Deterioration prediction device, deterioration prediction method, and program

Abstract

A deterioration prediction device according to the present disclosure is provided with: a sensor history information acquisition means that acquires sensor history information of a structure; a strata information acquisition means that acquires strata information of the ground under the structure; a deterioration prediction means that predicts the deterioration state of the structure at a future point in time on the basis of the sensor history information and the strata information; and an output means that outputs the deterioration prediction result of the structure.

Description

[Technical field]

[0001] The present disclosure relates to a deterioration prediction device, a deterioration prediction method, and a recording medium. [Background technology]

[0002] Structures such as roads and runways require repairs due to deterioration over time. There are technologies that can predict the deterioration state of structures in order to plan repairs.

[0003] For example, Patent Document 1 discloses a structure inspection support system that analyzes the deterioration state of a structure and generates data on predicted values ​​of the future degree of deterioration. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-057192 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the case of large structures such as roads, bridges, and runways, when displacement of the ground (surface layer) occurs that affects the entire structure, deterioration of the surface such as cracks occurs. This surface deterioration occurs separately from displacement caused by deterioration of the structure itself, such as deterioration of surface components (individual deterioration). For this reason, it is necessary to predict the deterioration state of a structure by taking into account not only individual deterioration but also deterioration associated with ground displacement.

[0006] There are various types of ground, and the progression of displacement such as land subsidence differs depending on the type. The structure inspection support system described in Patent Document 1 uses sensor history information and regional characteristic information to determine the deterioration level of a structure. However, even in the same region, the ground structure varies greatly depending on the location, making it difficult to appropriately predict the deterioration state of a structure.

[0007] An example of an objective of the present disclosure is to provide a deterioration prediction device, etc. that can appropriately predict the deterioration state of a structure. [Means for solving the problem]

[0008] A deterioration prediction device in one embodiment of the present invention comprises a sensor history information acquisition means for acquiring sensor history information of a structure, a stratum information acquisition means for acquiring stratum information of the ground beneath the structure, a deterioration prediction means for predicting the deterioration state of the structure at a future point in time based on the sensor history information and the stratum information, and an output means for outputting the deterioration prediction results of the structure.

[0009] A deterioration prediction device method in one embodiment of the present invention acquires sensor history information of a structure, acquires stratum information of the ground beneath the structure, predicts the deterioration state of the structure at a future point in time based on the sensor history information and the stratum information, and outputs a deterioration prediction result for the structure.

[0010] In one embodiment of the present invention, a recording medium records a program that causes a computer to acquire sensor history information of a structure, acquire stratum information of the ground beneath the structure, predict the deterioration state of the structure at a future point in time based on the sensor history information and the stratum information, and output the deterioration prediction results for the structure. Effect of the Invention

[0011] One example of the effect of the present disclosure is the ability to appropriately predict the deterioration state of a structure. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram illustrating an example of a configuration of a deterioration prediction device according to a first embodiment. [Diagram 2] FIG. 2 is a conceptual diagram showing an example of the configuration of the degradation prediction device and its peripherals according to the first embodiment. [Diagram 3] FIG. 3 is a diagram showing the deterioration state of a structure based on sensor history information on a map. [Figure 4]Figure 4 shows information on the geological layers beneath a structure on a map. [Diagram 5] FIG. 5 is an example of a display screen showing the degree of deterioration of a structure predicted by the deterioration prediction unit. [Figure 6] FIG. 6 shows another example of a display screen of the deterioration degree of a structure predicted by the deterioration prediction unit. [Figure 7] FIG. 7 shows another example of a display screen of the deterioration degree of a structure predicted by the deterioration prediction unit. [Figure 8] FIG. 8 is a flow diagram showing an example of the operation of the deterioration predicting device according to the first embodiment. [Figure 9] FIG. 9 is a block diagram illustrating an example of a hardware configuration of the deterioration predicting device according to the first embodiment. As illustrated in FIG. [Figure 10] FIG. 10 is a block diagram illustrating an example of a configuration of a deterioration prediction device according to the second embodiment. As shown in FIG. [Figure 11] FIG. 11 is a flow diagram showing an example of the operation of the deterioration predicting device according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Next, an embodiment of the present invention will be described with reference to the drawings. Each drawing is for explaining an embodiment of the present invention. However, the embodiment of the present invention is not limited to the description in each drawing. Also, the same numbers are given to similar configurations in each drawing, and repeated description may be omitted. Also, in the drawings used in the following description, description of configurations of parts that are not related to solving the problem of the present invention may be omitted and not illustrated.

