property contained in that data group. For example, the arithmetic mean of the correct values ​​of the fresh property contained in the data group corresponding to the leaf is set as the predicted value for that leaf. In one example, if the data group G41 includes m data sets (m is an integer equal to or greater than 2), the arithmetic mean of the m correct values ​​of the fresh property is set as the predicted value for the leaf corresponding to the data group G41. The predicted values ​​of the fresh property set for multiple leaves differ.

[0060] The model construction unit 26 may perform division (branching) up to the data groups corresponding to leaves, while selecting features to be associated with predicted values ​​of fresh properties and setting thresholds to be used for branching, based on any construction conditions. As described above, in the training phase, the training data TD is divided into two data groups according to the conditions for each stage, and a predicted value of fresh properties is set for each leaf (end node).

[0061] In the evaluation phase, where a prediction model M is used to obtain a predicted value of fresh properties from the evaluation features F1 to FN, the given evaluation features F1 to FN (combinations of the values ​​of the features F1 to FN) are evaluated to determine which leaf they fall into according to the branching conditions for each stage. Each leaf has a predicted value of fresh properties, so a predicted value of fresh properties can be obtained according to the combination of the values ​​of the features F1 to FN.

[0062] In the prediction model M, there are cases where only a selected portion of the feature quantities F1 to FN are used as branching conditions. That is, in the prediction model M, there are cases where only a selected portion of the feature quantities F1 to FN are associated with the predicted value of the fresh property. Even in such cases, the portion of the feature quantities associated with the predicted value of the fresh property are selected from the feature quantities F1 to FN, and therefore the prediction model M still represents the relationship between the feature quantities F1 to FN and the predicted value of the fresh property.

[0063] The model construction unit 26 may construct a corresponding prediction model M for each type of fresh property (a prediction model M that predicts only one type of fresh property). For example, the model construction unit 26 may separately construct a prediction model M for obtaining a predicted value of slump, a prediction model M for obtaining a predicted value of slump flow, and a prediction model M for obtaining a predicted value of air volume.

[0064] 2 , the model holding unit 28 holds (stores) the prediction model M constructed by the model construction unit 26. The prediction model M, which is a trained model, may be transferable between computers. Therefore, the prediction model M constructed in the quality prediction device 10 may be used in a manufacturing system other than the manufacturing system 1.

[0065] The prediction unit 30 uses the prediction model M constructed by the model construction unit 26 to obtain predicted values ​​of fresh properties according to the evaluation feature quantities F1 to FN obtained by the feature quantity acquisition unit 24. For example, the prediction unit 30 inputs the values ​​of the evaluation feature quantities F1 to FN into the prediction model M and obtains predicted values ​​of fresh properties output from the prediction model M. The prediction unit 30 may obtain predicted values ​​for each type of fresh property (for example, for each of slump, slump flow, and air content) using the corresponding prediction model M.

[0066] 3 is constructed, in the prediction model M, the value of the evaluation feature F2, one of the evaluation features F1 to FN, is first compared with a threshold value Th1. If the value of the evaluation feature F2 is equal to or greater than the threshold value Th1, the calculation step proceeds to a node corresponding to the data group G22, where the value of the evaluation feature F2 is compared with a threshold value Th22. If the value of the evaluation feature F2 is smaller than the threshold value Th22, the calculation step proceeds to a node corresponding to the data group G33, where the value of the evaluation feature F10 is compared with a threshold value Th31.

[0067] If the value of the evaluation feature F10 is smaller than the threshold value Th31, the prediction model M outputs the predicted value of the fresh properties set in the leaf (end node) corresponding to the data group G41. By performing the above calculations in the prediction model M, the prediction unit 30 can obtain predicted values ​​of the fresh properties of the ready-mixed concrete to be evaluated from the combination of the values ​​of the evaluation feature F1 to FN.

[0068] The output unit 32 outputs the predicted values ​​of the fresh properties acquired by the prediction unit 30 to the monitor 14. As a result, the predicted values ​​of the fresh properties of the ready-mixed concrete to be evaluated are displayed on the monitor 14, and an operator such as a worker can grasp the predicted values ​​of the fresh properties of the ready-mixed concrete.

[0069] 4 to 7, a specific example of machine learning performed by the model construction unit 26 when constructing a prediction model M based on a decision tree algorithm will be described. The model construction unit 26 may construct the prediction model M by machine learning including ensemble learning. In machine learning including ensemble learning, a single prediction model M may be constructed by combining multiple models.

[0070] 4A schematically illustrates an analytical process for obtaining predicted values ​​using bagging, a technique of ensemble learning. In one example, a first model M1 (weak learner) is generated using a portion of data randomly extracted from the training data TD, and a second model M2 (weak learner) is generated using another portion of data randomly extracted from the training data TD. A portion of the data used to generate the first model M1 may overlap with a portion of the data used to generate the second model M2. In the prediction model M constructed by bagging, for example, the arithmetic mean of the predicted values ​​of the first model M1 and the second model M2 is output as the final predicted value.

[0071] For simplicity, FIG. 4A illustrates the construction of a prediction model M from the fusion of two models (weak learners). However, a prediction model M that ultimately outputs one predicted value may be constructed from three or more models (weak learners). A prediction model M that ultimately outputs one predicted value may be constructed by using RandomForest, in which models (weak learners) generated from a portion of data, such as a first model M1, are generated using a portion of features randomly selected for each model. Note that RandomForest can also be considered a bagging technique.

[0072] The model construction unit 26 may construct the prediction model M by machine learning using boosting, which is machine learning including ensemble learning. In machine learning using boosting, intermediate models Mtk (weak learners) are generated sequentially using a decision tree algorithm. Note that k is an integer from 1 to K, where K is an integer equal to or greater than 2. If the intermediate models Mtk are generated in order of k from 1 to K, in machine learning using boosting, an intermediate model Mt1 is generated first. Then, focusing on the error between the predicted value in the intermediate model Mt1 and the correct value, the next intermediate model, intermediate model Mt2, is generated. The single predicted value finally output by the prediction model M is a value obtained by aggregating the outputs of the intermediate models Mt1 to MtK through some kind of calculation.

[0073] The model construction unit 26 may construct a prediction model M consisting of intermediate models Mt1 to MtK using, for example, "Adaboost," a boosting technique. FIG. 4B schematically illustrates the process of constructing a model using Adaboost. The weights of the training data TD used to generate the next intermediate model Mt2 are updated based on the error between the predicted value (1) of the previous intermediate model Mt1 and the correct value. For example, the weights are updated so that they are larger for data sets with larger errors. Thereafter, the weight update and generation of the intermediate model Mtk are repeated, for example, up to a set number of times, K times.

[0074] The model construction unit 26 may construct the prediction model M by machine learning using gradient boosting as the machine learning using boosting. That is, the model construction unit 26 may construct the prediction model M by machine learning using a gradient boosting decision tree. In the gradient boosting tree, the prediction model M is constructed by combining boosting, gradient descent, and a decision tree.

[0075] FIG. 5 schematically illustrates the process of constructing a model using gradient boosting. In gradient boosting, an intermediate model Mt1 is first generated, and the error between a predicted value (1) based on the intermediate model Mt1 and a correct value is calculated. Then, an intermediate model Mt2 capable of predicting the error in the predicted value (1) is generated, and a correction value (2) for correcting the predicted value (1) is calculated from the intermediate model Mt2. Thereafter, calculation of a correction value (k) for correcting the current predicted value (k) is repeated up to, for example, a set number of times, K. The correction value (k) is also referred to as a gradient (k).

[0076] In addition to the above-mentioned LightGBM and XGBoost, CatBoost is also an example of a decision tree algorithm that includes gradient boosting. In XGBoost, node branching (splitting) is performed level-wise, as shown in FIG. 6(a). That is, all nodes are branched from the left. On the other hand, in LightGBM, node branching (splitting) is performed leaf-wise, as shown in FIG. 6(b). That is, branching is performed by narrowing down to nodes that should be branched (for example, by prioritizing nodes that result in smaller losses). For nodes that no longer require branching, no further calculations are performed.

