Non-destructive diagnostic method, non-destructive diagnostic device, and non-destructive diagnostic program for porous concrete layers

Abstract

To provide a nondestructive diagnostic method, a nondestructive diagnostic device and a nondestructive diagnostic program for a porous concrete layer capable of nondestructively diagnosing soundness of a porous concrete layer of an underdrainage device.SOLUTION: A nondestructive diagnostic method for a porous concrete layer comprises: a step S13 of performing machine learning using first exploration data of a healthy porous concrete layer as teacher data, to generate a learning model for the healthy porous concrete layer; and a step S16 of using as input values second exploration data obtained by exploring the inside of the porous concrete layer after it has been installed and used as a part of an underdrainage device with an electromagnetic radar probe, and diagnosing the soundness of the porous concrete layer installed as the part of the underdrainage device using the learning model.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to a non-destructive diagnostic method, a non-destructive diagnostic device, and a non-destructive diagnostic program for a porous concrete layer. [Background technology]

[0002] Activated carbon adsorption basins and sand filtration basins are installed at water purification plants in designated areas depending on the condition of the raw water. Activated carbon adsorption basins and sand filtration basins are equipped with underdrainage systems. Among these, perforated plate-type underdrainage systems have a porous concrete layer as one of their components. The porous concrete layer is installed on the top layer of the underdrainage system and has the functions of supporting filter media such as activated carbon and sand, and of evenly passing treated water filtered by the filter media and collecting it in a pressure chamber. The porous concrete layer is formed using a different method than the regular concrete used in bridges and tunnels, and is porous and permeable. In other words, the porous concrete layer is formed in a coarser state than regular concrete.

[0003] If a defect such as a crack or fissure occurs in the porous concrete layer, there is a risk that filter media such as activated carbon or sand may pass through the porous concrete layer and become mixed into the treated water in the pressure chamber. Furthermore, if the defect such as a crack is serious, the filter media may fall into the pressure chamber, causing a disruption to the operation of the equipment. In other words, if a defect such as a crack or fissure occurs in the porous concrete layer, there is a risk that the porous concrete layer will not be able to perform its function. Therefore, there is a need for a non-destructive method for diagnosing the soundness of the porous concrete layer.

[0004] Patent Document 1 discloses a nondestructive evaluation method and a nondestructive diagnostic device for the soundness of concrete bodies such as bridge decks, girders, piers, abutments, tunnel linings, retaining walls, revetments, buildings, etc. The concrete body described in Patent Document 1 is formed in a denser state compared to a porous concrete layer. Therefore, in an image based on the exploration data obtained by exploring the concrete body described in Patent Document 1 with an electromagnetic wave radar explorer, the background is relatively clear.

[0005] In contrast, as mentioned above, the porous concrete layer is formed in a coarser state than normal concrete. Therefore, in images based on the exploration data acquired by exploring the porous concrete layer with an electromagnetic radar probe, the background is less clear than in the case of the concrete body described in Patent Document 1. Therefore, while the nondestructive evaluation method and nondestructive diagnostic device described in Patent Document 1 can nondestructively diagnose the soundness of normal concrete bodies used in bridges, tunnels, etc., they may not be able to nondestructively diagnose the soundness of the porous concrete layer of the underdrain system. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2010-107259 Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention has been made in consideration of the above circumstances, and aims to provide a non-destructive diagnostic method, a non-destructive diagnostic device, and a non-destructive diagnostic program for a porous concrete layer that can non-destructively diagnose the soundness of the porous concrete layer of a lower drainage device. [Means for solving the problem]

[0008] A first aspect of the present invention is a method for non-destructively diagnosing the soundness of a porous concrete layer of an underdrainage device installed in at least one of an activated carbon adsorption basin and a sand filtration basin, comprising the steps of: exploring the inside of the healthy porous concrete layer using an electromagnetic radar probe and storing the acquired first exploration data; performing machine learning using the stored first exploration data as training data to generate a learning model for the healthy porous concrete layer; storing the generated learning model; exploring the inside of the porous concrete layer after it has been installed and used as part of the underdrainage device using the electromagnetic radar probe to obtain second exploration data; and diagnosing the soundness of the porous concrete layer installed as part of the underdrainage device using the acquired second exploration data as input values ​​and the stored learning model.

[0009] According to a first aspect of the present invention, first exploration data obtained by exploring the interior of a sound porous concrete layer with an electromagnetic radar exploration device are stored in advance, and machine learning is performed using the stored first exploration data as training data to generate and store a learning model for the sound porous concrete layer. Then, after the porous concrete layer has been installed and used as part of an underdrainage device, second exploration data obtained by exploring the interior of the porous concrete layer with the electromagnetic radar exploration device are used as input values, and the soundness of the porous concrete layer installed as part of an underdrainage device is diagnosed using the stored learning model. As a result, the non-destructive diagnosis method for a porous concrete layer according to the first aspect of the present invention can non-destructively diagnose the soundness of a porous concrete layer that has been formed in a rougher state than normal concrete.

[0010] A second aspect of the present invention is a non-destructive diagnosis method for a porous concrete layer, characterized in that, in the first aspect of the present invention, the first exploration data is exploration data obtained by exploring the inside of a specimen of the healthy porous concrete layer using the electromagnetic wave radar exploration device.

[0011] According to a second aspect of the present invention, the exploration data acquired from a sound porous concrete layer is exploration data acquired by exploring the interior of a specimen of the sound porous concrete layer using an electromagnetic radar probe. Therefore, machine learning can be performed using the exploration data of a simple porous concrete layer as the specimen as training data to generate a learning model, and the soundness of the porous concrete layer to be diagnosed can be diagnosed. As a result, the non-destructive diagnosis method for a porous concrete layer according to the second aspect of the present invention can diagnose the soundness of the porous concrete layer in a simpler manner.

[0012] A third aspect of the present invention is a non-destructive diagnosis method for a porous concrete layer, characterized in that, in the first aspect of the present invention, the first exploration data is exploration data obtained by exploring the inside of the porous concrete layer using the electromagnetic radar exploration device before it is installed and used as part of the under-drainage device.

[0013] According to the third aspect of the present invention, the exploration data acquired from a sound porous concrete layer is exploration data acquired by using an electromagnetic radar probe to explore the interior of the porous concrete layer before it is installed as part of an underdrainage device and used. Therefore, machine learning can be performed using the exploration data from the porous concrete layer actually installed as part of an underdrainage device as training data to generate a learning model and diagnose the soundness of the porous concrete layer. As a result, the non-destructive diagnosis method for a porous concrete layer according to the third aspect of the present invention can diagnose the soundness of the porous concrete layer with higher accuracy.

