Concrete floor base evaluation device and concrete floor base evaluation method

Abstract

To evaluate a state of a concrete floor foundation according to a surface layer-contained moisture content or surface layer-discharged moisture content of the concrete floor foundation.SOLUTION: A server device 20 stores a learned model that has machine-learned a correlation between an explanatory variable and a target variable on the basis of teacher data that has image data of a concrete floor foundation as the explanatory variable and has a surface layer-contained moisture content or surface layer-discharged moisture content measured for the concrete floor foundation as the target variable. A worker images the concrete floor foundation being the evaluation target of the surface layer-contained moisture content or surface layer-discharged moisture content by an imaging device 1005 of an AR display device 10 in such a state that the AR display device 10 is attached to the head. The image data imaged by the imaging device 1005 is input to the learned model of the server device 20 as the explanatory variable, and consequently information according to the surface layer-contained moisture content or surface layer-discharged moisture content output as the target variable is displayed on the AR display device 10.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technique for evaluating the condition of a concrete floor substrate. [Background technology]

[0002] Surface moisture, one of the quality indicators of concrete subfloor foundations, can be broadly divided into moisture contained in the surface layer of the concrete subfloor and moisture released from the surface layer. The moisture content of the surface layer, which is present within a range of approximately 40 mm from the surface, can cause mid- to long-term peeling, swelling, and internal condensation. The moisture released from the surface layer, which is absorbed by dryness test paper attached to the surface layer, is present within a range of approximately 5 mm from the surface and can cause poor adhesion during the application of finishing materials. Patent Document 1, for example, discloses a method for evaluating the quality of aggregate itself before mixing concrete, using a learning model with aggregate images as explanatory variables and the aggregate surface moisture content as the target variable. However, while high-frequency capacitance moisture meters are effective for measuring the moisture content of the surface layer of a poured concrete subfloor, and dryness test paper is effective for determining the moisture content released from the surface layer, determining each moisture content requires time and effort, such as correcting the moisture content determined on-site through expert confirmation after measurement. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2021-135199 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention has been made in consideration of the above-mentioned background, and aims to use artificial intelligence to instantly evaluate the condition of a concrete floor base based on the amount of moisture contained in the surface layer of the concrete floor base or the amount of moisture released from the surface layer. [Means for solving the problem]

[0005] In order to solve the above problems, the concrete floor base evaluation device of the present invention includes an input unit that inputs image data of the concrete floor base photographed by an imaging device as an explanatory variable to a trained model that has learned the correlation between image data of the concrete floor base photographed by an imaging device and the amount of moisture contained in the surface layer of the concrete floor base, and an acquisition unit that acquires the amount of moisture contained in the surface layer of the concrete floor base as a target variable; An identification unit that identifies the use of the concrete floor base; an output unit that outputs information corresponding to the acquired moisture content of the surface layer; an output unit that classifies the moisture content of the surface layer acquired as the objective variable according to the intended use and outputs information according to the result of the classification; It is equipped with:

[0006] The concrete floor base evaluation device of the present invention includes an input unit that inputs image data of the concrete floor base photographed by the imaging device as an explanatory variable to a trained model that has learned the correlation between image data of the concrete floor base photographed by the imaging device and the amount of surface moisture released from the surface of the concrete floor base, and an acquisition unit that acquires the amount of surface moisture released in the concrete floor base as a target variable. An identification unit that identifies the use of the concrete floor base; an output unit that outputs information corresponding to the acquired amount of surface water released; an output unit that classifies the amount of surface layer released water acquired as the objective variable according to the intended use and outputs information according to the result of the classification; It is equipped with:

[0008] The system may further include an evaluation unit that evaluates the construction state of the concrete floor base based on information corresponding to the classification results output by the output unit.

[0009] The output unit may display information according to the classification result and / or the evaluation result superimposed on the shape of the concrete floor base.

[0010] The applications may be divided based on the magnitude of the dynamic load acting on the concrete floor substrate.