[0077] Furthermore, LightGBM utilizes a histogram-based algorithm when branching. FIG. 7(a) schematically illustrates the decision-making process when determining branching using a pre-sorted algorithm, which is different from the histogram-based algorithm. FIG. 7(b) schematically illustrates the decision-making process when determining branching using the histogram-based algorithm. In FIGS. 7(a) and 7(b), each of "d1" to "d6" represents one piece of data (or one data set). The pre-sorted algorithm checks each value in data d1 to d6 and then determines where to branch (split). On the other hand, the histogram-based algorithm groups the values ​​in data d1 to d6 using a histogram, and determines where to branch (split) for each group (group).

[0078] Furthermore, LightGBM uses a technique called GOSS (Gradient-based One-Side Sampling) to reduce the amount of data used during training. Specifically, for data with large errors, all of the data is used as data that has not yet been trained, while for data with small errors, only a portion of the data is used as it has been trained to a certain extent. In addition, LightGBM uses a technique called FEB (Exclusive Feature Bundling) to combine multiple features of different types that do not simultaneously become zero (have low correlation) and can be combined without any problems into a single feature for calculation.

[0079] <Specific Examples of Load Feature FL> Next, specific examples of one or more load feature FL included in the feature F1 to FN will be described with reference to Fig. 8 as well. The feature F1 to FN include, for example, one or more types of load feature FL among the multiple types of load feature FL exemplified below. Fig. 8(a) schematically shows time-series data related to the power load value of the mixer 114 in one batch of processing. The time-series data related to the power load value is, for example, data obtained by repeatedly measuring the power (kW) supplied to the mixer 114 at a predetermined sampling period.

[0080] In the graph showing the time series data in FIG. 8A, "time t1" indicates the time when the mixer 114 starts mixing, and "time t2" indicates the time when the mixer 114 finishes mixing. Time t1 corresponds to, for example, the time when the operation control unit 22 starts driving the agitating member 114a. Time t2 corresponds to, for example, the time when the operation control unit 22 determines that the condition for finishing mixing by the mixer 114 is met. In one example, when a predetermined time has elapsed since the start of driving the agitating member 114a, the operation control unit 22 determines that the above condition is met and stops driving the agitating member 114a.

[0081] The one or more load feature quantities FL may include values ​​at a predetermined time point in the time series data of the power load value. The predetermined time point is a time point set in advance by an operator or the like. The one or more load feature quantities FL may include one or more values ​​of an initial value P1, a minimum value P2, a maximum value P3, and a final value P4. The initial value P1 is the power load value at the start of mixing in the time series data. The minimum value P2 is the power load value at the minimum point after the time (time t1) when the initial value P1 is obtained. Note that the power load value at the start of mixing (initial value P1) may also be the minimum. The maximum value P3 is the power load value at the maximum point in the time series data. The final value P4 is the power load value at the end of the time series data.

[0082] The period of time-series data that is the target for obtaining the load feature FL is not limited to the period from time t1 to time t2. The period of time-series data that is the target for obtaining the load feature FL may be a period starting from time t1 and ending at a point when an arbitrary elapsed time tp has elapsed since time t1. In the example shown in FIG. 8( a), the elapsed time tp corresponds to the difference between time t2 and time t1. In this case, the final value P4 corresponds to the power load value at the time when mixing by the mixer 114 is completed.

[0083] Unlike the time-series data shown in Fig. 8(a), as shown in Fig. 8(b), one or more values ​​of an initial value P1, a minimum value P2, a maximum value P3, and a final value P4 may be acquired as the load feature FL from the time-series data for a period from time t1 to a time before time t2. The start point of the period of time-series data that is the subject of obtaining the load feature FL may be a time after time t1. For example, the start point and end point of the period are set so that the period of time-series data that is the subject of obtaining the load feature FL is the same between the training phase and the evaluation phase.

[0084] The one or more load features FL may include, instead of or in addition to the power load values ​​at the predetermined time points, statistics obtained from time-series data of power load values. The statistics as the load features FL are one or more values ​​selected from the group consisting of a fluctuation range, a decline width, a total value, a mean value, a standard deviation, a coefficient of variation, a median, a first quartile, a third quartile, kurtosis, and skewness. The fluctuation range is calculated by the difference between the maximum value P3 and the minimum value P2. The decline width is calculated by the difference between the maximum value P3 and the final value P4.

[0085] Statistical quantities other than the fluctuation range and the decline width may be calculated from a data group of power load values ​​included in at least a portion of the time series data (a set of power load values ​​obtained for each sampling period). For example, the total value may be calculated by accumulating the power load values ​​for each sampling period for at least a portion of the time series data. Note that some of the data of the power load values ​​for each sampling period may be thinned out before calculating statistical quantities such as the total value. The time series data for obtaining statistical quantities may be data representing a moving average of the power load values ​​instead of data representing the time change of the power load values ​​themselves. The one or more load features FL may include features obtained from the waveform of the time series data and / or features obtained from an image displaying the time series data.

[0086] The sampling period for measuring the power load value may be 0.01 to 1 second (e.g., 0.1 second). Measurement of the power load value may be started immediately after mixing of the concrete materials other than water begins, or immediately after dry mixing (mixing of the concrete materials other than water) and adding water to the concrete materials other than water and starting mixing. The number of power load values ​​measured varies depending on the measurement interval of the power load value and the mixing time, but from the viewpoint of predicting the fresh properties with higher accuracy, it is preferably 1,000 or more, more preferably 2,000 or more, and particularly preferably 2,500 or more. The power load value can be measured by a device equipped with the same power source as the mixer 114.

[0087] The one or more load features FL may include a change in the power load value or information (numerical data) representing a change pattern of the power load value. From the viewpoint of predicting fresh properties with higher accuracy and at an earlier stage, data on the power load value after the start of mixing water and concrete materials other than water and the point at which the power load value of the mixer 114 stabilizes (the point at which the change in the power load value becomes small) may be used as the load feature FL. When mixing concrete materials, the determination of when the power load value of the mixer 114 stabilizes varies depending on the water-cement ratio of the ready-mixed concrete, etc. For example, the mixer power load value may be determined to be stable when any of the following conditions (1) to (3) is satisfied: (1) During a period in which the power load value is decreasing, the rate of change in the power load value at predetermined intervals (e.g., 1 second) longer than the sampling period is within ±1% continuously for a predetermined set time (e.g., 3 seconds) or more. The rate of change of the power load value is calculated, for example, by [P(t) - P(t-1)] / P(t-1), where P() is the power load value at each time, t is the current measurement time, and t-1 is the measurement time immediately before that. (2) If the water-cement ratio of the ready-mixed concrete is about 50 to 70%, the rate of change is about 30 seconds after the start of mixing water and the concrete materials other than water (for example, after the concrete materials other than water are poured into the mixer and dry-mixed, water is poured into the mixer and mixing is started). (3) If the water-cement ratio is reduced (less than 50%) from the viewpoint of strength development, the time is delayed. For high-strength concrete, the rate of change is about 1 to 10 minutes after the start of mixing water and the concrete materials other than water.

[0088] <Specific Examples of Feature Amounts Other than Load Feature Amount FL> The feature amounts F1 to FN may include, as information other than the load feature amount FL, one or more feature amounts (hereinafter referred to as "time feature amounts") related to the time when the mixer 114 mixes the concrete materials. For example, the one or more time feature amounts include at least one of the date and time when the mixer 114 mixes the concrete materials, the cumulative number of batches mixed by the mixer 114 in one day, the time from an arbitrarily set reference time point to the time when the mixer 114 mixed the concrete materials, and the cumulative number of batches mixed by the mixer 114 from the arbitrarily set reference time point.

[0089] The date when the mixer 114 performs mixing represents a specific day when the mixer 114 produced ready-mixed concrete, as a combination of month and day. In this case, the specific month may be one feature, and the specific day may be another feature. The time when the mixer 114 performs mixing represents a specific point in time when the mixer 114 produced ready-mixed concrete, as a combination of hour, minute, and second, or a combination of hour and minute. In this case, the specific hour may be one feature, and the specific minute (or each of the specific minute and second) may be another feature.

[0090] The time (the specific time when the mixer 114 produces ready-mixed concrete) as a feature quantity may be the time when concrete materials are supplied to the mixer 114, or the time when ready-mixed concrete is discharged from the mixer 114. The time (the specific time when the mixer 114 produces ready-mixed concrete) as a feature quantity may be the time when the drive of the agitating member 114a in the mixer 114 is started, or the time when the drive of the agitating member 114a is stopped.