[0014] A fourth aspect of the present invention is a non-destructive diagnosis method for a porous concrete layer, characterized in that in the first aspect of the present invention, the first exploration data is exploration data obtained by exploring the inside of the porous concrete layer using the electromagnetic radar exploration device before it is installed and used as part of the lower drainage device installed in at least one of an activated carbon adsorption basin different from the activated carbon adsorption basin and a sand filtration basin different from the sand filtration basin.

[0015] According to a fourth aspect of the present invention, the probe data acquired from a healthy porous concrete layer is probe data acquired by using an electromagnetic radar probe to probe the interior of a porous concrete layer installed in at least one of an activated carbon adsorption basin and a sand filtration basin other than the activated carbon adsorption basin or sand filtration basin in which the porous concrete layer to be diagnosed is installed and before use. Therefore, machine learning is performed using the probe data for the porous concrete layer installed in at least one of an activated carbon adsorption basin and a sand filtration basin other than the activated carbon adsorption basin or sand filtration basin in which the porous concrete layer to be diagnosed is installed as training data to generate a learning model, and the soundness of the porous concrete layer to be diagnosed can be diagnosed. As a result, the non-destructive porous concrete layer diagnosis method according to the fourth aspect of the present invention can diagnose the soundness of a porous concrete layer to be diagnosed that is installed as part of an underdrainage system, even in an activated carbon adsorption basin or a sand filtration basin after the porous concrete layer has already been used.

[0016] A fifth aspect of the present invention is a non-destructive diagnosis method for a porous concrete layer, characterized in that, in any one of the first to fourth aspects of the present invention, it further comprises the steps of: storing third inspection data obtained by inspecting the interior of the porous concrete layer having a defect using the electromagnetic radar inspection device; and performing machine learning using the stored first inspection data and third inspection data as training data to generate a learning model regarding the presence or absence of the defect.

[0017] According to a fifth aspect of the present invention, machine learning is performed using the exploration data related to a sound porous concrete layer and the exploration data related to a defective porous concrete layer as training data to generate a learning model related to the presence or absence of defects inside the porous concrete layer. As a result, the non-destructive diagnosis method for a porous concrete layer according to the fifth aspect of the present invention can diagnose the soundness of the porous concrete layer with higher accuracy.

[0018] A sixth aspect of the present invention is a non-destructive diagnosis method for a porous concrete layer, characterized in that, in the fifth aspect of the present invention, the third exploration data is exploration data obtained by exploring the inside of a specimen of the porous concrete layer having the defect using the electromagnetic wave radar exploration device.

[0019] According to a sixth aspect of the present invention, the probe data acquired from a porous concrete layer having a defect is probe data acquired by probing the interior of a specimen of the porous concrete layer having a defect using an electromagnetic radar probe. Therefore, machine learning can be performed using the probe data of a simple porous concrete layer as the specimen as training data to generate a learning model, and the soundness of the porous concrete layer to be diagnosed can be diagnosed. As a result, the non-destructive diagnosis method for a porous concrete layer according to the sixth aspect of the present invention can diagnose the soundness of the porous concrete layer in a simpler manner.

[0020] A seventh aspect of the present invention is a non-destructive diagnosis method for a porous concrete layer, characterized in that in the fifth aspect of the present invention, the third exploration data is exploration data obtained by exploring the inside of the porous concrete layer having the defect using the electromagnetic radar probe after it has been installed and used as part of the lower drainage device installed in at least one of an activated carbon adsorption basin different from the activated carbon adsorption basin and a sand filtration basin different from the sand filtration basin.

[0021] According to a seventh aspect of the present invention, the probe data acquired from the defective porous concrete layer is probe data acquired by using an electromagnetic radar probe to probe the interior of the defective porous concrete layer installed and used in at least one of an activated carbon adsorption basin and a sand filtration basin other than the activated carbon adsorption basin and the sand filtration basin in which the porous concrete layer to be diagnosed is installed, after the porous concrete layer has exceeded its design life. Therefore, machine learning can be performed using the probe data for the defective porous concrete layer installed in at least one of an activated carbon adsorption basin and a sand filtration basin other than the activated carbon adsorption basin and the sand filtration basin in which the porous concrete layer to be diagnosed is installed as training data to generate a learning model, and the soundness of the porous concrete layer to be diagnosed can be diagnosed. Thus, the non-destructive diagnosis method for porous concrete layers according to the seventh aspect of the present invention can diagnose the soundness of the porous concrete layer to be diagnosed, which is installed as part of an underdrainage system, by using the probe data for the porous concrete layer installed in at least one of an activated carbon adsorption basin and a sand filtration basin other than the activated carbon adsorption basin and the sand filtration basin in which the porous concrete layer to be diagnosed is installed.

[0022] An eighth aspect of the present invention is an apparatus for non-destructively diagnosing the soundness of a porous concrete layer of an underdrainage device installed in at least one of an activated carbon adsorption basin and a sand filtration basin, comprising: a healthy overall inspection data memory unit that stores first inspection data obtained by inspection with an electromagnetic radar probe, the first inspection data relating to the inside of the healthy porous concrete layer; a machine learning unit that performs machine learning using the first inspection data stored in the healthy overall inspection data memory unit as training data to generate a learning model relating to the healthy porous concrete layer; a learning model memory unit that stores the learning model generated by the machine learning unit; a post-operation inspection data memory unit that stores second inspection data obtained by inspection with the electromagnetic radar probe, the second inspection data relating to the inside of the porous concrete layer after it has been installed and used as part of the underdrainage device; and a soundness diagnosis unit that diagnoses the soundness of the porous concrete layer installed as part of the underdrainage device using the inspection data input values ​​stored in the post-operation inspection data memory unit and the learning model stored in the learning model memory unit.