[0011] Further, the concrete floor base evaluation method of the present invention includes: The computerA step of inputting image data of the concrete floor substructure photographed by the imaging device as an explanatory variable into a trained model that has learned the correlation between image data of the concrete floor substructure photographed by the imaging device and the amount of moisture contained in the surface layer of the concrete floor substructure; The computer A step of acquiring the moisture content of the surface layer of the concrete floor base as a response variable; A step of a computer identifying a use of the concrete floor subfloor; a step of outputting information corresponding to the obtained moisture content of the surface layer; a step of classifying the moisture content of the surface layer obtained as the objective variable according to the intended use, and outputting information according to the result of the classification; It is equipped with:

[0012] Further, the concrete floor base evaluation method of the present invention includes: The computer A step of inputting image data of the concrete floor substructure photographed by the imaging device as an explanatory variable into a trained model that has learned the correlation between image data of the concrete floor substructure photographed by the imaging device and the amount of surface moisture released from the surface of the concrete floor substructure; The computer a step of acquiring the amount of moisture released from the surface layer of the concrete floor base as a response variable; A step of a computer identifying a use of the concrete floor subfloor; a step of outputting information corresponding to the acquired amount of surface layer released water; a step of classifying the amount of surface water released obtained as the objective variable according to the intended use, and outputting information according to the result of the classification; Equipped with. [Effects of the Invention]

[0015] According to the present invention, it is possible to evaluate the condition of a concrete floor base in accordance with the amount of moisture contained in the surface layer or the amount of moisture released from the surface layer without taking much time and effort.

[0016] Furthermore, according to the present invention, it is possible to generate a trained model for making an evaluation based on the amount of moisture contained in the surface layer of a concrete floor base or the amount of moisture released from the surface layer.

[0017] Furthermore, according to the present invention, it is possible to operate a device for carrying out an evaluation according to the amount of moisture contained in the surface layer of a concrete floor base and / or the amount of moisture released from the surface layer. [Brief explanation of the drawings]

[0018] [Figure 1]1 is a block diagram showing an example of the overall configuration of a concrete floor base evaluation system 1 according to one embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing an example of the hardware configuration of a server device 20 according to the embodiment. [Figure 3] FIG. 2 is a block diagram showing an example of the hardware configuration of the AR display device 10 according to the embodiment. [Figure 4] FIG. 2 is a block diagram showing an example of the functional configuration of the server device 20. [Figure 5] FIG. 10 is a diagram illustrating a color sample of a dryness test paper. [Figure 6] A table illustrating the relationship between the color of a color sample and the amount of moisture contained within a 5mm depth range from the surface of a concrete floor base. [Figure 7] FIG. 2 is a block diagram showing an example of the functional configuration of the AR display device 10. [Figure 8] 10 is a flowchart showing an example of a method for generating a trained model in the embodiment. [Figure 9] 10 is a flowchart showing an example of a method for evaluating moisture content using a trained model in the embodiment. [Figure 10] 4 is a table illustrating examples of grades of surface layer moisture content for different uses in the embodiment. [Figure 11] 4 is a table illustrating examples of grades of surface moisture release amounts for different uses in the embodiment. [Figure 12] 2 is a diagram illustrating an XYZ coordinate system of the AR display device 10. FIG. [Figure 13] 10 is a diagram illustrating a case where the AR display device 10 displays information about the moisture content of the surface layer. [Figure 14] 10 is a diagram illustrating a case where the AR display device 10 displays information relating to the amount of water released from the surface layer. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of the present invention will be described. [composition] 1 is a block diagram showing an example of the overall configuration of a concrete floor base evaluation system 1 according to an embodiment of the present invention. The concrete floor base evaluation system 1 includes an AR (Augmented Reality) display device 10, a server device 20, a communication network 30 that communicatively connects the AR display device 10 and the server device 20, a data input device 40 that inputs training data to the server device 20 for machine learning by the server device 20, and an imaging device 50. The server device 20, the data input device 40, and the imaging device 50 are communicatively connected.

[0020] 2 is a diagram showing the hardware configuration of the server device 20. The server device 20 is physically configured as a computer device including a processor 2001, a memory 2002, a storage 2003, a communication device 2004, an input / output device 2005, and a bus connecting these devices. Each of these devices operates using power supplied from a power source (not shown).

[0021] Each function in the server device 20 is realized by loading predetermined software (programs) onto hardware such as the processor 2001 and memory 2002, causing the processor 2001 to perform calculations, control communication via the communication device 2004, acquire data transmitted from other devices, and control at least one of reading and writing data in the memory 2002 and storage 2003.

[0022] The processor 2001 controls the entire computer by running, for example, an operating system, and may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc.

[0023] The processor 2001 reads programs (program codes), software modules, data, etc. from at least one of the storage 2003 and the communication device 2004 into the memory 2002, and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described below. The functional blocks of the server device 20 may be realized by a control program stored in the memory 2002 and running on the processor 2001. The various processes may be executed by one processor 2001, or may be executed simultaneously or sequentially by two or more processors 2001.