[0091] The month and day when the mixer 114 performs mixing, as well as the hour, minute, and second when the mixer 114 performs mixing, may be specified using trigonometric functions. First, with reference to FIG. 9 , specifying (representing) the "hour" of time using trigonometric functions will be described. Typically, the "hour" of a day is specified (represented) using 24 numerical values ​​from 0:00 to 23:00, assuming that it returns to 0:00 at midnight. When specifying using such normal numerical values, for example, even though there is only a one-hour difference between 0:00 and 23:00 without taking the date into consideration, there is a large numerical difference between them. Therefore, the "hour" may be specified (represented) to have periodicity using trigonometric functions.

[0092] In one example, h o'clock (h is any integer between 0 and 23) is expressed as a combination of cos{2π×(h / 24)} and sin{2π×(h / 24)}, as shown in Figure 9. In this case, midnight is (1, 0), 6 o'clock is (0, 1), 12 o'clock is (-1, 0), and 18 o'clock is (0, -1). In the input of the prediction model M, cos{2π×(h / 24)} may be one feature quantity, and sin{2π×(h / 24)} may be another feature quantity.

[0093] The m minutes (m is any integer between 0 and 59) and s seconds (s is any integer between 0 and 59) may also be represented by the following combinations. In addition, in the input of the prediction model M, similar to "hour," each of the two numerical values ​​may be one feature. - m minutes: cos{2π×(m / 60)}, sin{2π×(m / 60)} - s seconds: cos{2π×(s / 60)}, sin{2π×(s / 60)}

[0094] If the "month" of a date is M (M is any integer between 1 and 12) and the "day" is D (D is any integer between 1 and Dm), then month M and day D may each be represented by the following combinations. Dm is determined for each month and is an integer between 28, 29, 30, and 31. In the input of the prediction model M, as with the "hour," each of the two numerical values ​​may be a single feature. Month M: cos[2π×{(M-1) / 12}], sin[2π×{(M-1) / 12}] Day D: cos[2π×{(D-1) / Dm}], sin[2π×{(D-1) / Dm}]

[0095] The cumulative number of batches per day related to mixing by the mixer 114 is information that identifies the batch process executed that day (the batch process executed in which the ready-mixed concrete was produced). The timing at which mixing by the mixer 114 was executed can also be indicated by the batch process executed in which the ready-mixed concrete of interest was produced.

[0096] When the time from an arbitrarily set reference time point to the time point when the mixer 114 performs mixing is used as a feature, the reference time point may be set by an operator such as a worker. The reference time point may be set to a time point after the mixer 114 is cleaned during maintenance of the manufacturing apparatus 100 and before production by the mixer 114 is resumed. Note that the time point before the restart also includes a time point before production by the mixer 114 is started for the first time in a day in the case where the mixer 114 is cleaned after a daily shutdown. The time between the reference time point and the time point when the mixer 114 performs mixing (production of ready-mix concrete) may be expressed in minutes or seconds.

[0097] The time when the mixer 114 performs mixing (production of ready-mixed concrete) may be the time when concrete materials are supplied to the mixer 114, or the time when the produced ready-mixed concrete is discharged from the mixer 114. The time when the mixer 114 performs mixing (production of ready-mixed concrete) may be the time when the mixer 114 starts driving the agitating member 114a, or the time when the mixer 114 stops driving the agitating member 114a. The quality prediction device 10 may measure the time from a reference time point to the time when the mixer 114 performs mixing.

[0098] The cumulative batch number from an arbitrarily set reference point in time is information that specifies which batch process is to be executed from the reference point in time (which batch process from the reference point in time the ready-mixed concrete was produced in). In the cumulative batch number from the reference point in time, the cumulative batch number is reset to 0 at the reference point in time. The reference point in time for counting the cumulative batch number may also be set to a point in time after the mixer 114 has been cleaned during maintenance of the manufacturing apparatus 100 and before production by the mixer 114 is resumed (including a point in time before production by the mixer 114 is started for the first time in a day).

[0099] The feature quantities F1 to FN may include information related to mixing by the mixer 114 (information other than the power load value). In addition to the elapsed time tp, examples of information related to mixing include the amount of concrete mixed for one batch, the mixing time, the time when the power load value reaches its maximum value, the time from when the power load value reaches its maximum value until the ready-mixed concrete is discharged from the mixer 114, and the time from when the power load value reaches its maximum value until the measurement time mp (final value P4) described below. The feature quantities F1 to FN may also include nominal strength, information indicating the type of cement, information indicating the type of admixture, information indicating the type of fine aggregate, information indicating the type of coarse aggregate, and information indicating the amount of additive added. The information indicating the type of cement, etc. may include how the types of cement, etc. are classified and each type identified. The feature quantities F1 to FN may also include numerical data obtained from images of ready-mixed concrete being produced by the mixer 114 or immediately after production, or numerical data obtained from images of ready-mixed concrete after it has been discharged from the mixer 114. The feature amounts F1 to FN may include at least one of a target quality when the ready-mixed concrete is used and a target quality when the ready-mixed concrete is shipped.

[0100] Further examples of features other than the load feature FL included in the features F1 to FN are given below. Note that some of the features overlap with the examples described above. In addition to the load feature FL, the features F1 to FN may include one or more types of data selected from data on the blending conditions of ready-mixed concrete, data on the quality of the target ready-mixed concrete, data on cement, which is a concrete material, data on concrete materials other than cement, data on concrete material mixing equipment, and data on the environment during the production (including transportation) of ready-mixed concrete.

[0101] (Data Related to Mixing Conditions of Ready-Mixed Concrete) The feature amounts F1 to FN may include, as data related to the mixing conditions of ready-mixed concrete, one or more types of data selected from the data group shown in (i) to (iv) below. (i) The type and origin of the materials used (or product name); (ii) At least one of the amounts (mass, weight, volume) and densities of cement, water, fine aggregate, coarse aggregate, various admixtures (ground granulated blast furnace slag, fly ash, silica fume, expansive additive, fine volcanic glass powder, crushed stone powder, metakaolin, etc.), various admixtures (AE agents, water reducers, AE water reducers, high-performance water reducers, high-performance AE water reducers, superplasticizers, setting retarders, hardening accelerators, shrinkage reducers, etc.), and various fibers (steel fibers, glass fibers, carbon fibers, aramid fibers, nylon fibers, vinylon fibers, polyethylene fibers, polypropylene fibers, etc.); and (iii) The water-cement ratio, water-binder ratio, air content, fine aggregate percentage, bulk volume of coarse aggregate, maximum size of coarse aggregate, coarse particle percentage, particle shape determination actual volume percentage, total amount of alkali in concrete, sludge solids percentage, and recovered aggregate replacement percentage. (iv) Amount of stabilizer used in adhesive mortar and sludge water

[0102] (Data Related to Target Quality of Ready-Mixed Concrete) The feature quantities F1 to FN may include one or more types of data selected from the group of data shown in (i) to (iii) below as data related to the target quality of ready-mixed concrete. (i) Target slump, slump flow, air content, time to reach 500 mm flow, time to stop flowing, appearance (still image or moving image), temperature, unit water content, plastic viscosity, yield value, funnel flow time, compactibility, deformability, fillability, and gap passability at the time of shipping, transportation, unloading, or pouring of ready-mixed concrete. (ii) Concrete strength (design standard strength, durability design standard strength, quality standard strength, structural strength correction value (S value), mix control strength, nominal strength, etc.) (iii) Design static elastic modulus, resilience, toughness coefficient, chloride content in concrete, length change rate, expansion rate, mass reduction rate, carbonation depth, chloride ion penetration depth, chloride ion diffusion coefficient, porosity, air permeability coefficient, water permeability coefficient, air bubble spacing coefficient, electrical resistivity, dynamic elastic modulus, Poisson's ratio, creep coefficient, and color. The strength, slump, slump flow, air content, and chloride content of concrete can be measured by the test method described in JIS A 5308:2024 (ready-mixed concrete).

[0103] (Data on Cement) The feature quantities F1 to FN may include, as data on cement, which is a concrete material, one or more types of data selected from (a) data on cement as a whole, (b) data on raw materials for cement clinker, (c) data on burning conditions for cement clinker, (d) data on grinding conditions for cement, and (e) data on cement clinker.