[0023] According to an eighth aspect of the present invention, the whole-body inspection data storage unit pre-stores first inspection data acquired by inspecting the interior of a healthy porous concrete layer with an electromagnetic radar probe. The machine learning unit then performs machine learning using the first inspection data stored in the whole-body inspection data storage unit as training data to generate a learning model for a healthy porous concrete layer. The learning model storage unit stores the learning model generated by the machine learning unit. The post-operation inspection data storage unit also stores second inspection data acquired by inspecting the interior of the porous concrete layer with the electromagnetic radar probe after the porous concrete layer has been installed and used as part of an underdrainage device. The soundness assessment unit then uses the second inspection data stored in the post-operation inspection data storage unit as input values ​​and assesses the soundness of the porous concrete layer installed as part of an underdrainage device using the learning model stored in the learning model storage unit. This allows the non-destructive diagnosis device for a porous concrete layer according to the eighth aspect of the present invention to non-destructively assess the soundness of a porous concrete layer formed in a rougher state than normal concrete.

[0024] A ninth aspect of the present invention is a program executed by a computer of an apparatus for non-destructively diagnosing the soundness of a porous concrete layer of an underdrainage device installed in at least one of an activated carbon adsorption basin and a sand filtration basin, the program causing the computer to execute the following steps: storing first exploration data obtained by exploring the inside of the healthy porous concrete layer using an electromagnetic radar probe; performing machine learning using the stored first exploration data as training data to generate a learning model for the healthy porous concrete layer; storing the generated learning model; exploring the inside of the porous concrete layer after it has been installed and used as part of the underdrainage device using the electromagnetic radar probe to obtain second exploration data; and diagnosing the soundness of the porous concrete layer installed as part of the underdrainage device using the acquired second exploration data as input values ​​and the stored learning model.

[0025] According to a ninth aspect of the present invention, a computer is caused to execute an operation of pre-storing first exploration data obtained by exploring the interior of a sound porous concrete layer with an electromagnetic radar exploration device, performing machine learning using the stored first exploration data as training data, and generating and storing a learning model for the sound porous concrete layer.Then, the computer is caused to execute an operation of using second exploration data obtained by exploring the interior of the porous concrete layer with the electromagnetic radar exploration device after it has been installed and used as part of an underdrainage device as input values, and diagnosing the soundness of the porous concrete layer installed as part of an underdrainage device based on the stored learning model.

[0026] As a result, the non-destructive diagnostic program for a porous concrete layer according to the ninth aspect of the present invention can non-destructively diagnose the soundness of a porous concrete layer that is formed in a rougher state than normal concrete. [Effects of the Invention]

[0027] According to the present invention, it is possible to provide a non-destructive diagnostic method, a non-destructive diagnostic device, and a non-destructive diagnostic program for a porous concrete layer that can non-destructively diagnose the soundness of the porous concrete layer of an under-drainage device. [Brief explanation of the drawings]

[0028] [Figure 1] 1 is a schematic perspective view showing an underdrain device having a porous concrete layer to which a non-destructive diagnostic method according to an embodiment of the present invention is applied; [Figure 2] FIG. 2 is an enlarged schematic perspective view of the underdrain device of the present embodiment. [Figure 3] FIG. 2 is a cross-sectional view showing the underdrain device of the present embodiment. [Figure 4] 1 is a block diagram showing a configuration of a main part of a nondestructive diagnostic device according to an embodiment of the present invention. [Figure 5] 1 is a block diagram showing a specific configuration of a main part of a nondestructive diagnostic device according to an embodiment of the present invention. [Figure 6]1 is an example of an image relating to the exploration data obtained from a specimen of a porous concrete layer. [Figure 7] 1 is a flowchart illustrating a nondestructive diagnostic method according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0029] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiments described below are preferred examples of the present invention, and therefore various technically preferable limitations are applied thereto, but the scope of the present invention is not limited to these aspects unless otherwise specified in the following description to the effect that the present invention is particularly limited. Furthermore, in each drawing, similar components are designated by the same reference numerals, and detailed descriptions thereof will be omitted as appropriate.

[0030] FIG. 1 is a schematic perspective view showing an underdrain device having a porous concrete layer to which a non-destructive diagnostic method according to an embodiment of the present invention is applied. FIG. 2 is a schematic perspective view showing an enlarged underdrain device of this embodiment. FIG. 3 is a cross-sectional view showing the underdrain device of this embodiment. For the sake of convenience, FIGS. 1 and 2 show parts of the components cut away.

[0031] The non-destructive diagnostic method according to this embodiment is a method for non-destructively diagnosing the soundness of the porous concrete layer 51 of the underdrain device 5 installed in at least one of an activated carbon adsorption basin and a sand filtration basin. In the following explanation, a case where the underdrain device 5 is installed in an activated carbon adsorption basin will be taken as an example.

[0032] As shown in Fig. 1, an activated carbon adsorption basin 2 is provided in a water purification plant depending on the condition of the water 3 to be treated, for example, when the water 3 to be treated is contaminated with organic matter. An underdrain device 5 is installed in the activated carbon adsorption basin 2. The underdrain device 5 has a porous concrete layer 51, a dispersed gravel layer 52, a slit plate 53, and air dispersion beams 54. As described above, the non-destructive diagnostic method according to this embodiment is applied to the porous concrete layer 51 of the underdrain device 5. First, the underdrain device 5 will be described with reference to Figs. 1 to 3.

[0033] The porous concrete layer 51 is located on the top layer of the underdrainage device 5. The porous concrete layer 51 is formed by cementing gravel with a particle size of approximately 6 mm to 8 mm. Sand is not mixed with the gravel and cement. In other words, the method of forming the porous concrete layer 51 differs from the method of forming ordinary concrete used in buildings such as bridges and tunnels. The porous concrete layer 51 is formed in a coarser state than ordinary concrete. The gravel used in the porous concrete layer 51 is uniform or homogeneous. Therefore, the porous concrete layer 51 is formed with uniformly or evenly distributed voids, which are the parts of the gravel other than the solid parts solidified with cement. The uniformly or evenly distributed voids allow the porous concrete layer 51 to pass treated water evenly. Specifically, the porosity of the porous concrete layer 51 is approximately 30±5%. Here, "porosity" refers to the ratio of voids per unit volume of the porous concrete layer 51, expressed as a percentage.

[0034] In the case of an activated carbon adsorption basin, activated carbon is placed on the porous concrete layer 51 as the filter medium 4. In the case of a sand filter basin, filter sand is placed on the porous concrete layer 51 as the filter medium 4. The activated carbon as the filter medium 4 adsorbs and removes suspended solids in the water to be treated 3 supplied to the activated carbon adsorption basin 2, as well as odors, colors, and organic matter dissolved in the water to be treated 3 supplied to the activated carbon adsorption basin 2. In other words, the filter medium 4 functions to adsorb and filter the water to be treated 3 supplied to the activated carbon adsorption basin 2. The porous concrete layer 51 functions to support the filter medium 4 from below and to evenly pass the treated water filtered by the filter medium 4 and collect it in the pressure chamber 21. The function of the porous concrete layer 51 will be described in detail later. The thickness t1 of the porous concrete layer 51 (see FIG. 3) is approximately 70 mm. However, the thickness t1 is not limited to 70 mm.