[0014] <First embodiment> FIG. 1 is a block diagram showing an example of the configuration of a deterioration prediction device 10 according to the first embodiment. The deterioration prediction device 10 includes a sensor history information acquisition unit 101, a stratum information acquisition unit 102, a deterioration prediction unit 103, and an output unit 104. Each component may store at least a part of the information specified, acquired, and determined by each component in a storage unit (not shown). In this case, each component may acquire necessary information from the storage unit. The deterioration prediction device 10 is a device for predicting the deterioration state of a structure based on the sensor history information acquired from the sensor history information acquisition device and the stratum information of the ground under the structure. Examples of the structure include civil engineering structures such as roads, bridges, scree frames, embankments, piers, revetments, and runways.

[0015] FIG. 2 is a conceptual diagram showing an example of the configuration of the deterioration prediction device 10 and its periphery according to the first embodiment. As shown in FIG. 2, the deterioration prediction device 10 is used as a system including a computer 510, a drive recorder 520 as an example of a sensor history information acquisition device, an SAR 530 as a synthetic aperture radar system (hereinafter, SAR), a terminal device 540 as an example of a display device, and a vehicle 550 as an example of a moving body. However, in this embodiment, the SAR 530 is not an essential component. The network 580 is a communication path that connects each device and system to each other. Note that the deterioration prediction device 10 (computer 510) and the drive recorder 520 may be directly connected to each other, or may be connected via a network or the like.

[0016] The drive recorder 520 outputs sensor history information to a database (not shown). The sensor history information is accumulated data of information acquired from a sensor to determine the status of a structure and its surroundings. Examples of sensors include a camera, a speedometer, or an accelerometer. The drive recorder 520 is mounted on a moving body, for example, to acquire sensor information. Examples of moving bodies include a vehicle and a drone. In addition, instead of the drive recorder 520, the sensor information may be acquired using a fixed camera attached to the moving body, a camera brought to the moving body by a person, or a fixed camera installed on a road. In addition, instead of the drive recorder 520, a fixed camera such as a panoramic camera or a built-in camera attached to the moving body may be used. In addition, the sensor information may be acquired using a camera mounted on a smartphone or tablet brought to the moving body by a person, or a fixed camera installed on a road.

[0017] The SAR 530 is a radar system that transmits and receives radio waves while a flying object such as an artificial satellite or an aircraft is moving, and obtains an image equivalent to that of an antenna with a large aperture. The SAR 530 outputs a measurement image (SAR image) or a ground surface displacement to the deterioration prediction device 10.

[0018] The terminal device 540 displays the deterioration prediction result of the structure output by the deterioration prediction device 10. The terminal device 540 may be any device that can display the deterioration prediction result of the structure. The terminal device 540 may be, for example, a terminal device of a road administrator such as a local government.

[0019] The number of components included in FIG. 2 is an example. For example, the number of drive recorders 520 may be one or more. Alternatively, at least a part of drive recorders 520 may not be mounted on vehicle 550. Note that FIG. 2 shows drive recorder 520 outside vehicle 550 for ease of understanding. However, drive recorder 520 may be mounted inside vehicle 550.

[0020] Returning to FIG. 1, the sensor history information acquisition unit 101 acquires sensor history information of a structure. The sensor history information is information that accumulates sensor information from the past to the present, and is stored in a database (not shown). The sensor history information acquisition unit 101 acquires the sensor history information from this database. Note that the sensor information may be accumulated in the sensor history information acquisition device. In this case, the sensor history information acquisition unit 101 may acquire the sensor history information directly from the sensor history information acquisition device. The sensor history information acquisition unit 101 acquires sensor history information for an arbitrary period, triggered by detection of an operation by a user to start predicting deterioration of a structure. When acquiring history information of image data as sensor history information, the sensor history information acquisition unit 101 acquires information on the date and time when each image data was acquired and the position where the image data was taken, together with the history of the image data. The position information includes, for example, a position on a map, latitude and longitude, and position information by a Global Navigation Satellite System (GNSS) or a Global Positioning System (GPS). The sensor history information acquisition unit 101 may acquire information at the time of sensor acquisition, such as brightness, the presence or absence of shadow, the presence or absence of backlight, or the surrounding weather, together with the sensor history information.

[0021] The stratum information acquisition unit 102 acquires stratum information of the ground under the structure. The ground is the surface layer of the ground that supports the foundation of the structure. The stratum information acquisition unit 102 acquires stratum information of the ground where the target structure for deterioration prediction exists, for example, from a public database.