[0104] The feature quantities F1 to FN may include one or more types of data selected from the group of data shown in (a1) to (a3) ​​below as data related to the entire cement: (a1) Type, chemical composition, mineral composition, wet f. CaO, loss on ignition, Blaine specific surface area, particle size distribution, sieve test residue amount, and color of cement used as concrete material; (a2) Mineralogical properties and crystallographic properties of each mineral contained in cement; and (a3) ​​Hemihydration rate of gypsum contained in cement.

[0105] The feature quantities F1 to FN may include one or more types of data selected from the data group shown in (b1) to (b4) below as data related to the raw materials of cement clinker. (b1) The chemical composition, hydraulic hardness, sieve test residue, Blaine specific surface area (fineness), loss on ignition, supply amount, supply amount of auxiliary materials (special raw materials such as waste), amount stored in the blending silo (remaining amount), and amount stored in the storage silo (remaining amount) of the raw materials mixed for cement clinker. (b2) The current value of the cyclone located between the raw material mill and the blending silo for the mixed raw materials (representing the rotation speed of the cyclone, which is correlated with the speed of the raw materials passing through the cyclone). (b3) The chemical composition and hydraulic hardness of the raw materials mixed for cement clinker (the raw materials mixed for cement clinker from which fine particles and the like have been removed by a countercurrent airflow during transportation; hereinafter referred to as the raw materials fed into the kiln) at a predetermined time before the time of feeding into the kiln (for example, one time point 5 hours before, or multiple time points such as four time points 3 hours before, 4 hours before, 5 hours before, and 6 hours before). (b4) Chemical composition, hydraulic hardness, Blaine specific surface area, sieve test residue, decarbonation rate, and moisture content of raw materials obtained by mixing raw materials and auxiliary materials for cement clinker

[0106] The feature quantities F1 to FN may include one or more types of data selected from the group of data shown in the following (c1) to (c3) as data on the burning conditions of the cement clinker: (c1) the amount of cement clinker raw materials fed into the kiln, the kiln rotation speed, the outlet temperature, the burning zone temperature, the temperature of the cement clinker, the average kiln torque, the O2 concentration, and the NOx concentration during the burning of the cement clinker; (c2) the temperature of the clinker cooler; and (c3) the flow rate of the preheater gas (which is correlated with the preheater temperature).

[0107] The feature quantities F1 to FN may include (d) data related to the cement grinding conditions, which may include one or more types of data selected from the grinding temperature, the amount of water sprayed in the finishing mill, the separator air volume, the type of gypsum, the amount of gypsum added, the amount of cement clinker added, the rotation speed of the finishing mill, the temperature of the powder discharged from the finishing mill, the amount of powder discharged from the finishing mill, and the amount of powder not discharged from the finishing mill.

[0108] The feature quantities F1 to FN may include one or more types of data selected from the group of data shown in the following (e1) to (e3) as data on cement clinker: (e1) mineral composition, chemical composition, wet f. CaO (free lime), and volume weight of cement clinker; (e2) crystallographic properties (lattice constant, crystallite size, etc.) of each mineral contained in cement clinker; and (e3) ratio of two or more mineral compositions contained in cement clinker.

[0109] (Data Related to Concrete Materials Other Than Cement) The feature quantities F1 to FN may include, as data related to concrete materials other than cement, one or more types of data selected from the data group shown in (i) to (iv) below. (i) Classification of aggregate (at least one of fine aggregate and coarse aggregate), type, bone dry density, surface dry density, water absorption rate, moisture content, surface moisture rate, mass fraction lost in stability test, abrasion loss, maximum size, particle size, coarse particle rate, mass fraction of material retained between successive sieves, actual volume fraction for particle shape determination, amount of fine particles, amount of clay lumps, organic impurities, amount of chloride, and alkali-silica reactivity. (ii) Type, density, specific surface area, 45 μm sieve residue, 1.2 mm sieve residue, flow value ratio, activity index, moisture content, expansiveness (length change rate), and content of various chemical components (silicon dioxide: SiO2, magnesium oxide: MgO, aluminum oxide: Al2O3, sulfur trioxide: SO3, free calcium oxide: f.CaO, free silicon: f.Si, etc.). (iii) Type, components, density, appearance (color), chloride ion content, total alkali content, water reduction rate, flow value ratio, bleeding amount ratio, bleeding amount difference, difference in setting time (initial time, final setting time), compressive strength ratio, length change ratio, resistance to freezing and thawing (relative dynamic modulus of elasticity), and change over time. (iv) Type, density, nominal diameter, nominal length, shape, fineness, tensile strength, tensile modulus of elasticity, adhered moisture content, melting temperature, and alkali resistance (strength retention rate).

[0110] (Data Related to Concrete Material Mixing Equipment) The feature quantities F1 to FN may include one or more types of data selected from the data group shown in (i) and (ii) below as data related to concrete material mixing equipment. (i) Type, model, product name, manufacturer name, manufacturing year, rated capacity, total output, production capacity, main body mass, unloaded mass during operation, external dimensions, tilt angle (in the case of a drum type), rotation speed (drum, mixing blade, mixing shaft), and mixing performance (deviation rate of air amount in concrete, deviation rate of mortar amount in concrete, deviation rate of coarse aggregate in concrete, deviation rate of consistency (slump), deviation rate of compressive strength) of the mixer 114. (ii) Information related to the order in which materials are added, mixing amount, addition time, mixing time, discharge time, re-addition time, and cycle time

[0111] (Environmental Data) The feature quantities F1 to FN may include one or more types of data selected from the group of data shown in (i) to (iii) below as data related to the environment during concrete production (including transportation): (i) Outdoor temperature and humidity, atmospheric pressure, amount of solar radiation, hours of sunshine, amount of rainfall, wind speed, wind direction, weather, climate, and climate (ii) Temperature of each concrete material, temperature and humidity of the place where the materials are stored (inside containers such as silos and storage jars), and temperature of the mixer or inside the mixer (iii) Vehicle information of the truck agitator, load capacity, transportation time, transportation distance, temperature and humidity of the drum or inside the drum, information on road traffic conditions, and information on vibrations applied to the vehicle

[0112] 10 , the quality prediction device 10 includes a circuit 50. The circuit 50 includes a processor 51, a memory 52, a storage 53, an input / output port 54, and a timer 55. The storage 53 is configured with one or more nonvolatile memory devices such as a flash memory or a hard disk. The storage 53 stores at least a quality prediction program that causes a computer to execute a construction process, an acquisition process, and a prediction process, which will be described later. The storage 53 stores a quality prediction program for configuring each functional block of the quality prediction device 10.

[0113] The memory 52 is composed of one or more volatile memory devices such as a random access memory. The memory 52 temporarily stores a quality prediction program loaded from the storage 53. The processor 51 is composed of one or more arithmetic devices such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The processor 51 configures each functional block of the quality prediction device 10 by executing the quality prediction program loaded into the memory 52. ​​The calculation results by the processor 51 are temporarily stored in the memory 52. ​​The input / output port 54 inputs and outputs information to and from the input device 12, the monitor 14, the mixer 114, etc. in response to a request from the processor 51.

[0114] The timer 55 measures the elapsed time by, for example, counting a reference pulse at a fixed interval. The circuit 50 is not necessarily limited to one in which each function is configured by a program. For example, the circuit 50 may be configured with at least some of its functions by a dedicated logic circuit or an ASIC (Application Specific Integrated Circuit) that integrates such a dedicated logic circuit. The quality prediction program may be provided by being permanently recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the quality prediction program may be provided via a communication network as a data signal superimposed on a carrier wave.

[0115] [Method for manufacturing ready-mixed concrete] Next, an example of a method for manufacturing ready-mixed concrete executed in the manufacturing system 1 will be described. The method for manufacturing ready-mixed concrete includes a manufacturing process and a quality prediction process. The manufacturing process is a process for manufacturing ready-mixed concrete. The quality prediction process is a process for predicting the fresh properties of the ready-mixed concrete after or during the manufacturing process. The quality prediction process may be executed during a period that overlaps with at least a portion of the period during which the manufacturing process is repeatedly executed.