[0024] The memory 2002 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 2002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 2002 can store executable programs (program codes), software modules, etc. for implementing the method according to this embodiment.

[0025] Storage 2003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a solid-state drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 2003 may also be called an auxiliary storage device.

[0026] The communication device 2004 is hardware (transmission / reception device) for performing communication between computers, and is also called, for example, a network device, a network controller, a network card, or a communication module.

[0027] The input / output device 2005 is an input / output interface that receives input and output of data from external devices, and receives data input from the data input device 40 and the imaging device 50 .

[0028] 3 is a diagram showing the hardware configuration of the AR display device 10. The AR display device 10 is physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an image capturing device 1005, a display device 1006, an operation device 1007, and a bus connecting these devices. The communication device 1004 performs wireless communication. Each function realized by the AR display device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001, the memory 1002, and the processor 1001 performing calculations to control communication by the communication device 1004, image capturing by the image capturing device 1005, and display by the display device 1006, and by controlling at least one of reading and writing of data from and to the memory 1002 and the storage 1003.

[0029] The AR display device 10 displays a virtual image called an AR object superimposed on real space. AR display methods include a method of displaying an AR object superimposed on a captured image of real space on a display surface that displays the captured image, and a method of displaying an AR object on a light-transmitting display surface placed in front of the user's eyes at a position superimposed on real space visible to the user through the display surface. The AR display device 10 of this embodiment may display AR using either method.

[0030] FIG. 4 is a diagram illustrating an example of the functional configuration of the server device 20. The acquisition unit 21 acquires various data from devices external to the server device 20 (the AR display device 10, the data input device 40, and the imaging device 50). The data acquired by the acquisition unit 21 from the imaging device 50 includes image data representing images of multiple concrete floor underlayments captured by the imaging device 50. The data acquired by the acquisition unit 21 from the data input device 40 includes the moisture content and released moisture content of the surface layer measured for each of the image data of the multiple concrete floor underlayments. The data acquired from the imaging device 50 and the data input device 40 is used as training data when the server device 20 performs machine learning. The data acquired by the acquisition unit 21 from the AR display device 10 includes image data of the concrete floor underlayment to be evaluated captured by the imaging device 1005. The data acquired from the AR display device 10 is input to a trained model generated by machine learning in the server device 20.

[0031] The moisture content of the surface layer is, for example, the amount of moisture contained within a range of approximately 40 mm from the surface of the concrete floor base. This moisture content of the surface layer can be measured using a high-frequency capacitance moisture meter (for example, the D mode of the HI-500 or HI-520-2 manufactured by Kett Electric Laboratory). Moisture content of the surface layer can cause peeling, swelling, internal condensation, and other problems over the medium to long term.

[0032] The surface moisture content is defined as the amount of moisture contained within a 5 mm depth range from the surface of the concrete floor base. Surface moisture content can be measured using dryness test paper. Specifically, a dryness test paper (manufactured by Toyo Roshi) is taped to the concrete floor base. After 10 minutes, the color of the dryness test paper is compared with the 10-level color sample shown in Figure 5. The color sample with the closest color is selected to estimate the surface moisture content grade. Figure 6 shows an example of the relationship between the color of the sample and the amount of moisture contained within a 5 mm depth range from the surface of the concrete floor base. The mass moisture content in Figure 6 is the amount of moisture contained within a 5 mm depth range from the surface of the concrete floor base, as measured by an embedded ceramic sensor. Figure 5 shows that the surface moisture content grade measured by the dryness test paper correlates with the amount of moisture actually measured by the ceramic sensor. Surface moisture content can cause poor adhesion during the application of finishing materials to concrete floor bases.

[0033] 4, the training data generation unit 22 generates training data for performing machine learning to separately derive the surface layer moisture content and the surface layer released moisture content of a concrete floor base using data acquired from the imaging device 50 and the data input device 40. Specifically, the training data generation unit 22 generates training data in which image data of each concrete floor base is used as an explanatory variable for machine learning and the surface layer moisture content measured for each concrete floor base is used as the objective variable for machine learning, and training data in which image data of each concrete floor base is used as an explanatory variable for machine learning and the surface layer released moisture content measured for each concrete floor base is used as the objective variable for machine learning.