[0022] Strata information includes information on developed land, surface strata, soil information, or topographical information. Developed land information is information on construction work carried out on land other than residential land to turn it into a residential area, etc. Developed land information includes, for example, existing embankments, belly-up embankments, valley filling, natural ground, non-embankments, and other fills. Surface strata are strata that have accumulated near the ground surface. Surface strata include geological names such as low-lying marsh deposits, natural levees, sandbar deposits, Iimuro Formation, Kakio Formation, Musashino Loam Formation, Musashino Gravel Formation, Obaradai Gravel Layer / Zengyo Gravel Layer, Shimosueyoshi Loam Layer / Shimosueyoshi Formation, Hayada Loam Formation, Maioka Loam Formation, Tsurumi Layer / Maioka Layer, or information indicating the strata age such as Middle / Late Pleistocene, Early Pleistocene, Socene, Late Pleistocene, Quaternary Period, etc. Soil information includes, for example, soil types such as immature sand dune soil, black soil, brown lowland soil, gray lowland soil, rubble soil, dark red soil, artificially altered soil, and artificially altered soil, as well as soil properties such as fine sand, coarse sand, silt, and clay. Note that stratum and soil that easily contain water, such as silt or clay, are often soft. Topography information includes natural topography such as loam (volcanic ash) plateaus, debris flow deposits, mountain slopes, old water areas, old river channels, declining lowlands, riverbeds and riverbeds, wetlands, current water areas, gravel areas, natural levees, valley lowlands, foothill debris surfaces and talus, and artificial topography such as artificial flatlands, cut land, reclaimed land, embankments, and gravel mining sites.

[0023] The stratum information may also include information related to land and soil other than the stratum. For example, the information may include (average) slope angle, steep slope designation, annual precipitation, rainwater infiltration basin, low land with poor drainage, landslide hazard area, liquefaction risk, shaking tendency, landslide hazard area, land use type, registered land use, etc. This information allows the softness of the ground to be understood. The land use types include high-rise buildings, low-rise buildings, low-rise building dense areas, public facilities, factories, exhibition halls, parking lots, parks and green spaces, forests, vacant lots, roads, railways, rivers, lakes and marshes, and coasts. The registered land uses include residential land, rice fields, farms, pastures, wilderness, salt fields, springs, ponds and marshes, forests, cemeteries, temple grounds, canal land, water supply land, wastewater channels, reservoirs, embankments, ditches, conservation forests, public roads, parks, railway land, school land, and miscellaneous land. For example, lowlands often found near rivers, ponds, and other water bodies are often soft.

[0024] The stratum information acquisition unit 102 may also acquire time-series information (stratum history) of the stratum as the stratum information. For example, the state of soil before and after development in a developed area, past changes or movements of the river basin, plans for development and modification of the ground surface, development plans, improvement plans, development plans such as underground tunnel construction or plans for building large buildings, and surrounding construction plans may be associated with the information. Strata that were once river basins are often weak. The stratum information used by the deterioration prediction unit 103 may be one or more. The stratum information used by the deterioration prediction unit 103 may be fixed, or may be arbitrarily specified by the user.

[0025] The deterioration prediction unit 103 predicts the deterioration state of the structure at a future time point based on the sensor history information and the stratum information input from the sensor history information acquisition unit 101 and the stratum information acquisition unit 102, respectively. First, the deterioration prediction unit 103 predicts the reference deterioration state of the structure at a future time point using a trained model generated by machine learning. This trained model is a model generated using sensor history information at multiple past time points, such as images of the structure and acceleration, as teacher data, and outputs a prediction of the deterioration of the structure for the sensor history information. In addition, the deterioration prediction unit 103 may predict the reference deterioration state of the structure using a statistical prediction method, such as an autoregressive model, a moving average method, or an exponential average method. In addition, the deterioration prediction unit 103 may predict the reference deterioration state at a future time point based on the reference deterioration speed for each structure calculated from the sensor history information. In other words, the reference deterioration state at a future time point is a deterioration state predicted based only on the sensor history information.

[0026] Next, the deterioration prediction unit 103 predicts the deterioration state of the structure at a future time point by adding the stratum information of the ground under the structure to the reference deterioration state predicted as described above. Specifically, the deterioration prediction unit 103 predicts the deterioration state taking into consideration that the deterioration rate of the structure differs depending on the stratum information of the ground under the structure. For example, when the stratum information is a soft stratum, the deterioration rate becomes larger. In this case, the deterioration prediction unit 103 predicts that the deterioration state will progress more than the reference. On the other hand, when the stratum information is a hard stratum, the deterioration rate is equivalent to the reference deterioration rate. In this case, the deterioration prediction unit 103 predicts that the deterioration state is equivalent to the reference. However, the method of predicting the deterioration state by the deterioration prediction unit 103 is not limited to this. For example, the deterioration prediction unit 103 may generate a model generated using the sensor history information, the stratum information, and the actual deterioration state at a future time point as teacher data, and predict the deterioration state of the structure at a future time point based on the generated model. This model is a model that inputs sensor history information and geological layer information and outputs the deterioration state of a structure at a future time.