[0035] Dispersed gravel layer 52 is provided between porous concrete layer 51 and slit plate 53. For example, the layer of dispersed gravel layer 52 provided above slit plate 53 is formed of gravel with a particle size of approximately 6 mm or more and 8 mm or less. The thickness t2 (see FIG. 3) of the layer of dispersed gravel layer 52 provided above slit plate 53 is approximately 20 mm. However, thickness t2 is not limited to 20 mm. For example, the layer of dispersed gravel layer 52 provided inside slit plate 53 is formed of gravel with a particle size of approximately 8 mm or more and 12 mm or less.

[0036] The slit plate 53 is provided between the dispersed gravel layer 52 and the air dispersion beam 54. The slit plate 53 has a main body and a mesh portion. The main body of the slit plate 53 is formed from reinforced concrete such as stainless steel bars. The mesh portion of the slit plate 53 is installed on top of the main body and is formed from, for example, stainless steel. The slit plate 53 prevents the dispersed gravel layer 52 from falling into the pressure chamber 21. The thickness t3 (see Figure 3) of the slit plate 53 is approximately 60 mm. However, the thickness t3 is not limited to 60 mm.

[0037] The air dispersion beams 54 are provided on the lowest floor of the underdrain device 5. The air dispersion beams 54 are made of reinforced concrete and support the slit plates 53 from below. The thickness t4 of the air dispersion beams 54 (see FIG. 3) is approximately 360 mm. However, the thickness t4 is not limited to 360 mm.

[0038] The treated water filtered by the filter material 4 passes through the under-drain device 5 and is collected in a pressure chamber 21 provided below the under-drain device 5. The treated water collected in the pressure chamber 21 passes through the pressure chamber 21 and a collection culvert 22 to be supplied to a water reservoir or the like.

[0039] As described above, the activated carbon used as the filter medium 4 captures suspended solids in the water to be treated 3 supplied to the activated carbon adsorption basin 2, as well as odors, colors, organic matter, and the like dissolved in the water to be treated 3 supplied to the activated carbon adsorption basin 2. Therefore, after the activated carbon adsorption basin 2 starts operating, cleaning is periodically performed to remove suspended solids adhering to the filter medium 4. Cleaning of the filter medium 4 is called backwashing, and is performed, for example, by supplying water to the filter medium 4 while blowing air into it. The "backwashing" of this embodiment will be further described with reference to FIGS. 2 and 3.

[0040] When backwashing is performed, for example, as shown by an arrow A1 in Figures 2 and 3, backwash water is supplied from the pressure chamber 21 through the under-drain device 5 to the filter medium 4. The flow rate of the backwash water is, for example, 1 m 2 per 0.3m 3 / min or more, 0.5m 3 / min or less. However, the flow rate of the backwash water is not limited to this range.

[0041] When backwashing is performed, backwash air is supplied to the filter medium 4 at the same time as the supply of backwash water. That is, for example, as indicated by arrow A2 in FIG. 2, the backwash air supplied from the cleaning air conduit 23 (see FIG. 1) is led into the air dispersion beam 54. Then, as indicated by arrow A3 in FIGS. 2 and 3, the backwash air led into the air dispersion beam 54 is blown out from a plurality of holes formed in the side of the air dispersion beam 54, passes through the under-drain device 5 and is supplied to the filter medium 4. The flow rate of the backwash air is, for example, 1 m 2 per 0.5m 3 / min or more, 1.0m 3 / min or less. However, the flow rate of the backwash air is not limited to this range.

[0042] In the backwashing process, the backwash water and backwash air vigorously agitate the activated carbon serving as the filter medium 4, causing the suspended matter adhering to the filter medium 4 to peel off from the filter medium 4. As the backwash water is supplied to the filter medium 4, the water level in the activated carbon adsorption basin 2 rises. Just before the water in the activated carbon adsorption basin 2 flows out into the backwash water outflow conduit 24 (see Figure 1), the supply of backwash air is stopped. Subsequently, only the backwash water is supplied to the filter medium 4, and the captured suspended matter (i.e., suspended matter) that has become suspended in the layer of the filter medium 4 due to the simultaneous supply of the backwash water and backwash air is discharged through the backwash water outflow conduit 24.

[0043] The cleaning (i.e., backwashing) of the filter media 4 needs to be performed uniformly throughout the activated carbon adsorption basin 2. Therefore, the porous concrete layer 51 is required to have the function of uniformly distributing the backwash water and backwash air.

[0044] As described above with reference to Figures 1 to 3, the functions required of the porous concrete layer 51 include the function of supporting the filter material 4, the function of allowing the treated water filtered by the filter material 4 to pass through evenly and collect it in the pressure chamber 21, and the function of evenly dispersing the backwash water and backwash air.

[0045] If a defect such as a crack or fissure occurs in the porous concrete layer 51, the filter material 4, such as activated carbon, may fall through the porous concrete layer 51 and become mixed with the treated water stored in the pressure chamber 21. Furthermore, if the defect, such as a crack, is serious, the filter material 4 may fall into the pressure chamber 21, causing disruption to the operation of the facility. In other words, if a defect such as a crack or fissure occurs in the porous concrete layer 51, the porous concrete layer 51 may not be able to function properly. Therefore, a non-destructive method for diagnosing the integrity of the porous concrete layer 51 is desirable. However, as mentioned above, the porous concrete layer 51 is formed in a coarse state compared to ordinary concrete. Therefore, it is difficult to non-destructively diagnose the integrity of the porous concrete layer 51 using a method for non-destructively diagnosing the integrity of ordinary concrete bodies. In addition, knowledge and skill are required to identify the defective part to be diagnosed and to diagnose the soundness of the porous concrete layer 51 using only images based on the exploration data acquired by exploring the porous concrete layer 51 with an electromagnetic radar probe.

[0046] In contrast, the non-destructive diagnostic method, non-destructive diagnostic device, and non-destructive diagnostic program of this embodiment perform machine learning using exploration data obtained from a healthy porous concrete layer as training data, generate and store a learning model for a healthy porous concrete layer, and diagnose the soundness of the porous concrete layer 51 based on the learning model. The nondestructive diagnostic method, the nondestructive diagnostic device, and the nondestructive diagnostic program according to this embodiment will be further described below with reference to the drawings.