[0116] The manufacturing process includes, for example, a transporting process, a weighing process, a feeding process, a mixing process, a discharging process, and a loading process. In the transporting process, various types of concrete materials are transported to storage bottles 111 by a transporting device 104, and the various materials are individually supplied to the storage bottles 111. In the weighing process, the various materials are individually supplied from the storage bottles 111 to weighing bottles 112, and the various materials are weighed in the weighing bottles 112. In the weighing process, when the measured amount of each material reaches a predetermined set amount, the material is discharged into a collecting hopper 113. In the feeding process, after all types of materials are collected in the collecting hopper 113, the materials in the collecting hopper 113 are fed (supplied) to a mixer 114.

[0117] In the mixing process, multiple types of concrete materials are mixed in the mixer 114. In the mixing process, the quality prediction device 10, which has a function of controlling the manufacturing apparatus 100, may control the mixer driving unit 114b in accordance with predetermined operating conditions. In the mixing process, the power supplied from the quality prediction device 10 to the mixer driving unit 114b may be adjusted so that the rotation speed of the mixer driving unit 114b follows a target rotation speed.

[0118] In the discharging process, after mixing of the concrete materials in the mixer 114 is completed, the ready-mixed concrete is discharged from the mixer 114 into the loading hopper 115. In the loading process, the ready-mixed concrete discharged into the loading hopper 115 is loaded onto the transport vehicle 200.

[0119] The quality prediction process (method for predicting the quality of ready-mixed concrete) includes a model construction process in a learning phase and a quality evaluation process in an evaluation phase. In the quality prediction process, the model construction process is executed before the quality evaluation process.

[0120] The model construction process includes a construction step. The construction step is a step of constructing a prediction model M by machine learning based on training data TD including multiple data sets. In the construction step, the prediction model M is constructed so that one or more feature quantities selected from multiple feature quantities F1 to FN correspond to predicted values ​​of fresh properties using a decision tree. The construction step may be performed by the model construction unit 26 of the quality prediction device 10. In each of the multiple data sets in the training data TD, the training feature quantities F1 to FN correspond to the correct value of the fresh properties.

[0121] The quality evaluation process includes an acquisition process and a prediction process. The acquisition process is a process of acquiring evaluation feature quantities F1 to FN obtained when producing ready-mixed concrete using the mixer 114. The evaluation feature quantities F1 to FN are the same type of data as the training feature quantities F1 to FN. The feature quantities F1 to FN include at least one or more load feature quantities FL related to the power load value of the mixer 114. The acquisition process may be performed by the feature quantity acquisition unit 24 of the quality prediction device 10.

[0122] The prediction step is a step of acquiring predicted values ​​of fresh properties according to the evaluation features F1 to FN acquired in the acquisition step, using the prediction model M constructed in the construction step. The acquisition step may be performed by the prediction unit 30 of the quality prediction device 10. The quality evaluation step may include an output step. The output step is a step of outputting the predicted values ​​of fresh properties acquired in the prediction step to the monitor 14. The output step may be performed by the output unit 32 of the quality prediction device 10.

[0123] 11(a) is a flowchart showing an example of a series of processes executed in the model building process. This model building process is executed before the production phase including the above-mentioned manufacturing process is executed in the manufacturing apparatus 100, or at the beginning of the start of the above-mentioned production phase. In this model building process, for example, ready-mixed concrete actually manufactured in the manufacturing apparatus 100 is used as ready-mixed concrete for training.

[0124] In the model building process, step S11 is executed first. In step S11, training data TD for machine learning is prepared by, for example, an operator such as a worker. The training data TD is composed of multiple data sets as described above. Each of the multiple data sets in the training data TD includes feature quantities F1 to FN (training input information) obtained when training ready-mixed concrete is produced, and correct values ​​of fresh properties (e.g., slump) associated with the feature quantities F1 to FN.

[0125] The correct values ​​of the fresh properties may be values ​​obtained by actually measuring the fresh properties of the training ready-mixed concrete. In one example, after the training ready-mixed concrete is loaded onto the transport vehicle 200, a portion of the ready-mixed concrete is extracted by a worker or the like. The slump, slump flow, air content, etc. of the extracted ready-mixed concrete are then measured by the worker or the like, and at least a portion of these measured values ​​are used as the correct values ​​in the training data TD.

[0126] Next, step S12 is executed. In step S12, for example, the model construction unit 26 of the quality prediction device 10 constructs a prediction model M by performing machine learning using the training data TD prepared in step S11. The model construction unit 26 may construct the prediction model M by machine learning using a decision tree algorithm. The model construction unit 26 may construct the prediction model M by performing machine learning involving ensemble learning in machine learning using a decision tree algorithm. The model construction unit 26 may construct the prediction model M by performing machine learning using boosting (e.g., gradient boosting) in machine learning involving ensemble learning. In one example, the model construction unit 26 constructs the prediction model M using LightGBM developed by Microsoft (registered trademark).

[0127] The model construction unit 26 may construct one or more models from among a prediction model M that outputs a predicted value of slump, a prediction model M that outputs a predicted value of slump flow, and a prediction model M that outputs a predicted value of air volume. The model construction unit 26 may construct a prediction model M that outputs the ratio of slump flow to slump as a fresh property.

[0128] Next, step S13 is executed. In step S13, for example, the model holding unit 28 stores the prediction model M constructed in step S12. This completes the model construction process.

[0129] 11(b) is a flowchart showing an example of a series of processes executed in the quality evaluation process. This quality evaluation process is carried out, for example, during a period that overlaps with at least a part of the period during which the above-mentioned production process for the ready-mixed concrete to be evaluated is executed.

[0130] In the quality evaluation process, first, the quality prediction device 10 executes step S21. In step S21, for example, the quality prediction device 10 waits until the evaluation timing, which is the timing to evaluate the quality of the ready-mixed concrete to be evaluated. The evaluation timing may be predetermined to a certain time period in a day, or may be predetermined to the timing of executing a certain number of batch processes in a day. The evaluation timing may also be the timing at which an instruction to execute the evaluation is received from an operator such as a worker.

[0131] Next, the quality prediction device 10 executes step S22. In step S22, for example, the feature acquisition unit 24 acquires feature quantities F1 to FN when the ready-mixed concrete to be evaluated is produced. The feature quantities F1 to FN acquired in step S22 are input data for evaluation whose fresh properties (e.g., slump) are unknown.

[0132] Next, the quality prediction device 10 executes step S23. In step S23, for example, the prediction unit 30 predicts the fresh properties of the ready-mixed concrete to be evaluated based on the evaluation feature quantities F1 to FN acquired in step S22 and the prediction model M stored in the model storage unit 28. In one example, the prediction unit 30 inputs the evaluation feature quantities F1 to FN acquired in step S22 into the prediction model M, and acquires predicted values ​​of the fresh properties output from the prediction model M.

[0133] Next, the quality prediction device 10 executes step S24. In step S24, for example, the output unit 32 displays the predicted values ​​of the fresh properties acquired in step S23 on the monitor 14. This allows an operator such as a worker to check the prediction results regarding the fresh properties of the ready-mixed concrete to be evaluated.

[0134] This completes the quality evaluation process. The quality prediction device 10 may execute the series of processes from steps S21 to S24 each time one batch of ready-mixed concrete is produced (for each batch processing). The quality prediction device 10 may execute the series of processes from steps S21 to S24 each time multiple batches of ready-mixed concrete are produced (for each multiple batch processing).

[0135] 11(a) and 11(b) are merely examples and can be modified as appropriate. In the series of processes, one step and the next step may be executed in parallel, or some steps may be executed in an order different from that of the example described above. Steps different from those of the example described above may be executed instead of or in addition to at least some of the steps of the series of processes.

[0136] In addition to the quality prediction device 10 predicting the quality of ready-mixed concrete, the manufacturing apparatus 100 may measure the fresh properties of the ready-mixed concrete periodically (for example, once to 100 times per day). In this case, the prediction model M may be updated based on the actual measured values ​​of the fresh properties of the ready-mixed concrete and the feature quantities F1 to FN when the actual measured values ​​were obtained. In the prediction step, the fresh properties of the ready-mixed concrete may be predicted using the updated prediction model M. Note that even when the updated prediction model M is used, the step of predicting the fresh properties of the ready-mixed concrete is still performed based on the prediction model M and the feature quantities F1 to FN acquired in the acquisition step.