[0027] When predicting the deterioration state of a road as a structure, the deterioration prediction unit 103 uses an index representing the deterioration degree of the road. Here, the index representing the deterioration degree of the road will be described. There are multiple types of road deterioration. Road deterioration is classified into multiple types including, for example, cracks, potholes, rutting, and abnormal flatness of the road. Cracks may be classified into different types, such as linear cracks and tortoiseshell cracks, depending on the shape. A linear crack is a single linear crack. A tortoiseshell crack is a tortoiseshell crack that occurs when vertical and horizontal linear cracks are connected. Cracks on a road generally tend to progress in the order of linear cracks, tortoiseshell cracks, and potholes. The deterioration prediction unit 103 may predict, for example, the time of occurrence of potholes based on, for example, sensor history information of the road and geological layer information of the ground.

[0028] The crack degree is expressed by any one of the shape, length, area, and number of cracks, or a combination of these. The crack rate is an example of the crack degree. The crack rate is expressed, for example, by 100 x (crack area / road section area). In this case, the deterioration degree value ranges from 0% to 100%. The crack area is calculated by any method. Note that the method of calculating the crack rate is not particularly limited, and other known calculation methods can be applied in addition to the above.

[0029] The size of a pothole is expressed, for example, by the area, width, length, or depth of the pothole, or a combination of these. The amount of rutting is the depth of a continuous depression in the direction of the road that occurs at the position where the wheels of a vehicle pass (rut portion) due to traffic load.

[0030] The degree of cracking, the number and size of potholes, and the amount of rutting may be calculated based on measurement data obtained by measuring the road surface with a sensor, or these indices may be calculated based on the recognition results of road deterioration recognized from images of the road.

[0031] The flatness may be expressed by the International Roughness Index (IRI). The IRI is an index that relates the road surface to the driver's ride comfort, and is a numerical representation of the degree of unevenness. The IRI may be calculated based on measurement data obtained by measuring the road surface with a sensor. Alternatively, the IRI may be calculated based on the value of an acceleration sensor attached to the vehicle while the vehicle is traveling. Specifically, for example, the IRI is calculated based on the value of the upward and downward acceleration included in the acceleration acquired at the detection position. Note that the method of calculating the IRI is not limited to the above, and any known calculation method may be adopted.

[0032] The deterioration degree is not limited to the above-mentioned indexes, and any index representing road deterioration including, for example, MCI (Maintenance Control Index) may be used. The MCI value is the minimum value of the results of calculating four definition equations using crack rate, rutting amount, and flatness. The MCI decreases with the deterioration of the road. If the structure is a runway, BBI (Boeing Bump Index), which is an index representing flatness, may be used.

[0033] The deterioration prediction unit 103 predicts the deterioration rate using the above-mentioned index as follows, and predicts the deterioration level based on the deterioration rate. For example, a stratum containing soil that easily absorbs water, such as silt or clay, may expand due to freezing inside the stratum (frost heave), and cracks may occur on the surface of the structure. Therefore, when the stratum information includes a stratum that easily absorbs water, the deterioration prediction unit 103 predicts that the cracking rate (deterioration rate) of the structure due to freezing will be high. On the other hand, a stratum that has little moisture or good drainage, such as a sand layer, a gravel layer, or a bedrock, is not easily affected by frost heave. In this case, the deterioration prediction unit 103 predicts that the deterioration rate of the structure will not be affected by frost heave. Furthermore, when a stratum that is easily deformed by heat or pressure is included, the deterioration prediction unit 103 predicts that the cracking rate of the structure due to expansion and compression inside the stratum will be high.

[0034] Furthermore, when the stratum information includes an embankment, there is a possibility of subsidence due to the embankment or the soft stratum of the embankment. That is, the ground on which the embankment is built in a valley or on a slope may subside due to the weight of the soil of the embankment itself. Furthermore, when the soil on which the embankment is built contains construction waste materials such as glass, the ground is weak and may subside. Therefore, the deterioration prediction unit 103 predicts that the deterioration rate of the structure will be higher than the standard deterioration rate according to the stratum information of the ground on which the embankment is built.

[0035] When predicting the deterioration degree, the deterioration prediction unit 103 may further use information on the environment of the structure acquired from an external database. The external database is, for example, weather data or traffic data. The weather data is, for example, precipitation information or temperature information published on the website of the Japan Meteorological Agency or the like. The deterioration prediction unit 103 predicts the deterioration degree taking into account information on the environment of the structure and different deterioration rates. For example, the deterioration prediction unit 103 predicts that areas with heavy precipitation or extremely low temperatures will deteriorate faster than the standard due to the influence of the precipitation or temperature. For example, the deterioration prediction unit 103 predicts that roads and bridges with heavy traffic will deteriorate faster than the standard due to the influence of the traffic volume.

[0036] The deterioration prediction unit 103 may predict the deterioration level taking into consideration that the deterioration rate differs depending on the season. For example, the deterioration prediction unit 103 predicts that the deterioration rate will be higher than the standard rate during the snow melting period in early spring, periods of heavy rainfall, and midwinter.

