Determining device, determining method, and program

Abstract

Provided are a determining device, a determining method, and a program capable of predicting the degree of risk of debris falling from a building before the debris falls. A determining device (100) is provided with a processor (110) and a memory (120) for storing a program to be executed by the processor (110), and determines the degree of risk of predicted falling debris, wherein the processor (110): acquires three-dimensional measurement data relating to the surface of a building, measured by a measuring device; detects, on the basis of the three-dimensional measurement data, a raised area that is a pre-indication of falling debris; estimates the size of the falling debris on the basis of the raised area; estimates the weight of the falling debris from the size and a covering thickness from the surface of the building to reinforcing bars or steel material; and determines the degree of risk on the basis of the weight.

Description

Determination device, determination method, and program

[0001] The present invention relates to a determination device, a determination method, and a program, and more particularly to a technique for determining the predicted risk of falling debris.

[0002] Traditionally, the main method of inspecting infrastructure structures (bridges, tunnels, etc., hereafter referred to as structures) for lifting and peeling (skinning) has been for inspectors to go to the site and visually observe the surface condition for cracks, leaks, etc. Inspectors would use a hammer or metal rod to tap areas judged to be at high risk of lifting or peeling, and determine the degree of deterioration by listening to the difference in sounds between normal and abnormal areas.

[0003] On the other hand, various inspection techniques are currently being developed, and it is expected that these will be utilized to improve the efficiency of inspection work in the future. Various techniques have also been proposed for inspecting the lifting and peeling of buildings mentioned above.

[0004] For example, in Patent Document 1, the spalling prediction and diagnosis method described therein takes an infrared thermal image of the surface of a concrete structure with an infrared camera and simultaneously measures the outside air temperature near the surface, and based on the infrared thermal image and the outside air temperature, calculates the temperature difference of the spalled part as the temperature difference between the healthy part and the spalled part, and the measured temperature environment as the difference between the surface temperature of the healthy part and the outside air temperature, calculates a temperature environment coefficient as the ratio of the calculated temperature difference of the spalled part to the calculated measured temperature environment, and quantitatively evaluates the risk of spalling of the cover concrete (concrete from the rebar surface to the concrete surface) based on the temperature environment coefficient. The spalling risk calculated last time is also compared with the spalling risk calculated this time to predict the timing of spalling.

[0005] JP 2016-006398 A

[0006] Here, there have been proposals for techniques to predict peeling, such as the technique described in Patent Document 1, but no techniques have been proposed for quantifying the risk of peeling once a part that has been predicted to peel off has fallen.

[0007] The present invention has been made in consideration of the above circumstances, and its purpose is to provide a determination device, a determination method, and a program that can predict the risk of falling debris from a building before it falls.

[0008] In order to achieve the above-mentioned object, a first aspect of the present invention provides a determination device that determines the predicted risk of falling debris, and is equipped with a processor and a memory that stores a program to be executed by the processor.The processor acquires three-dimensional measurement data of the surface of a building measured by a measuring device, detects raised areas that are a precursor to falling debris based on the three-dimensional measurement data, estimates the size of the falling debris based on the raised areas, estimates the weight of the falling debris from the size and the cover thickness from the surface of the building to the rebar or steel material, and determines the risk based on the weight.

[0009] In a first aspect of the present invention, a measuring device acquires three-dimensional measurement data of the surface of a structure measured, detects raised areas based on the three-dimensional measurement data, estimates the size and weight of the falling material, and determines the level of danger. This makes it possible to predict the danger of falling material from a structure before it falls.

[0010] A second aspect of the present invention provides a determination device in the first aspect, wherein the processor acquires information on surface displacement from the three-dimensional measurement data and detects a raised region based on the information on the surface displacement.

[0011] A third aspect of the present invention provides a determination device according to the first aspect, in which a processor acquires surface displacement information from three-dimensional measurement data, creates a contour map of the surface based on the surface displacement information, detects surface protrusions based on the contour map, and estimates size based on the protrusions.

[0012] A fourth aspect of the present invention provides the determination device of the third aspect, wherein the processor acquires surface crack information and corrects the size based on the crack information and the contour diagram.

[0013] A fifth aspect of the present invention provides the determination device of the fourth aspect, wherein the processor acquires an image of the surface and acquires crack information from the image.

[0014] A sixth aspect of the present invention provides the determination device of the first aspect, wherein the processor corrects the determined risk level based on the size.

[0015] A seventh aspect of the present invention provides a determination device according to the first aspect, wherein the processor acquires information on changes in the float over time and updates the size based on the information on changes in the float over time.

[0016] A determination device according to an eighth aspect of the present invention is the determination device of the first aspect, wherein the measurement device includes a LiDAR or a stereo camera.

[0017] A determination device according to a ninth aspect of the present invention is the eighth aspect, wherein the LiDAR measures three-dimensional measurement data using an FMCW (Frequency Modulated Continuous Wave) method.

[0018] A tenth aspect of the present invention provides the determination device of the first aspect, wherein the material of the surface of the building includes concrete or a concrete repair material.