[0137] In the above example, the quality prediction device 10 predicts the quality of ready-mixed concrete obtained by mixing using the mixer 114 of the manufacturing apparatus 100 and before shipping to the construction site. The timing at which the quality prediction device 10 predicts the quality is not limited to this example. The quality prediction device 10 may also predict the quality of ready-mixed concrete being mixed using the mixer 114 (ready-mixed concrete obtained before mixing is completed). The quality prediction device 10 may also predict the quality of ready-mixed concrete at the completion of mixing using the mixer 114. The quality prediction device 10 may also predict the quality of ready-mixed concrete being transported to a construction site where the ready-mixed concrete will be used, or the quality of ready-mixed concrete upon arrival at the construction site (at the time of unloading). For example, the transport vehicle 200 is provided with a mixer 214 that mixes the ready-mixed concrete (see FIG. 1 ). The mixer 214 in the transport vehicle 200, such as an agitator truck, is also referred to as a drum.

[0138] If the production system 1 includes a transport vehicle 200, at least some of the feature quantities F1 to FN may include feature quantities obtained when the mixer 214 is used to produce (mix) ready-mixed concrete. The one or more load feature quantities FL may be one or more feature quantities related to the power load value of the mixer 214 when mixing the ready-mixed concrete. The feature quantity acquisition unit 24 of the quality prediction device 10 may calculate the one or more load feature quantities FL from time-series data up to a measurement time point mp during the mixing operation by the mixer 214. The prediction unit 30 may predict the quality of the ready-mixed concrete after it is discharged from the mixer 214 while the mixing operation by the mixer 214 is continuing.

[0139] The feature acquisition unit 24 of the quality prediction device 10 may acquire, as at least a portion of the feature quantities F1 to FN (one or more load feature quantities FL), feature quantities related to the power load value of the mixer 214 when mixing the ready-mixed concrete. The feature quantities F1 to FN may include, as data related to the transportation of ready-mixed concrete, one or more types of data selected from the following: (i) the volume of ready-mixed concrete in the mixer 214 of the transport vehicle 200, the thickness, mass, and temperature of the pouring member; (ii) the outside air temperature during transportation; and (iii) transportation time (the time from the end of mixing to the end of transportation (unloading)) and transportation distance. The feature quantities F1 to FN may also include batcher data (such as the type of heating / cooling device, stored water temperature, stored aggregate temperature, stored cement temperature, measuring bottle type, discharge volume, and maximum discharge pressure). The quality prediction device 10 does not necessarily have a function for controlling the manufacturing apparatus 100.

[0140] In the above example, ready-mixed concrete is produced at a location (manufacturing apparatus 100) separate from the construction site. The location where ready-mixed concrete is produced is not limited to this example. Concrete materials (e.g., concrete materials excluding water) may be transported to the construction site by a transport vehicle, and ready-mixed concrete may be produced at the construction site. In this case, a mixer provided on the transport vehicle may add water to the concrete materials transported by the transport vehicle, and the materials may be mixed to obtain ready-mixed concrete. When ready-mixed concrete is produced at the construction site, the quality prediction device 10 may predict the quality of the ready-mixed concrete, such as its fresh properties, at the construction site. The feature acquisition unit 24 of the quality prediction device 10 may acquire, as at least a part of one or more load feature values ​​FL, a feature value related to the power load value of the mixer provided on the transport vehicle when ready-mixed concrete is produced at the construction site.

[0141] In one example of the various examples described above, at least some of the matters described in other examples may be combined.

[0142] [Verification of prediction results using a prediction model] Next, we will explain the results of verifying the prediction results of freshness properties using a prediction model M constructed using a decision tree algorithm and inputting feature quantities F1 to FN using a dataset for which the correct values ​​are known. In this verification, the same training data TD was used to compare the prediction results with those of a comparison model constructed using a deep neural network (DNN) different from the decision tree algorithm.

[0143] A training dataset of 120,552 items was prepared as training data TD, and a test dataset of 13,383 items, in which the correct values ​​were known, was prepared for evaluating the prediction results. In each of the training dataset and the test dataset, the features F1 to FN were linked to the correct values ​​of the fresh properties. Data obtained from a ready-mixed concrete manufacturing system configured similarly to manufacturing system 1 was used as the correct values ​​(measured values) of the features F1 to FN and the fresh properties. In addition, the fresh properties of ready-mixed concrete before shipment were used as the target for prediction by the prediction model.

[0144] The data items shown in Table 1 below were used as feature quantities F1 to FN. In Table 1, "manufacturing month (sin, cos)" means data that expresses the manufacturing month using the trigonometric functions sin and cos, and has the same meaning for manufacturing date, etc. "cement type (a1, a2, a3, a4, a5)" means data that indicates which cement of the cement types a1 to a5 is included, and has the same meaning for admixture type, etc.

[0145] (Example 1: LightGBM) A prediction model M was constructed using LightGBM, a type of decision tree algorithm. Specifically, the prediction model M was constructed using "LightGBM (version 4.3.0)" provided by Microsoft. A model for predicting slump, a model for predicting slump flow, and a model for predicting air volume were constructed separately. When constructing the prediction model M according to Example 1, the main hyperparameters were set as shown in Table 2 below.

[0146] (Example 2: XGBoost) A prediction model M was constructed using XGBoost, a type of decision tree algorithm. Specifically, the prediction model M was constructed using the well-known library "XGBoost (version 1.5.2)." A model for predicting slump, a model for predicting slump flow, and a model for predicting air volume were constructed separately. When constructing the prediction model M according to Example 2, the main hyperparameters were set as shown in Table 3 below.

[0147] (Comparative Example: DNN) A comparative prediction model was constructed using a deep neural network. When constructing the comparative prediction model, the main conditions (network configuration) were set as follows: - A batch normalization layer for normalizing input data to the model was not used. - Dropout, which causes some connections in the intermediate layer of the neural network to be missing, was not used. - The intermediate layer of the neural network was a fully connected layer with eight layers. - Adam was used as the weight update formula, and HuberLoss was used as the loss function. - Machine learning was performed so that three types of data - slump, slump flow, and air volume - were output as fresh properties from one prediction model.

[0148] (Verification Results) For each of Example 1, Example 2, and Comparative Example, a test dataset was used to compare the predicted values ​​obtained by the prediction model with the correct values ​​contained in the dataset. Specifically, the correct answer was defined as the case where the predicted value obtained by the model was within a range obtained by adding a predetermined tolerance to the correct answer value in the test dataset, and the accuracy rate, which represents the ratio of the number of correct datasets to the total number of test data, was calculated as an evaluation index.

[0149] FIG. 12( a) shows the evaluation results of the accuracy rate for slump. In FIG. 12( a), when the tolerance is "±0.5 cm," the data set whose predicted value by the model falls within a range of ±0.5 cm of the correct value is considered to be the correct answer, and the percentage of the data set that is considered to be the correct answer is shown as the accuracy rate (%). The accuracy rates (%) are also calculated similarly when the tolerance is "±1.0 cm," "±1.5 cm," "±2.0 cm," and "±2.5 cm." The results shown in FIG. 12( a) show that the accuracy rates when predicting slump using the prediction model M according to Examples 1 and 2 are significantly higher than those of the comparative example.

[0150] FIG. 12(b) shows the evaluation results of the accuracy rate for slump flow. In FIG. 12(b), when the tolerance is "±2.5 cm," data sets whose predicted values ​​by the model are within a range of ±2.5 cm of the correct value are considered to be correct, and the percentage of data sets that are considered to be correct is shown as the accuracy rate (%). Accuracy rates (%) were also calculated in the same way when the tolerance is "±3.0 cm," "±4.0 cm," "±5.0 cm," "±6.0 cm," and "±7.5 cm." The results shown in FIG. 12(b) show that the accuracy rates when predicting slump flow using the prediction model M according to Examples 1 and 2 are significantly higher than those of the comparative example.

[0151] FIG. 13( a) shows the evaluation results of the accuracy rate for the air volume. In FIG. 13( a), when the tolerance is "±0.3%," the data set whose predicted value by the model is within ±0.3% of the correct value is considered to be the correct answer, and the percentage of the data set that is considered to be the correct answer is shown as the accuracy rate (%). The accuracy rates (%) are also calculated in the same way when the tolerance is "±0.5%, "±0.7%, "±1.0%, "±1.2%," and "±1.5%". From the results shown in FIG. 13( a), it can be seen that the accuracy rates when predicting the air volume using the prediction model M according to Example 1 and Example 2 are significantly higher than in the comparative example.