[0037] The time when the deterioration prediction unit 103 predicts the deterioration degree is set to any time point after the sensor information acquisition date of the sensor history information. The deterioration prediction unit 103 may predict the deterioration degree at a plurality of future time points, for example, in a range of months, seasons (quarters), or years. The deterioration prediction unit 103 may predict the deterioration degree at a future time point designated by the user.

[0038] The deterioration degree prediction may be performed in another device. The other device predicts the deterioration degree at the prediction time and transmits it to the deterioration prediction device 10. The deterioration prediction unit 103 obtains the deterioration degree from the other device and determines the predicted deterioration degree as the future deterioration degree.

[0039] The output unit 104 outputs the deterioration prediction result of the structure predicted by the deterioration prediction unit 103. For example, when the deterioration prediction unit 103 predicts the deterioration state of the structure, the output unit 104 notifies the terminal device 540 of the deterioration prediction result of the structure. The output unit 104 may select the notification destination.

[0040] The output unit 104 may display the deterioration prediction result on a display of a terminal device 540 of a road administrator such as a local government. The output unit 104 may display the deterioration prediction result of the structure at the time of prediction superimposed on a map.

[0041] Here, the deterioration prediction method and display method by the deterioration prediction unit 103 will be described with reference to the drawings. FIG. 3 is a diagram showing the deterioration state of a structure based on sensor history information on a map. In FIG. 3, the lines are roads. In FIG. 3, "deteriorated" is a portion where the structure is deteriorated, and "not deteriorated" is a portion where the structure is not deteriorated. FIG. 4 is a diagram showing stratum information of the ground under the structure on a map. As shown in FIG. 4, the boundary line B is a hard stratum and a soft stratum. The hard stratum and the soft stratum are classified by an index such as a converted N value. For example, generally, if the converted N value of clay soil is 3 or less, and if the converted N value of sandy soil is 5 or less, it is classified as a soft ground. However, the hard stratum and the soft stratum may be classified by an index other than the converted N value.

[0042] FIG. 5 is an example of a display screen of the deterioration degree of a structure predicted by the deterioration prediction unit 103. In the example of FIG. 5, the deteriorated portion in FIG. 3 is shown in a color with different shading according to the degree of deterioration after six months. That is, the area indicated by X in FIG. 5 is a hard layer, so the deterioration degree is equal to the standard. On the other hand, the area indicated by Y in FIG. 5 is a soft layer, so the deterioration degree is larger than the standard. In addition, the output unit 104 displays on the map the deterioration state according to the deterioration degree at a time selected from a plurality of time points. That is, as shown in FIG. 5, the output unit 104 may display a slider bar and a slider, and switch the deterioration prediction result of the prediction time to be displayed based on the position of the slider on the slider bar. In the example of FIG. 5, the deterioration prediction results after six months, 12 months, and 18 months can be switched and displayed.

[0043] Fig. 6 is another example of a display screen of the deterioration degree of a structure predicted by the deterioration prediction unit 103. The display screen of Fig. 6 does not switch the deterioration prediction result displayed based on the position of the slider as in Fig. 5, but has a prediction button. When this prediction button is pressed with the original image displayed, the deterioration prediction unit 103 may automatically switch the screen to display the deterioration prediction result of the time when it is predicted that the deterioration degree of the road in the display screen will first exceed the threshold value. As shown in Fig. 6, if the time when it is predicted that the deterioration degree will first exceed the threshold value is six months from now, the deterioration prediction unit 103 automatically switches to a screen of the deterioration prediction result six months from now.

[0044] The output unit 104 may also display a graph showing the predicted time and the change in the deterioration degree together with the predicted image. FIG. 7 is another example of a display screen of the deterioration degree of a structure predicted by the deterioration prediction unit 103. FIG. 7 includes a graph showing the relationship between the predicted time and the MCI. As shown in FIG. 7, the output unit 104 may also display the correspondence between the predicted image to be displayed and the position in the graph. For example, a plot on the graph or a value on the axis corresponding to the predicted image to be displayed may be displayed with more emphasis than other plots or values. Furthermore, the output unit 104 may display a threshold value of the deterioration degree at which repair of the structure is required on the graph.

[0045] Furthermore, the output unit 104 may display on a graph the time or date when the deterioration level is predicted to exceed the threshold value, as shown in Fig. 7. In the example of Fig. 7, the time when the deterioration level is predicted to exceed the threshold value is April 20, 2023, which is 16 months from now. Furthermore, the output unit 104 may change the color of the position on the graph corresponding to the time when the deterioration level is predicted to exceed the threshold value, or may highlight the position by displaying an icon, etc.

[0046] In addition, by pressing the "Crack Rate" or "IRI" button on the screen of Figure 7, a graph relating to the predicted time and crack rate and a graph relating to the predicted time and IRI may be displayed.