[0047] FIG. 4 is a block diagram showing the configuration of the main parts of the nondestructive diagnostic device according to this embodiment. The nondestructive diagnostic device 6 according to this embodiment has a computer 61 and a storage unit 62. The computer 61 has a control unit 63 (see FIG. 5 ) and reads out a program 621 stored in the storage unit 62 to execute various calculations and processes. The term "computer" as used here is not limited to a personal computer, but also includes an arithmetic processing unit, a microcomputer, etc. included in information processing equipment, and is a general term for equipment and devices that can realize the functions of the present invention by a program.

[0048] The storage unit 62 stores a program 621 to be executed by the computer 61. Examples of the storage unit 62 include a semiconductor memory or a hard disk drive (HDD) built into the nondestructive diagnostic device 6. Alternatively, the storage unit 62 may be an external storage device connected to the computer 61.

[0049] The program 621 of this embodiment is an example of the "non-destructive diagnostic program" of the present invention. The program 621 includes a sequence program for measurement, an image processing program for image processing, an arithmetic program, and the like. The program 621 is not limited to being stored in the storage unit 62, but may be stored in advance in a computer-readable storage medium and distributed, or may be downloaded to the non-destructive diagnostic device 6 via a network.

[0050] FIG. 5 is a block diagram showing a specific configuration of the main parts of the nondestructive diagnostic device according to this embodiment. Figure 6 shows an example of an image related to the exploration data obtained from a porous concrete layer specimen.

[0051] The nondestructive diagnosis device 6 according to this embodiment includes a control unit 63, a storage unit 62, a touch panel 64, and a communication unit 65. The control unit 63 is, for example, a central processing unit (CPU) that reads a program 621 (see FIG. 4 ) stored in the storage unit 62 and executes various calculations and processes. The control unit 63 includes an image processing unit 631, a machine learning unit 632, and a soundness diagnosis unit 633. The image processing unit 631, the machine learning unit 632, and the soundness diagnosis unit 633 are realized by the computer 61 executing the program 621 stored in the storage unit 62. The image processing unit 631, the machine learning unit 632, and the soundness diagnosis unit 633 may be realized by hardware or a combination of hardware and software. The storage unit 62 includes a healthy whole body exploration data storage unit 622, a defective body exploration data storage unit 623, a learning model storage unit 624, and a post-operation exploration data storage unit 625.

[0052] The image processing unit 631 performs image processing on the exploration data acquired by the electromagnetic radar exploration device 7 during exploration of the interior of the porous concrete layer 51. The electromagnetic radar exploration device 7 is a known electromagnetic radar exploration device that emits electromagnetic waves from a transmitting antenna and receives, via a receiving antenna, the reflected waves reflected at the boundary surface between materials with different dielectric constants within the porous concrete layer 51, thereby exploring the interior of the porous concrete layer 51 and acquiring exploration data. For example, the image processing unit 631 performs at least one of background processing, surface position identification processing for the porous concrete layer 51, noise removal processing, and gain processing on the exploration data received from the electromagnetic radar exploration device 7 via the communication unit 65. The image processing unit 631 also performs processing to display an image of the interior of the porous concrete layer 51 as a result of the image processing on the touch panel 64 or the external monitor 8, thereby visualizing the interior of the porous concrete layer 51.

[0053] The display unit on which the image processing unit 631 displays the image of the inside of the porous concrete layer 51 is not limited to the touch panel 64 and the external monitor 8. The touch panel 64 and the external monitor 8 are examples of the display unit.

[0054] The machine learning unit 632 performs machine learning using the exploration data stored in the healthy whole exploration data storage unit 622 as training data, and generates a learning model for the healthy porous concrete layer 51. Furthermore, the machine learning unit 632 performs machine learning using the exploration data stored in the healthy whole exploration data storage unit 622 and the exploration data stored in the defective body exploration data storage unit 623 as training data, and generates a learning model for the presence or absence of defects inside the porous concrete layer 51.

[0055] The healthy whole inspection data storage unit 622 stores inspection data related to the interior of a sound porous concrete layer 51 that is free from defects. The inspection data stored in the healthy whole inspection data storage unit 622 is an example of the "first inspection data" of the present invention. The inspection data stored in the healthy whole inspection data storage unit 622 is, for example, inspection data acquired by an electromagnetic radar probe 7 inspecting a specimen of the sound porous concrete layer 51 that is not installed as part of the underdrainage device 5. In other words, in this example, the inspection data acquired by the electromagnetic radar probe 7 inspecting the specimen of the sound porous concrete layer 51 is pre-stored in the healthy whole inspection data storage unit 622. The image shown in FIG. 6(a) is an example of an image obtained by image processing the inspection data stored in the healthy whole inspection data storage unit 622 by the image processing unit 631. The specimen for acquiring the first inspection data may be a sound specimen of a single layer of porous concrete, or may be a sound specimen having the same component structure as an underdrainage device actually installed in an activated carbon adsorption basin, etc.

[0056] Alternatively, the exploration data stored in the healthy overall exploration data storage unit 622 is, for example, exploration data acquired by the electromagnetic wave radar exploration device 7 exploring the inside of the porous concrete layer 51 before it is installed as part of the under-drainage device 5 and used. That is, in this example, the exploration data acquired by the electromagnetic wave radar exploration device 7 exploring the healthy porous concrete layer 51 before it is used is stored in advance in the healthy overall exploration data storage unit 622.

[0057] Alternatively, the exploration data stored in the healthy overall exploration data storage unit 622 is, for example, exploration data acquired by using the electromagnetic radar probe 7 to explore the interior of a porous concrete layer 51 that has been installed in at least one of an activated carbon adsorption basin and a sand filtration basin other than the activated carbon adsorption basin or sand filtration basin in which the porous concrete layer 51 to be diagnosed is installed and before use. In other words, in this example, the exploration data acquired by the electromagnetic radar probe 7 to explore the healthy porous concrete layer 51 other than the porous concrete layer 51 to be diagnosed and before use is stored in advance in the healthy overall exploration data storage unit 622.