[0152] As an evaluation index separate from the accuracy rate, we calculated the "sum of the mean and standard deviation" regarding the error between the predicted value and the correct value. Using the sum of the mean and standard deviation as an evaluation index means that the error is evaluated taking into account the variability. In other words, even if the error itself (the mean of the errors) is small, if the standard deviation is large, it means that the variability of the errors is large, which is not desirable. Also, even if the standard deviation of the errors is small, if the mean value is large, it means that the error itself is large, which is not desirable. In calculating the sum of the mean and standard deviation, we calculated the error between the model's predicted value and the correct value for each test dataset, and then found the mean and standard deviation from these errors.

[0153] 13B shows the results of calculating the average error and the sum of the standard deviation for each of the fresh properties of slump, slump flow, and air content. The results shown in FIG. 13B show that the average error and the sum of the standard deviation are significantly smaller when predictions are made using the prediction model M according to Examples 1 and 2 than in the comparative example, for all of the fresh properties of slump, slump flow, and air content.

[0154] From a different perspective than prediction accuracy, we evaluated the "learning time," which is the time from when the computer starts training after the preparation of training data TD and the setting of various hyperparameters are complete until the construction of a predictive model is completed. The learning time can be described as the time required for the computer to construct a predictive model. The evaluation results of the learning time are shown in Table 4 below.

[0155] In Table 4, Examples 1 and 2 represent the learning time required to build a prediction model M that predicts only slump, while Comparative Example represents the learning time required to build a prediction model that simultaneously outputs slump, slump flow, and air volume. The results shown in Table 4 demonstrate that the learning time can be significantly reduced in Examples 1 and 2 compared to the Comparative Example. It is assumed that the learning time in the Comparative Example is not significantly different from the learning time required to build a comparative model that predicts only slump using the same method as the Comparative Example. Even if the learning time in Examples 1 and 2 is tripled, it is still significantly reduced compared to the Comparative Example. It is also evident that the learning time can be further reduced in Example 1 compared to Example 2. For example, shortening the learning time makes it possible to build or update the prediction model M while still producing ready-mixed concrete (ready-mixed concrete production can continue without being restricted by the construction or update of the prediction model M).

[0156] (Consideration of Factors for Improved Prediction Accuracy Compared to the Comparative Example) (Prediction Model Based on Decision Tree) The prediction model M constructed in Examples 1 and 2 above is both constructed using a decision tree algorithm. In a decision tree, branching is performed by focusing on the part of each node where the feature value differs most. Therefore, as long as there is a relationship between the feature value and the data to be predicted, a certain degree of accuracy can be ensured even with a limited amount of training data. Furthermore, because the model structure is simple, overlearning is unlikely to occur even with a small amount of data. On the other hand, the DNN according to the comparative example performs complex learning using all feature value data in the training data and excels at capturing detailed features and patterns of the data. A DNN with such characteristics requires a huge amount of data to ensure accuracy. Furthermore, because the model structure is complex, it is prone to excessive fitting to the training data when the amount of data is small. In machine learning, it is practically difficult to prepare training data with a huge amount of data. Therefore, it is believed that a decision tree can construct a more accurate prediction model when predictions are made using limited data.

[0157] [Ensemble Learning Using Decision Trees] The prediction models M constructed in Examples 1 and 2 above are both constructed using a decision tree algorithm including ensemble learning. Ensemble learning using decision trees includes a technique called "bagging," in which multiple decision trees with different characteristics are arranged in parallel and trained independently, and a technique called "boosting," in which multiple decision trees with different characteristics are arranged in series and trained sequentially. These techniques use multiple decision trees to construct a prediction model, thereby reducing the variability in prediction results and the error between predicted values ​​and correct values. Therefore, it is presumed that ensemble learning using multiple decision trees improved prediction accuracy compared to learning using a single DNN. Note that DNN ensemble learning can have a disadvantage in that it requires a great deal of effort to construct a model because the model structure becomes more complex and it can be difficult to appropriately combine models and adjust parameters.

[0158] [Gradient Boosting Decision Tree] The prediction model M constructed in Example 1 above is constructed using LightGBM, a type of machine learning that includes gradient boosting. (1) LightGBM excels at capturing the synergistic effects when combining numerical data between parameters (features). On the other hand, DNN is not suited to capturing the interrelationships between data. Considering that freshness, an example of quality, is determined by a combination of concrete mix, date, and load value, it is presumed that building a model using LightGBM, which can capture interactions, improved prediction accuracy. (2) LightGBM can select and learn important features of data. Specifically, for features with large prediction errors, all data is used as important features, while for features with small prediction errors, data is randomly extracted and retrained as learned features, creating a prediction model. On the other hand, DNN performs complex learning using all data related to each feature, and excels at capturing detailed features and patterns of data. A DNN with these characteristics is effective when used with image, video, or audio data, but requires a huge amount of data to improve accuracy. For predictions using limited numerical data, it is believed that a highly accurate predictive model can be created by selecting and learning important features of the data.

[0159] [Summary of the Present Disclosure] The quality prediction method for ready-mixed concrete described above is a quality prediction method for predicting the quality of ready-mixed concrete. This quality prediction method includes a construction step of constructing a prediction model (M) by machine learning based on training data (TD) including multiple data sets; an acquisition step of acquiring multiple feature quantities (F1-FN) obtained when producing ready-mixed concrete using a mixer (114, 214); and a prediction step of using the prediction model (M) constructed in the construction step to acquire a predicted value of quality based on the multiple feature quantities (F1-FN) acquired in the acquisition step. In each of the multiple data sets, the multiple feature quantities (F1-FN) are associated with a correct value of quality. The multiple feature quantities (F1-FN) include one or more load feature quantities (FL) related to the power load value of the mixer (114, 214) when mixing concrete materials or stirring ready-mixed concrete. In the construction step, a prediction model (M) is constructed so that one or more feature quantities selected from a plurality of feature quantities (F1 to FN) are associated with predicted values ​​of quality using a decision tree.

[0160] As explained in the above verification results, it was confirmed that the prediction accuracy of the prediction model can be significantly improved by using a prediction model (M) constructed so that one or more feature quantities selected from a plurality of feature quantities (F1 to FN) correspond to the predicted quality value using a decision tree. In other words, the above quality prediction method in which the quality of ready-mixed concrete is predicted using the prediction model (M) is useful for improving prediction accuracy. Furthermore, since a model is constructed using a decision tree, the time required to construct the prediction model can be shortened.

[0161] In the above-described method for predicting the quality of ready-mixed concrete, the construction step may involve constructing a prediction model (M) by machine learning including ensemble learning. In this case, a plurality of models are combined to construct a prediction model (M) that obtains a single final predicted value, which is more useful for improving prediction accuracy.

[0162] In the above-described method for predicting the quality of ready-mixed concrete, the construction step may construct a prediction model (M) by machine learning using boosting. In this case, multiple models generated in sequence after taking into account errors are combined to construct a prediction model (M) that obtains a single final predicted value, thereby improving the prediction accuracy of a model using a decision tree.

[0163] In the above-described method for predicting the quality of ready-mixed concrete, the construction step may construct a prediction model (M) by machine learning using gradient boosting. In this case, multiple models generated in sequence to reduce errors are combined to construct a prediction model (M) that provides a single final predicted value, thereby further improving the prediction accuracy of a model using a decision tree.

[0164] In the above-described method for predicting the quality of ready-mixed concrete, the one or more load features (FL) may include at least one of a value at a predetermined time point in time-series data of the power load value of the mixer (114, 214) when mixing concrete materials or mixing ready-mixed concrete, and a statistic obtained from the time-series data. The statistic may be one or more values ​​selected from the group consisting of the difference between the maximum and minimum values ​​(variation range), the difference between the maximum and final values ​​(decline range), the sum, the mean, the standard deviation, the coefficient of variation, the median, the first quartile, the third quartile, kurtosis, and skewness. The power load value when mixing or mixing using the mixer fluctuates over time. As in the above method, by using at least one of the power load value at a specific time point and the statistic obtained from the time-series data as a feature, information that better reflects the characteristics of the time-varying power load value can be added to the input of the prediction model (M).