[0047] [Explanation of operation] 8 is a flow diagram showing an example of the operation of the deterioration prediction device 10 according to the first embodiment. The sensor history information acquisition unit 101 acquires sensor history information of the structure (step S101). The stratum information acquisition unit 102 acquires stratum information of the ground below the structure (step S102). The deterioration prediction unit 103 predicts the deterioration state of the structure at a future time point based on the sensor history information and the stratum information (step S103). Next, the output unit 104 outputs the deterioration prediction result of the structure (step S104).

[0048] In the deterioration prediction device 10, the deterioration prediction unit 103 predicts the deterioration state of a structure at a future time point based on the sensor history information and the geological layer information. This makes it possible to predict the deterioration state of a structure by taking into account not only the deterioration information of the structure surface but also the geological layer information of the ground. Therefore, the deterioration prediction device 10 makes it possible to appropriately predict the deterioration of a structure.

[0049] [Hardware configuration] Next, the hardware configuration of the deterioration prediction device 10 will be described. Each component of the deterioration prediction device 10 may be configured by a hardware circuit. Alternatively, in the deterioration prediction device 10, each component may be configured using a plurality of devices connected via a network. For example, the deterioration prediction device 10 may be configured by using cloud computing. Alternatively, in the deterioration prediction device 10, a plurality of components may be configured by a single piece of hardware. Alternatively, the deterioration prediction device 10 may be realized as a computer device including a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory). The deterioration prediction device 10 may be realized as a computer device including a network interface circuit (NIC) in addition to the above configuration.

[0050] 9 is a block diagram showing an example of a hardware configuration of the deterioration prediction device 10. The deterioration prediction device 10 includes a CPU 610, a ROM 620, a RAM 630, a storage device 640, and a NIC 650, and constitutes a computer device. The CPU 610 reads a program from the ROM 620 and / or the storage device 640. The CPU 610 then controls the RAM 630, the storage device 640, and the NIC 650 based on the read program. The computer including the CPU 610 then controls these configurations and realizes the functions of the sensor history information acquisition unit 101, the geological layer information acquisition unit 102, the deterioration prediction unit 103, and the output unit 104 shown in FIG. 1.

[0051] When implementing each function, the CPU 610 may use the RAM 630 or the storage device 640 as a temporary storage medium for programs and data. Alternatively, the CPU 610 may read, using a storage medium reading device (not shown), a program contained in a storage medium 690 that stores a program in a computer-readable manner. Alternatively, the CPU 610 may receive a program from an external device (not shown) via the NIC 650, store the program in the RAM 630 or the storage device 640, and operate based on the stored program.

[0052] The ROM 620 stores programs and fixed data executed by the CPU 610. The ROM 620 is, for example, a P-ROM (Programmable-ROM) or a flash ROM. The RAM 630 temporarily stores programs and data executed by the CPU 610. The RAM 630 is, for example, a D-RAM (Dynamic-RAM). The storage device 640 stores data and programs that the deterioration prediction device 10 stores for a long period of time. The storage device 640 may also operate as a temporary storage device for the CPU 610. The storage device 640 is, for example, a hard disk device, a magneto-optical disk device, a solid state drive (SSD), or a disk array device. The ROM 620 and the storage device 640 are non-volatile (non-transitory) recording media. On the other hand, the RAM 630 is a volatile (transitory) recording media. The CPU 610 can operate based on the programs stored in the ROM 620, the storage device 640, or the RAM 630. That is, the CPU 610 can operate using a non-volatile recording medium or a volatile recording medium.

[0053] The NIC 650 relays data exchange with external devices (such as the drive recorder 520, the SAR 530, the terminal device 540, and the vehicle 550) via the network. The NIC 650 is, for example, a LAN (Local Area Network) card. Furthermore, the NIC 650 is not limited to being wired, and may be wireless.

[0054] <Second embodiment> Next, a second embodiment of the present disclosure will be described in detail with reference to the drawings. Below, the description of the second embodiment will be omitted unless the description is unclear.

[0055] 10 is a block diagram showing an example of the configuration of a deterioration prediction device 11 according to the second embodiment. The deterioration prediction device 11 includes a sensor history information acquisition unit 111, a stratum information acquisition unit 112, a ground surface displacement acquisition unit 113, a deterioration prediction unit 114, and an output unit 115. The second embodiment differs from the first embodiment in that the second embodiment includes the ground surface displacement acquisition unit 113. The configurations of the sensor history information acquisition unit 111 and the stratum information acquisition unit 112 are similar to the corresponding configurations of the first embodiment, and therefore description thereof will be omitted.