[0034] The model generation unit 23 generates a trained model (hereinafter referred to as a surface layer moisture content trained model) by performing machine learning using a neural network based on training data in which image data of each concrete floor base is used as an explanatory variable and the amount of surface layer moisture content measured for each concrete floor base is used as an objective variable. The model generation unit 23 also generates a trained model (hereinafter referred to as a surface layer moisture release trained model) by performing machine learning using a neural network based on training data in which image data of each concrete floor base is used as an explanatory variable and the amount of surface layer moisture release measured for each concrete floor base is used as an objective variable. This machine learning allows for the generation of a trained model that outputs data corresponding to the objective variable when data corresponding to the explanatory variables is input by analyzing the relationship between the explanatory variables and the objective variable included in the training data.

[0035] The model storage unit 24 stores the surface layer moisture content learned model and the surface layer released moisture learned model generated by the model generation unit 23. When image data of a concrete floor undercoat to be evaluated is acquired from the AR display device 10 by the acquisition unit 21 and input as an explanatory variable to the surface layer moisture content learned model stored in the model storage unit 24, the surface layer moisture content of the concrete floor undercoat to be evaluated is output as a response variable. Furthermore, when image data of a concrete floor undercoat to be evaluated is acquired from the AR display device 10 by the acquisition unit 21 and input as an explanatory variable to the surface layer released moisture learned model stored in the model storage unit 24, the surface layer released moisture content of the concrete floor undercoat to be evaluated is output as a response variable.

[0036] The use identification unit 25 identifies the use of the concrete floor underlayment. The uses of the concrete floor underlayment are classified based on the magnitude of the dynamic load acting on the concrete floor underlayment, for example, into floors that are hardly subjected to dynamic loads (such as general residential floors), floors that are subjected to dynamic loads (such as private rooms and corridors in hospitals), and floors that are subjected to large dynamic loads (such as operating rooms in hospitals), but are not necessarily limited to such classifications. The operator can use the operation device 1007 of the AR display device 10 to select the use of the concrete floor underlayment to be evaluated for the amount of surface moisture content or surface moisture release. The use identification unit 25 identifies the use of the concrete floor underlayment by linking the result of the selection operation to image data of the concrete floor underlayment to be evaluated via the acquisition unit 21 from the AR display device 10.

[0037] The evaluation unit 26 classifies the surface layer moisture content and / or surface layer released moisture content, which are the objective variables output from the surface layer moisture content trained model and / or the surface layer released moisture trained model, according to the use identified by the use identification unit 25, and evaluates the construction state of the concrete floor base based on information according to the results of the classification. The output unit 27 outputs information according to the results of the evaluation by the evaluation unit 26 to the AR display device 10.

[0038] Fig. 7 is a diagram showing an example of the functional configuration of the AR display device 10. An operator uses the imaging device 1005 of the AR display device 10 to capture an image of the concrete floor base that is to be evaluated for the amount of moisture contained in the surface layer or the amount of moisture released from the surface layer. In Fig. 7, the input unit 11 transmits image data captured by the imaging device 1005 to the server device 20 via the communication network 30, thereby inputting the image data as an explanatory variable into the surface layer moisture content learned model and / or the surface layer moisture release learned model of the server device 20.

[0039] The acquisition unit 12 acquires, via the communication network 30, information corresponding to the results evaluated by the evaluation unit 26 in accordance with the surface layer moisture content and / or surface layer released moisture content output as target variables from the surface layer moisture content learned model and / or surface layer released moisture learned model of the server device 20.

[0040] The display unit 13 displays the information acquired by the acquisition unit 12.

[0041] [Operation] [Operations in the machine learning stage] 8 is a flowchart showing an example of a method for generating a trained model by the server device 20. First, the acquisition unit 21 of the server device 20 acquires, from the imaging device 50 and the data input device 40, image data of a plurality of concrete floor substrates and data indicating the surface layer moisture content and surface layer moisture release amount measured for these concrete floor substrates, in association with each other (step S11).

[0042] The teacher data generation unit 22 of the server device 20 generates teacher data in which the image data acquired by the acquisition unit 21 is used as an explanatory variable and the surface layer moisture content acquired by the acquisition unit 21 is used as a target variable, and generates teacher data in which the image data acquired by the acquisition unit 21 is used as an explanatory variable and the surface layer moisture content release amount acquired by the acquisition unit 21 is used as a target variable (step S12).

[0043] Next, the model generation unit 23 performs machine learning using the training data to generate a surface layer moisture content trained model and a surface layer release moisture trained model (step S13). The model storage unit 24 stores the generated surface layer moisture content trained model and surface layer release moisture trained model (step S14).