[0165] In the above-described method for predicting the quality of ready-mixed concrete, the multiple feature quantities (F1 to FN) may further include one or more timing feature quantities related to the timing when the mixer (114) mixes the concrete materials. The one or more timing feature quantities may include at least one of the following: the date and time when the mixer (114) mixes the concrete materials; the cumulative number of batches mixed by the mixer (114) in a day; the time from an arbitrarily set reference point until the mixer (114) mixed the concrete materials; and the cumulative number of batches mixed by the mixer (114) from the arbitrarily set reference point. The inventors' verification confirmed that even if the production conditions, including the operating conditions of the mixer (114), are the same, the power load value itself can vary depending on the timing of the production of ready-mixed concrete. In the above-described method, by adding the timing feature quantities to the prediction model (M), the prediction model (M) can be constructed taking into account the fluctuations in the power load value depending on the timing of the production of ready-mixed concrete. As a result, it is possible to reduce variations in the prediction results by the prediction model (M), which may occur due to differences in the manufacturing time.

[0166] The quality prediction program described above is a program that causes a computer to execute the quality prediction method. Since the quality prediction program executes the quality prediction method, it is useful for improving prediction accuracy, just like the quality prediction method.

[0167] The method for producing ready-mixed concrete described above includes a production process for producing ready-mixed concrete, and a quality prediction process for predicting quality using the quality prediction method after or during the production process. In this method for producing ready-mixed concrete, the quality is predicted using the quality prediction method, and therefore, like the quality prediction method, it is useful for improving prediction accuracy.

[0168] The ready-mixed concrete quality prediction device (10) described above is a device for predicting the quality of ready-mixed concrete. This quality prediction device (10) includes a model construction unit (26) that constructs a prediction model (M) through machine learning based on training data (TD) including multiple data sets; an acquisition unit (24) that acquires multiple feature quantities (F1-FN) obtained when producing ready-mixed concrete using a mixer (114, 214); and a prediction unit (30) that uses the prediction model (M) constructed by the model construction unit (26) to acquire a predicted value of quality corresponding to the multiple feature quantities (F1-FN) acquired by the acquisition unit (24). For each of the multiple data sets, the multiple feature quantities (F1-FN) are associated with a correct value of quality. The multiple feature quantities (F1-FN) include one or more load feature quantities (FL) related to the power load value of the mixer (114, 214) when mixing concrete materials or stirring ready-mixed concrete. The model construction unit (26) constructs a prediction model (M) so that one or more feature quantities selected from the plurality of feature quantities (F1 to FN) correspond to the predicted quality values ​​by a decision tree. This quality prediction device is useful for improving prediction accuracy, similar to the above-mentioned quality prediction method.

[0169] The ready-mixed concrete manufacturing system (1) described above includes a manufacturing apparatus (100) that manufactures ready-mixed concrete and the quality prediction device (10). The quality prediction device (10) predicts the quality of ready-mixed concrete manufactured by the manufacturing apparatus (100). Since this manufacturing system includes the quality prediction device, it is useful for improving prediction accuracy, similar to the quality prediction device.

[0170] 1... manufacturing system, 10... quality prediction device, 24... feature acquisition unit, F1 to FN... feature, FL... load feature, 26... model construction unit, TD... training data, 30... prediction unit, M... prediction model, 100... manufacturing device, 114... mixer, 114a... stirring member, 114b... mixer drive unit, 214... mixer.

Claims

1. A quality prediction method for predicting the quality of ready-mixed concrete, comprising: a construction step of constructing a prediction model by machine learning based on training data including a plurality of data sets; an acquisition step of acquiring a plurality of feature quantities obtained when ready-mixed concrete is produced using a mixer; and a prediction step of utilizing the prediction model constructed in the construction step to acquire a predicted value of the quality corresponding to the plurality of feature quantities acquired in the acquisition step, wherein, for each of the plurality of data sets, the plurality of feature quantities are associated with a correct value of the quality, and the plurality of feature quantities include one or more load feature quantities related to a power load value of the mixer when mixing concrete materials or stirring ready-mixed concrete, and wherein, in the construction step, the prediction model is constructed so that one or more feature quantities selected from the plurality of feature quantities are associated with the predicted value of the quality using a decision tree.

2. The method for predicting the quality of ready-mixed concrete according to claim 1, wherein in the construction step, the prediction model is constructed by machine learning including ensemble learning.

3. The method for predicting the quality of ready-mixed concrete according to claim 2, wherein in the construction step, the prediction model is constructed by machine learning using boosting.

4. The quality prediction method of ready-mixed concrete according to claim 1, wherein the quality of the ready-mixed concrete is predicted to be the fresh properties of the ready-mixed concrete, and in each of the plurality of data sets included in the training data, the plurality of feature quantities are associated with correct values ​​of the fresh properties, and in the construction process, the prediction model is constructed by machine learning including ensemble learning based on the training data, and in the prediction model constructed in the construction process, one or more feature quantities selected from the plurality of feature quantities are associated with predicted values ​​of the fresh properties using a decision tree, and in the construction process, the prediction model is constructed by machine learning using boosting as machine learning including ensemble learning.

5. The method for predicting the quality of ready-mixed concrete according to claim 3 or 4, wherein in the construction step, the prediction model is constructed by machine learning using gradient boosting.

6. A method for predicting the quality of ready-mixed concrete according to any one of claims 1 to 5, wherein the one or more load feature amounts include at least one of a value at a predetermined time point in time series data of the power load value of the mixer when mixing concrete materials or mixing ready-mixed concrete, and a statistical amount obtained from the time series data, and the statistical amount is one or more values ​​selected from the group consisting of the difference between the maximum value and the minimum value, the difference between the maximum value and the final value, the sum, the mean, the standard deviation, the coefficient of variation, the median, the first quartile, the third quartile, kurtosis, and skewness.

7. A method for predicting the quality of ready-mixed concrete according to any one of claims 1 to 5, wherein the one or more load feature amounts include a statistical amount obtained from time-series data of the power load value of the mixer when mixing concrete materials or mixing ready-mixed concrete, and the statistical amount is one or more values ​​selected from the group consisting of the difference between the maximum value and the minimum value, the difference between the maximum value and the final value, the sum, the mean, the standard deviation, the coefficient of variation, the median, the first quartile, the third quartile, kurtosis, and skewness.

8. A method for predicting the quality of ready-mixed concrete according to any one of claims 1 to 7, wherein the plurality of feature amounts further include one or more time feature amounts related to a time when the mixer mixes the concrete materials, and the one or more time feature amounts include at least one of: a date and time when the mixer mixes the concrete materials; a cumulative number of batches mixed by the mixer in one day; a time from an arbitrarily set reference time point to the time when the mixer mixes; and a cumulative number of batches mixed by the mixer from an arbitrarily set reference time point.

9. A quality prediction program that causes a computer to execute the quality prediction method according to any one of claims 1 to 8.

10. A method for producing ready-mixed concrete, comprising: a production process for producing ready-mixed concrete; and a quality prediction process for predicting the quality by the quality prediction method according to any one of claims 1 to 8 after or during the production process.

11. A quality prediction device for predicting the quality of ready-mixed concrete, comprising: a model construction unit that constructs a prediction model by machine learning based on training data including a plurality of data sets; an acquisition unit that acquires a plurality of feature quantities obtained when producing ready-mixed concrete using a mixer; and a prediction unit that uses the prediction model constructed by the model construction unit to acquire a predicted value of the quality according to the plurality of feature quantities acquired by the acquisition unit, wherein, for each of the plurality of data sets, the plurality of feature quantities are associated with a correct value of the quality, and the plurality of feature quantities include one or more load feature quantities related to the power load value of the mixer when mixing concrete materials or stirring ready-mixed concrete, and the model construction unit constructs the prediction model so that one or more feature quantities selected from the plurality of feature quantities are associated with the predicted value of the quality using a decision tree.

12. A ready-mixed concrete manufacturing system comprising: a manufacturing device for manufacturing ready-mixed concrete; and the quality prediction device according to claim 11, wherein the quality prediction device predicts the quality of the ready-mixed concrete manufactured by the manufacturing device.

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