[0056] The ground surface displacement acquisition unit 113 acquires the ground surface displacement using the measurement image acquired from the ground surface measurement device. Specifically, the ground surface displacement acquisition unit 113 acquires the SAR image captured by the SAR 530, and acquires the ground surface displacement by analyzing the acquired SAR image. Alternatively, the ground surface displacement acquisition unit 113 may directly acquire the ground surface displacement acquired by analyzing the SAR image captured by the SAR 530. The ground surface displacement may include information on the ground surface state such as ground subsidence / uplift, construction / removal of buildings, etc. The ground surface displacement acquisition unit 113 may acquire the observation result using a multispectrum from the SAR 530. In this case, the ground surface displacement acquisition unit 113 can analyze the type of the ground surface in addition to the ground surface displacement using the acquired measurement image. The type of the ground surface includes at least one of water surface, mud, garbage, dry soil, grassland, forest, farmland, and snow cover. The ground surface displacement acquisition unit 113 may use the displacement of a building or other structure. Furthermore, the ground surface displacement acquisition unit 113 may acquire the ground surface displacement using SAR images stored in a cloud system configured using cloud computing to which the drive recorder 520 is connected. The ground surface displacement acquisition unit 113 outputs the acquired ground surface displacement to the deterioration prediction unit 114.

[0057] The deterioration prediction unit 114 evaluates the deterioration of the structure based on the sensor history information, the stratum information, and the ground surface displacement. The deterioration prediction unit 114 predicts the deterioration state of the structure by taking into account the ground surface displacement in addition to the functions of the deterioration prediction unit 103. For example, the deterioration prediction unit 114 predicts that the deterioration rate of the structure is faster as the ground surface displacement is larger than the predicted value, and predicts the deterioration state based on the deterioration rate. The deterioration prediction unit 114 may predict that the deterioration rate will be higher than the standard for a ground surface portion where the ground surface is displaced rapidly and the ground surface displacement is moving nonlinearly, even if the ground surface displacement is not larger than the predicted value.

[0058] The deterioration prediction result may include the time of occurrence of a disaster. The deterioration prediction unit 114 may predict the time of occurrence of a disaster based on the acquired sensor history information, stratum information, and ground surface displacement. For example, the deterioration prediction unit 114 can predict the time of occurrence of a disaster such as the collapse of an embankment, a landslide of a slope or cliff, or a subsidence of a road surface or a roadbed or a roadbed of a railway, based on the ground surface displacement. However, the subsidence is not limited to the settlement of soil under the road surface, and may also occur when a pothole on the road surface increases in size. In this case, the deterioration prediction unit 114 predicts the time of occurrence of a disaster based on the sensor history information. In addition, the deterioration prediction unit 114 can predict the time of occurrence of a disaster such as the collapse of a building, an elevated railway, or an elevated road, or the collapse of a bridge, based on the displacement of abutments, piers, foundations, and other parts that contact the ground. The deterioration prediction unit 114 outputs the deterioration prediction result of the structure predicted in this way to the output unit 115.

[0059] The output unit 115 outputs the deterioration prediction result of the structure predicted by the deterioration prediction unit 114. The output destination and output method are similar to those of the output unit 104 in the first embodiment.

[0060] [Explanation of operation] FIG. 11 is a flow diagram showing an example of the operation of the deterioration prediction device 11 according to the second embodiment. Steps S201 to S202 in this embodiment are similar to steps S101 to S102 in the first embodiment, and therefore will not be described. The ground surface displacement acquisition unit 113 acquires ground surface displacement using a measurement image acquired from the ground surface measurement device (step S203). Next, the deterioration prediction unit 114 predicts the deterioration state of the structure at a future time point based on the sensor history information, the stratum information, and the ground surface displacement (step S204). Finally, the output unit 115 outputs the deterioration prediction result of the structure evaluated by the deterioration prediction unit 114 (step S205).

[0061] In the deterioration prediction device 11 according to the second embodiment, the deterioration prediction unit 114 predicts the deterioration state of a structure at a future time point based on the surface displacement in addition to the sensor history information and the stratum information. By predicting deterioration using dynamic information on the surface displacement, it is possible to predict the progress of future deterioration in detail. Therefore, it is possible to more accurately evaluate the deterioration state of the structure.

[0062] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes.

[0063] (Appendix 1) A sensor history information acquisition means for acquiring sensor history information of a structure; A layer information acquisition means for acquiring layer information of the ground beneath the structure; a deterioration prediction means for predicting a deterioration state of the structure at a future time point based on the sensor history information and the geological layer information; an output means for outputting a deterioration prediction result of the structure; A deterioration prediction device comprising:

[0064] (Appendix 2) A ground surface displacement acquisition means for acquiring a ground surface displacement using a measurement image acquired from the ground surface measurement device, The deterioration prediction means predicts a deterioration state of the structure at a future time based on the sensor history information, the geological layer information, and the ground surface displacement. 2. The degradation prediction device according to claim 1.

[0065] (Appendix 3) When the stratum information is a soft stratum, The deterioration prediction means predicts that deterioration at a future time will be more severe than a reference deterioration. 3. The degradation prediction device according to claim 1 or 2.

[0066] (Appendix 4) When the stratum information is a hard stratum, The deterioration prediction means predicts that deterioration at a future time point will be equivalent to a reference. 3. The degradation prediction device according to claim 1 or 2.