[0044] [Operation in the concrete floor subfloor evaluation stage] 9 is a flowchart showing an example of a method by which the server device 20 evaluates a concrete floor base. The worker operates the operation device 1007 of the AR display device 10 to select the use of the concrete floor base to be evaluated and whether they wish to evaluate the surface layer moisture content or the surface layer moisture release amount (hereinafter referred to as the moisture content type). The use specification unit 25 of the server device 20 acquires the results of these selection operations from the AR display device 10 via the acquisition unit 21, thereby specifying the use of the concrete floor base to be evaluated and the moisture content type desired by the worker (step S21).

[0045] The worker photographs the concrete floor base to be evaluated while wearing the AR display device 10 on his / her head or holding it in his / her hand. In the AR display, an XYZ coordinate system is set in the three-dimensional space of the display area by using IMES (Indoor Messaging System) positioning, a blueprint, on-site total station surveying, etc. As a result, an XYZ coordinate system is set in the floor surface area image-recognized as the concrete floor base, as shown in FIG. 12.

[0046] When the image capturing by the image capturing device 1005 of the AR display device 10 starts, the acquisition unit 21 acquires the image data captured by the image capturing device 1005 via the communication network 30 (step S22). The evaluation unit 26 divides the acquired image data into units of a predetermined size (for example, rectangular units of 50 cm square on the floor surface) and inputs each divided image data as an explanatory variable to the trained model corresponding to the moisture content type identified in step S21 (step S23). As a result, the evaluation unit 26 acquires, for each divided image data unit, the surface layer moisture content or surface layer moisture release amount of the concrete floor base corresponding to the image data as the objective variable (step S24).

[0047] The evaluation unit 26 classifies the surface moisture content or surface moisture release amount obtained for each divided image data unit into grades according to the use identified in step S21 and evaluates the construction condition of the concrete floor base (step S25).

[0048] Fig. 10 is a table illustrating the grades of surface layer moisture content by use, and Fig. 11 is a table illustrating the grades of surface layer moisture release by use. As shown in Fig. 10, with regard to the surface layer moisture content, for general floor use, it is classified into three grades: I (moisture meter reading less than 440), II (moisture meter reading less than 620), and III (moisture meter reading less than 780). For floor use where dynamic loads are applied, it is classified into two grades: I (moisture meter reading less than 440) and II (moisture meter reading less than 620). For floor use where large dynamic loads are applied, it is classified into one grade: I (moisture meter reading less than 440). Adhesives that can be used for concrete floor bases are associated with these grades.

[0049] Furthermore, as shown in Figure 11, with regard to the amount of moisture released from the surface, for general floor applications, there are three grades: I (color evaluation value less than 4.0), II (color evaluation value less than 5.0), and III (color evaluation value less than 6.0); for floor applications where dynamic loads are applied, there are three grades: I (color evaluation value less than 4.0), II (color evaluation value less than 5.0), and III (color evaluation value less than 6.0); and for floor applications where large dynamic loads are applied, there are two grades: I (color evaluation value less than 4.0) and II (color evaluation value less than 5.0). Adhesives that can be used for concrete floor bases are associated with these grades.

[0050] The output unit 27 outputs to the AR display device 10, for example, in accordance with the table shown in Fig. 10, as shown in Fig. 13 for a general floor, the floor surface portions GI, GII, and GIII corresponding to three grades of surface moisture content I (moisture meter reading less than 440), II (moisture meter reading less than 620), and III (moisture meter reading less than 780) are displayed in different colors (step S26). Furthermore, the output unit 27 outputs to the AR display device 10, for example, in accordance with the table shown in Fig. 11, as shown in Fig. 14 for a general floor, the floor surface portions GI, GII, and GIII corresponding to three grades of surface moisture release I (color evaluation value less than 4.0), II (color evaluation value less than 5.0), and III (color evaluation value less than 6.0) are displayed in different colors (step S26).

[0051] The evaluation unit 26 may evaluate the construction state of the concrete floor base (for example, whether the amount of moisture contained in the surface layer or the amount of moisture released from the surface layer of the concrete floor base is suitable for the intended use, whether it is OK to proceed to the next construction, etc.) based on the above grade. The output unit 27 may output, based on these evaluations, a message indicating, for example, whether the amount of moisture contained in the surface layer or the amount of moisture released from the surface layer of the concrete floor base is suitable for the intended use, or a message indicating whether it is OK to proceed to the next construction, together with or separately from the grade display.