[0058] The defect body exploration data storage unit 623 stores exploration data related to the interior of the porous concrete layer 51 having defects. The exploration data stored in the defect body exploration data storage unit 623 is an example of the "third exploration data" of the present invention. The exploration data stored in the defect body exploration data storage unit 623 is, for example, exploration data acquired by an electromagnetic wave radar probe 7 exploring a specimen of the porous concrete layer 51 having defects that is not installed as part of the underdrainage device 5. In other words, the exploration data acquired by the electromagnetic wave radar probe 7 exploring the specimen of the porous concrete layer 51 having defects is stored in advance in the defect body exploration data storage unit 623. The specimen for acquiring the third exploration data may be a specimen having a simulated defect in a single layer of porous concrete, or may be a specimen having a simulated defect with the same component structure as an underdrainage device actually installed in an activated carbon adsorption basin, etc.

[0059] Alternatively, the exploration data stored in the defective body exploration data storage unit 623 is, for example, exploration data acquired by using the electromagnetic wave radar probe 7 to explore the interior of a defective porous concrete layer 51 that has been installed and used in at least one of an activated carbon adsorption basin and a sand filtration basin other than the activated carbon adsorption basin and sand filtration basin in which the porous concrete layer 51 to be diagnosed is installed, and has exceeded its design life. In other words, in this example, the exploration data acquired by the electromagnetic wave radar probe 7 exploring the defective porous concrete layer 51 other than the porous concrete layer 51 to be diagnosed after the porous concrete layer 51 has been used is stored in advance in the defective body exploration data storage unit 623.

[0060] The images shown in Figures 6(b) to 6(d) are examples of images obtained by image processing using the image processing unit 631 based on the detection data stored in the defect detection data storage unit 623. Specifically, Figure 6(b) is an example of an image obtained by image processing using the image processing unit 631 based on the detection data related to a specimen of the porous concrete layer 51 having a 5 mm crack within the range A11. Figure 6(c) is an example of an image obtained by image processing using the image processing unit 631 based on the detection data related to a specimen of the porous concrete layer 51 having a 3 mm crack within the range A12. Figure 6(d) is an example of an image obtained by image processing using the image processing unit 631 based on the detection data related to a specimen of the porous concrete layer 51 having a 2 mm crack within the range A13. As shown in the examples of images shown in Figures 6(a) to 6(d), the background is relatively unclear in the images obtained by image processing of the detection data related to the interior of the porous concrete layer 51. Therefore, extensive knowledge is required to visually diagnose the soundness of the porous concrete layer 51.

[0061] Therefore, as described above, the machine learning unit 632 of this embodiment performs machine learning using the exploration data stored in the healthy whole exploration data storage unit 622 as training data, and generates a learning model for the healthy porous concrete layer 51. Furthermore, the machine learning unit 632 of this embodiment performs machine learning using the exploration data stored in the healthy whole exploration data storage unit 622 and the exploration data stored in the defective body exploration data storage unit 623 as training data, and generates a learning model for the presence or absence of defects inside the porous concrete layer 51.

[0062] 1 to 3, the gravel used in the porous concrete layer 51 is uniform or evenly distributed. Therefore, the voids formed in the porous concrete layer 51 are distributed uniformly or evenly within the porous concrete layer 51. Therefore, for example, the machine learning unit 632 performs machine learning using as training data the uniformity, evenness, or continuity of at least one of the gravel and the voids in the images obtained by image processing the exploration data stored in the healthy whole exploration data storage unit 622 by the image processing unit 631, and generates a learning model regarding the presence or absence of defects within the healthy porous concrete layer 51. Furthermore, for example, the machine learning unit 632 performs machine learning using as training data the uniformity, evenness, and continuity of at least one of the gravel and voids in the images obtained by image processing of the exploration data stored in the healthy whole exploration data storage unit 622 by the image processing unit 631, and the non-uniformity, non-uniformity, and discontinuity of at least one of the gravel and voids in the images obtained by image processing of the exploration data stored in the defective body exploration data storage unit 623 by the image processing unit 631, and generates a learning model regarding the presence or absence of defects inside the porous concrete layer 51.

[0063] The learning model storage unit 624 stores the learning model generated by the machine learning unit 632.

[0064] The soundness diagnosis unit 633 uses the exploration data stored in the post-operation exploration data storage unit 625 as input values, diagnoses the soundness of the porous concrete layer 51 installed as part of the underdrain device 5 using the learning model stored in the learning model storage unit 624, and outputs the presence or absence of defects as a diagnosis result. The learning model used by the soundness diagnosis unit 633 may be a learning model related to a sound porous concrete layer 51 stored in the learning model storage unit 624, or a learning model related to the presence or absence of defects inside the porous concrete layer 51 stored in the learning model storage unit 624.

[0065] The post-operation exploration data storage unit 625 stores exploration data acquired by the exploration of the electromagnetic radar exploration device 7 after the porous concrete layer 51 installed as part of the underdrainage device 5 has been used. The exploration data stored in the post-operation exploration data storage unit 625 is an example of the "second exploration data" of the present invention. The exploration data stored in the post-operation exploration data storage unit 625 is exploration data acquired by the electromagnetic radar exploration device 7 exploring the usage state of the porous concrete layer 51 after it has been installed and used as part of the underdrainage device 5. In other words, the exploration data acquired by the electromagnetic radar exploration device 7 exploring the usage state of the porous concrete layer 51 after the filter medium 4 has been placed on the porous concrete layer 51 is stored in the post-operation exploration data storage unit 625.

[0066] After the underdrain device 5 starts operation, the filter media 4 is replaced, for example, once every two to three years. When the filter media 4 is replaced, the water to be treated 3 (see FIG. 1) and the filter media 4 are removed from the activated carbon adsorption basin 2, exposing the surface (specifically, the upper surface) of the porous concrete layer 51. For example, at this time, the electromagnetic wave radar probe 7 can probe the usage state of the porous concrete layer 51 and obtain probe data, which can be stored in the post-operation probe data storage unit 625.

[0067] FIG. 7 is a flowchart illustrating the nondestructive diagnostic method according to this embodiment. First, in step S11, the interior of the healthy porous concrete layer 51 is probed by the electromagnetic radar probe 7, and the obtained probe data is stored in the healthy overall probe data storage unit 622. As described above with reference to FIGS. 5 and 6 , the probe data stored in the healthy overall probe data storage unit 622 is, for example, probe data related to a specimen of the healthy porous concrete layer 51 that is not installed as part of the underdrainage device 5. Alternatively, the probe data stored in the healthy overall probe data storage unit 622 is, for example, probe data related to the porous concrete layer 51 before it is installed as part of the underdrainage device 5 and used. Alternatively, the probe data stored in the healthy overall probe data storage unit 622 is, for example, probe data related to the porous concrete layer 51 before it is installed and used in at least one of an activated carbon adsorption basin and a sand filtration basin different from the activated carbon adsorption basin and the sand filtration basin in which the porous concrete layer 51 to be diagnosed is installed.