[0067] (Appendix 5) The deterioration prediction means predicts a deterioration state of the structure at a future time point by further using information on the environment of the structure obtained from a database. 5. A deterioration prediction device according to any one of claims 1 to 4.

[0068] (Appendix 6) The deterioration prediction means obtains meteorological information from the database and predicts a deterioration state of the structure. 6. A degradation prediction device according to claim 5.

[0069] (Appendix 7) the structure is a road, The deterioration prediction means predicts the deterioration level of the road at a future time point using at least one of indicators of cracks, potholes, ruts, and irregularities in the road. 7. A deterioration prediction device according to any one of claims 1 to 6.

[0070] (Appendix 8) The deterioration prediction means predicts the deterioration degree at a plurality of future time points, The output means displays on a map a state of deterioration according to a degree of deterioration at a time selected from the plurality of time points. 8. The degradation prediction device according to claim 7.

[0071] (Appendix 9) The deterioration prediction means predicts the deterioration degree at a plurality of future time points, the output means displays a predicted image of the road according to the deterioration level at a time selected from the plurality of time points, and a graph showing a relationship between the plurality of future time points and the deterioration level; 8. The degradation prediction device according to claim 7.

[0072] (Appendix 10) Acquire sensor history information for the structure, Acquire stratum information of the ground beneath the structure; predicting a deterioration state of the structure at a future time based on the sensor history information and the geological layer information; Output the deterioration prediction results of the structure Deterioration prediction methods.

[0073] (Appendix 11) Acquire sensor history information for the structure, Acquire stratum information of the ground beneath the structure; predicting a deterioration state of the structure at a future time based on the sensor history information and the geological layer information; Outputting the deterioration prediction result of the structure. A recording medium for recording a program that causes a computer to execute the above.

[0074] Although the present invention has been described above with reference to the embodiment, the present invention is not limited to the above embodiment. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. [Explanation of symbols]

[0075] 10, 11 Deterioration prediction device 101, 111 Sensor history information acquisition unit 102, 112 Geological information acquisition department 103, 114 Deterioration prediction section 104, 115 Output section 113 Surface Displacement Acquisition Unit 510 Computer 520 Drive Recorder 530 SAR 540 Terminal Equipment 550 vehicles 580 Network 610 CPU 620 ROM 630 RAM 640 Storage device 650NIC

Claims

1. A sensor history information acquisition means for acquiring sensor history information of a structure; A layer information acquisition means for acquiring layer information of the ground beneath the structure; a ground surface displacement acquisition means for acquiring a ground surface displacement using a measurement image acquired from the ground surface measurement device; a deterioration prediction means for predicting a deterioration state of the structure at a future time point based on the sensor history information, the geological layer information, and the ground surface displacement; an output means for outputting a deterioration prediction result of the structure; A deterioration prediction device comprising:

2. When the stratum information is a soft stratum, The deterioration prediction means predicts that deterioration at a future time will be more severe than a reference deterioration. The deterioration prediction device according to claim 1 .

3. When the stratum information is a hard stratum, The deterioration prediction means predicts that deterioration at a future time point will be equivalent to a reference. The deterioration prediction device according to claim 1 .

4. The deterioration prediction means predicts a deterioration state of the structure at a future time point by further using information on the environment of the structure obtained from a database. The deterioration prediction device according to any one of claims 1 to 3.

5. the structure is a road, The deterioration prediction means predicts the deterioration level of the road at a future time point using at least one of indicators of cracks, potholes, ruts, and irregularities in the road. The deterioration prediction device according to any one of claims 1 to 4.

6. The deterioration prediction means predicts the deterioration degree at a plurality of future time points, The output means displays on a map a state of deterioration according to a degree of deterioration at a time selected from the plurality of time points. The deterioration prediction device according to claim 5 .

7. The deterioration prediction means predicts the deterioration degree at a plurality of future time points, the output means displays a predicted image of the road according to the deterioration level at a time selected from the plurality of time points, and a graph showing a relationship between the plurality of future time points and the deterioration level; The deterioration prediction device according to claim 5 .

8. A computer comprising: Acquire sensor history information for the structure, Acquire stratum information of the ground beneath the structure; Obtaining ground surface displacement using the measurement image obtained from the ground surface measurement device; predicting a deterioration state of the structure at a future time based on the sensor history information, the geological layer information, and the ground surface displacement; Output the deterioration prediction results of the structure Deterioration prediction methods.

9. Acquire sensor history information for the structure, Acquire stratum information of the ground beneath the structure; Obtaining ground surface displacement using the measurement image obtained from the ground surface measurement device; predicting a deterioration state of the structure at a future time based on the sensor history information, the geological layer information, and the ground surface displacement; Outputting the deterioration prediction result of the structure. A program that causes a computer to execute the following.

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