[0052] According to the above-described embodiment, a display according to the surface layer moisture content or the surface layer moisture release amount can be displayed simply by the worker observing the concrete floor base using the AR display device 10. Furthermore, according to the display, the worker can determine whether the surface layer moisture content or the surface layer moisture release amount of the concrete floor base is suitable for the application, select an appropriate adhesive to use, and determine whether to proceed to the next process.

[0053] Note that, in the above embodiment, part of the processing executed by the server device 20 may be executed by the AR display device 10. Furthermore, the present invention may be a program or a method executed by the AR display device 10 or the server device 20 according to the above embodiment. [Explanation of symbols]

[0054] 1: Concrete floor substrate evaluation system, 10: AR display device, 11: input unit, 12: acquisition unit, 13: display unit, 20: server device, 21: acquisition unit, 22: training data generation unit, 23: model generation unit, 24: model storage unit, 25: application identification unit, 26: evaluation unit, 27: output unit, 30: communication network, 40: data input device, 50: imaging device, 1001: processor, 1002: memory, 1003: storage, 1004: communication device, 1005: imaging device, 1006: display device, 1007: operation device, 2001: processor, 2002: memory, 2003: storage, 2004: communication device, 2005 input / output device.

Claims

1. an input unit that inputs image data of the concrete floor base photographed by the imaging device as an explanatory variable into a trained model that has learned the correlation between image data of the concrete floor base photographed by the imaging device and the amount of moisture contained in the surface layer of the concrete floor base; an acquisition unit that acquires the moisture content of the surface layer of the concrete floor base as a response variable; An identification unit that identifies the use of the concrete floor base; an output unit that outputs information corresponding to the acquired surface layer moisture content, the output unit classifying the surface layer moisture content acquired as the objective variable according to the intended use, and outputting information corresponding to the result of the classification; A concrete floor substrate evaluation device comprising:

2. an input unit that inputs image data of the concrete floor base photographed by the imaging device as an explanatory variable into a trained model that has learned the correlation between image data of the concrete floor base photographed by the imaging device and the amount of surface moisture released from the surface of the concrete floor base; an acquisition unit that acquires the amount of moisture released from the surface layer of the concrete floor base as a response variable; An identification unit that identifies the use of the concrete floor base; an output unit that outputs information according to the acquired surface layer released moisture amount, the output unit classifying the surface layer released moisture amount acquired as the objective variable according to the use, and outputting information according to the result of the classification; A concrete floor substrate evaluation device comprising:

3. Based on the information according to the classification result output by the output unit, Equipped with an evaluation unit that evaluates the construction status of the floor base The concrete floor substrate evaluation device according to claim 1 or 2.

4. The output unit outputs information according to the result of the classification and / or the result of the evaluation to the concrete. Displayed superimposed on the shape of the floor substrate The concrete floor substrate evaluation device according to claim 3.

5. The applications are classified based on the magnitude of the dynamic load acting on the concrete floor substrate. There are The concrete floor substrate evaluation device according to claim 1 or 2.

6. A step in which a computer inputs image data of a concrete floor substructure photographed by an imaging device as an explanatory variable into a trained model that has learned the correlation between image data of the concrete floor substructure photographed by an imaging device and the amount of moisture contained in the surface layer of the concrete floor substructure; A step in which a computer acquires the moisture content of the surface layer of the concrete floor base as a response variable; A step in which a computer identifies an application of the concrete floor subfloor; a step in which a computer outputs information corresponding to the acquired surface layer moisture content, the step in which the surface layer moisture content acquired as the objective variable is classified according to the intended use, and information corresponding to the result of the classification is output; A concrete floor substrate evaluation method comprising:

7. A step in which a computer inputs image data of a concrete floor substructure photographed by an imaging device as an explanatory variable into a trained model that has learned the correlation between image data of the concrete floor substructure photographed by an imaging device and the amount of surface moisture released from the surface of the concrete floor substructure; A step in which a computer acquires the amount of moisture released from the surface layer of the concrete floor base as a response variable; A step in which a computer identifies an application of the concrete floor subfloor; a step in which a computer outputs information according to the acquired amount of surface-layer released moisture, the step including classifying the amount of surface-layer released moisture acquired as the objective variable according to the intended use, and outputting information according to the result of the classification; A concrete floor substrate evaluation method comprising:

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