[0068] Next, in step S12, the interior of the porous concrete layer 51 having a defect is probed by the electromagnetic wave radar probe 7, and the obtained probe data is stored in the defective body probe data storage unit 623. As described above with reference to Figures 5 and 6, the probe data stored in the defective body probe data storage unit 623 is, for example, probe data related to a specimen of the porous concrete layer 51 having a defect that is not installed as part of the underdrainage device 5. Alternatively, the probe data stored in the defective body probe data storage unit 623 is, for example, probe data related to a porous concrete layer 51 having a defect that has been installed and used in at least one of an activated carbon adsorption basin and a sand filtration basin other than the activated carbon adsorption basin and the sand filtration basin in which the porous concrete layer 51 to be diagnosed is installed, and that has exceeded its design life.

[0069] Next, in step S13, the machine learning unit 632 performs machine learning using a known machine learning algorithm such as a neural network, using the exploration data stored in the healthy whole exploration data storage unit 622 as training data, to generate a learning model for the healthy porous concrete layer 51. Furthermore, the machine learning unit 632 performs machine learning using a known machine learning algorithm such as a neural network, using the exploration data stored in the healthy whole exploration data storage unit 622 and the exploration data stored in the defective body exploration data storage unit 623 as training data, to generate a learning model for the presence or absence of defects inside the porous concrete layer 51.

[0070] Subsequently, in step S14, the learning model generated by the machine learning unit 632 is stored in the learning model storage unit 624.

[0071] Next, in step S15, the interior of the porous concrete layer 51 after it has been installed and used as part of the underdrain device 5 is inspected by the electromagnetic radar probe 7, and the acquired inspection data is stored in the post-operation inspection data storage unit 625. As described above with reference to Figure 5, the inspection data stored in the post-operation inspection data storage unit 625 is inspection data relating to the usage state of the porous concrete layer 51 after it has been installed and used as part of the underdrain device 5.

[0072] Subsequently, in step S16, the soundness diagnosis unit 633 uses the exploration data stored in the post-operation exploration data storage unit 625 as input values, diagnoses the soundness of the porous concrete layer 51 installed as part of the underdrain device 5 using the learning model stored in the learning model storage unit 624, and outputs the presence or absence of defects as the diagnosis result. The learning model used by the soundness diagnosis unit 633 is, for example, a learning model related to a sound porous concrete layer 51 stored in the learning model storage unit 624. Alternatively, the learning model used by the soundness diagnosis unit 633 is, for example, a learning model related to the presence or absence of defects inside the porous concrete layer 51 stored in the learning model storage unit 624.

[0073] According to the non-destructive diagnosis method, non-destructive diagnosis device, and non-destructive diagnosis program of the present embodiment, the machine learning unit 632 performs machine learning using the exploration data acquired from a sound porous concrete layer as training data, and generates a learning model for the sound porous concrete layer 51. The learning model storage unit 624 stores the learning model generated by the machine learning unit 632. The soundness diagnosis unit 633 uses the exploration data stored in the post-operation exploration data storage unit 625 as input values, and diagnoses the soundness of the porous concrete layer 51 installed as part of the underdrain device 5 using the learning model stored in the learning model storage unit 624, i.e., the learning model for the sound porous concrete layer 51. This makes it possible to non-destructively diagnose the soundness of the porous concrete layer 51, which is formed in a rougher state than normal concrete.

[0074] If the exploration data stored in the whole-body sound exploration data storage unit 622 is exploration data acquired by exploring the inside of a specimen of a sound porous concrete layer 51 with the electromagnetic wave radar probe 7, the machine learning unit 632 performs machine learning using the exploration data for the simple porous concrete layer 51 as the specimen as training data to generate a learning model, and the soundness diagnosis unit 633 can diagnose the soundness of the porous concrete layer 51 to be diagnosed. This allows the soundness diagnosis unit 633 to diagnose the soundness of the porous concrete layer 51 in a simpler manner.

[0075] If the exploration data stored in the overall soundness exploration data storage unit 622 is exploration data acquired by using the electromagnetic radar exploration device 7 to explore the interior of the porous concrete layer 51 before it is installed as part of the underdrainage device 5 and used, the machine learning unit 632 performs machine learning using the exploration data related to the porous concrete layer 51 that was actually installed as part of the underdrainage device 5 as training data to generate a learning model, and the soundness diagnosis unit 633 can diagnose the soundness of the porous concrete layer 51. This allows the soundness diagnosis unit 633 to diagnose the soundness of the porous concrete layer with higher accuracy.

[0076] If the exploration data stored in the overall health exploration data memory unit 622 is exploration data obtained by using an electromagnetic radar probe 7 to explore the inside of a porous concrete layer 51 that has been installed in at least one of an activated carbon adsorption pond and a sand filtration pond other than the activated carbon adsorption pond or sand filtration pond in which the porous concrete layer 51 to be diagnosed is installed and before it is used, the health diagnosis unit 633 can diagnose the health of the porous concrete layer 51 installed as part of the lower drainage device 5 even in an activated carbon adsorption pond or sand filtration pond after the porous concrete layer 51 has already been used.

[0077] Furthermore, the machine learning unit 632 performs machine learning using the exploration data acquired from the sound porous concrete layer and the porous concrete layer 51 having a defect as training data, and generates a learning model regarding the presence or absence of defects in the porous concrete layer 51. The learning model storage unit 624 stores the learning model generated by the machine learning unit 632. Furthermore, the soundness diagnosis unit 633 uses the exploration data stored in the post-operation exploration data storage unit 625 as input values, and diagnoses the soundness of the porous concrete layer 51 installed as part of the underdrain device 5 using the learning model stored in the learning model storage unit 624, i.e., the learning model regarding the presence or absence of defects in the porous concrete layer 51. This enables the soundness of the porous concrete layer 51 to be diagnosed with higher accuracy.

[0078] When the exploration data stored in the defect exploration data storage unit 623 is exploration data relating to a specimen of a porous concrete layer 51 having a defect that is not installed as part of the underdrainage device 5, the machine learning unit 632 performs machine learning using the exploration data relating to the simple porous concrete layer 51 as the specimen as training data to generate a learning model, and the soundness diagnosis unit 633 can diagnose the soundness of the porous concrete layer 51 to be diagnosed. This allows the soundness diagnosis unit 633 to diagnose the soundness of the porous concrete layer 51 in a simpler manner.

[0079] If the exploration data stored in the defect exploration data memory unit 623 is exploration data regarding a porous concrete layer 51 having defects after being installed and used in at least one of an activated carbon adsorption pond and a sand filtration pond other than the activated carbon adsorption pond and the sand filtration pond in which the porous concrete layer 51 to be diagnosed is installed, the soundness diagnosis unit 633 can diagnose the soundness of the porous concrete layer 51 to be diagnosed, which is installed as part of the lower drainage device 5, by using the exploration data regarding the porous concrete layer 51 installed in at least one of an activated carbon adsorption pond and a sand filtration pond other than the activated carbon adsorption pond and the sand filtration pond in which the porous concrete layer 51 to be diagnosed is installed.

[0080] The above describes the embodiments of the present invention. However, the present invention is not limited to the above embodiments, and various modifications can be made without departing from the scope of the claims. The configurations of the above embodiments can be partially omitted or arbitrarily combined in a different manner from the above. [Explanation of symbols]

[0081] 2: Activated carbon adsorption basin, 3: Water to be treated, 4: Filter material, 5: Under-water collection device, 6: Non-destructive diagnostic device, 7: Electromagnetic wave radar probe, 8: External monitor, 21: Pressure chamber, 22: Water collection culvert, 23: Cleaning air culvert, 24: Backwash water outflow culvert, 51: Porous concrete layer, 52: Distributed gravel layer, 53: Slit plate, 54: Air distribution beam, 61: Computer, 62: Memory unit, 63: Control unit, 64: Touch panel, 65: Communication unit, 621: Program, 622: Overall soundness inspection data memory unit, 623: Defective body inspection data memory unit, 624: Learning model memory unit, 625: Post-operation inspection data memory unit, 631: Image processing unit, 632: Machine learning unit, 633: Soundness diagnosis unit

Claims

1. A method for non-destructively diagnosing the soundness of a porous concrete layer of an under-drainage device installed in at least one of an activated carbon adsorption basin and a sand filtration basin, comprising: a step of detecting the inside of the sound porous concrete layer using an electromagnetic wave radar probe and storing first detection data obtained; performing machine learning using the stored first exploration data as training data to generate a learning model for the sound porous concrete layer; storing the generated learning model; a step of acquiring second exploration data by using the electromagnetic wave radar exploration device to explore the inside of the porous concrete layer after it has been installed and used as part of the underdrainage device; a step of diagnosing the soundness of the porous concrete layer installed as part of the underdrain device using the acquired second exploration data as an input value and the stored learning model; A non-destructive diagnostic method for a porous concrete layer, comprising:

2. A non-destructive diagnosis method for a porous concrete layer as described in claim 1, characterized in that the first exploration data is exploration data obtained by exploring the inside of a specimen of the healthy porous concrete layer using the electromagnetic radar exploration device.

3. A non-destructive diagnosis method for a porous concrete layer as described in claim 1, characterized in that the first exploration data is exploration data obtained by exploring the inside of the porous concrete layer using the electromagnetic radar exploration device before it is installed and used as part of the lower drainage device.

4. A non-destructive diagnosis method for porous concrete layers as described in claim 1, characterized in that the first exploration data is exploration data obtained by exploring the inside of the porous concrete layer using the electromagnetic radar exploration device before it is installed and used as part of the lower drainage device installed in at least one of an activated carbon adsorption pond other than the activated carbon adsorption pond and a sand filtration pond other than the sand filtration pond.

5. a step of storing third exploration data obtained by exploring the interior of the porous concrete layer having defects using the electromagnetic wave radar exploration device; performing the machine learning using the stored first inspection data and the stored third inspection data as training data to generate a learning model regarding the presence or absence of the defect; The non-destructive diagnosis method for a porous concrete layer according to claim 1, further comprising:

6. A non-destructive diagnosis method for porous concrete layers as described in claim 5, characterized in that the third inspection data is inspection data obtained by inspecting the inside of a specimen of the porous concrete layer having the defect using the electromagnetic radar inspection device.

7. A non-destructive diagnosis method for porous concrete layers as described in claim 5, characterized in that the third exploration data is exploration data obtained by exploring the inside of the porous concrete layer having the defect using the electromagnetic radar exploration device after it has been installed and used as part of the lower drainage device installed in at least one of an activated carbon adsorption pond other than the activated carbon adsorption pond and a sand filtration pond other than the sand filtration pond.

8. A device for non-destructively diagnosing the soundness of a porous concrete layer of a lower drainage device installed in at least one of an activated carbon adsorption basin and a sand filtration basin, a sound whole inspection data storage unit that stores first inspection data acquired by inspection using an electromagnetic wave radar inspection device, the first inspection data relating to the inside of the sound porous concrete layer; a machine learning unit that performs machine learning using the first exploration data stored in the healthy whole exploration data storage unit as training data to generate a learning model for the healthy porous concrete layer; a learning model storage unit that stores the learning model generated by the machine learning unit; a post-operation survey data storage unit that stores second survey data acquired by surveying the electromagnetic wave radar survey device, the second survey data relating to the inside of the porous concrete layer after it has been installed and used as part of the under-drainage device; and a soundness diagnosis unit that uses the second exploration data stored in the post-operation exploration data storage unit as an input value and diagnoses the soundness of the porous concrete layer installed as part of the underdrain device using the learning model stored in the learning model storage unit; A non-destructive diagnostic device for porous concrete layers, comprising:

9. A program executed by a computer of a device for non-destructively diagnosing the soundness of a porous concrete layer of an under-drainage device installed in at least one of an activated carbon adsorption basin and a sand filtration basin, The computer, a step of detecting the inside of the sound porous concrete layer using an electromagnetic wave radar probe and storing first detection data obtained; performing machine learning using the stored first exploration data as training data to generate a learning model for the sound porous concrete layer; storing the generated learning model; a step of acquiring second exploration data by using the electromagnetic wave radar exploration device to explore the inside of the porous concrete layer after it has been installed and used as part of the underdrainage device; a step of diagnosing the soundness of the porous concrete layer installed as part of the underdrain device using the acquired second exploration data as an input value and the stored learning model; A non-destructive diagnostic program for porous concrete layers, characterized by executing the above.

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