[0019] An eleventh aspect of the present invention is a determination method that uses a determination device that has a processor and a memory that stores a program to be executed by the processor and that determines the predicted risk of falling material, and includes the following steps performed by the processor: acquiring three-dimensional measurement data of the surface of a building measured by a measuring device; detecting raised areas that are a precursor to falling material based on the three-dimensional measurement data; estimating the size of the falling material based on the raised areas; estimating the weight of the falling material from the size and the cover thickness from the surface of the building to the rebar or steel material; and determining the risk based on the weight.

[0020] A twelfth aspect of the present invention is a program that executes a determination method using a determination device that determines the predicted risk of falling material, and that includes a processor and a memory that stores a program to be executed by the processor.The program causes the processor to execute the following steps: acquiring three-dimensional measurement data of the surface of a building measured by a measuring device; detecting raised areas that are a precursor to falling material based on the three-dimensional measurement data; estimating the size of the falling material based on the raised areas; estimating the weight of the falling material from the size and the cover thickness from the surface of the building to the reinforcing bars or steel material; and determining the risk based on the weight.

[0021] According to the present invention, the risk of debris falling from a building can be predicted before it falls.

[0022] FIG. 1 is a graph showing the relationship between the elapsed time after construction of a building and the surface displacement of the building, and a diagram showing an example of a cross-section of a building at each inspection. FIG. 2 is a schematic diagram of a building inspection system including a peeling prediction device according to the present invention. FIG. 3 is an external view of an embodiment of a three-dimensional measuring device including an FMCW LiDAR. FIG. 4 is a diagram showing an embodiment in which the three-dimensional shape of the surface of a building is measured using a stereo camera. FIG. 5 is a cross-sectional view of the vicinity of the surface of a building showing an example of a mechanism by which the surface of a building peels. FIG. 6 is a cross-sectional view of the vicinity of the surface of a building showing another example of a mechanism by which the surface of a building peels. FIG. 7 is a block diagram showing an embodiment of the hardware configuration of a peeling prediction device according to the present invention. FIG. 8 is a block diagram showing functions realized by a processor executing a dedicated program. FIG. 9 is a flow diagram explaining a determination method using a determination device. FIG. 10 is a diagram explaining three-dimensional measurement data and a contour diagram. FIG. 11 is a diagram explaining three-dimensional measurement data and a contour diagram. FIG. 12 is a diagram explaining three-dimensional measurement data and a contour diagram. FIG. 13 is a diagram explaining a detection step and a size estimation step. FIG. 14 is a diagram explaining a detection step and a size estimation step. Fig. 15 is a diagram showing changes in impact load over time. Fig. 16 is a block diagram showing functions realized by a processor. Fig. 17 is a diagram explaining a case where a raised area is corrected based on crack information. Fig. 18 is a block diagram showing functions realized by a processor. Fig. 19 is a diagram showing an example of information on changes in lift over time.

[0023] Preferred embodiments of a determination device, a determination method, and a program according to the present invention will be described below with reference to the accompanying drawings.

[0024] <Detection of Debris> First, detection of debris will be described. Debris refers to material that falls due to the peeling or spalling of concrete from the surface of a building. Debris occurs from ceilings, walls, pillars, bridge piers, etc. of buildings due to aging and other factors.

[0025] FIG. 1 is a graph showing the relationship between the elapsed time after construction of a building and the surface displacement of the building, and a diagram showing an example of a cross section of the building at each inspection time.

[0026] In Figure 1, the displacement of the structure's surface is measured at the start of inspection of the structure (the start of measurement during construction) t1, and at each subsequent inspection point (t2, t3, t4, t5, ...). By comparing the displacements at the same positions on the structure's surface, it is possible to observe areas where the surface rises over time since the construction of the structure.

[0027] In the example shown in Figure 1, the building is in a normal state (A) at the start of measurement time t1, but at the inspection time t2, the surface has risen slightly due to deterioration of the building (cracks (B)). The rise at this stage is not noticeable by visual inspection. Note that "cracks" are often caused by corrosion and thickening of the steel (reinforcing bars) inside the building.

[0028] At the initial stage of lifting (C) shown at inspection time t3, as corrosion of the reinforcing bars progresses, cracks also progress, causing the surface of the building to swell (lifting occurs).

[0029] At the final stage of the lifting (D) shown at inspection time t4, the "cracks" have progressed further, reaching the surface of the building, and the "lifting" has become even larger.

[0030] The inspection time point t5 indicates the time point when the concrete cover (concrete from the rebar surface to the concrete surface) falls ((E) Peeling / Falling).

[0031] In the example shown in Figure 1, the displacement (amount of rise) of the surface of the building measured at each inspection gradually increases, and it can be seen that the cover concrete eventually peels off and falls off.

[0032] Deterioration phenomena in buildings include deterioration of rebar due to rebar corrosion, as well as deterioration of concrete strength, cracking, surface deterioration, etc. All of these deterioration phenomena in buildings cause the surface to swell, so the time of peeling and spalling can be predicted from the change in the amount of surface swelling over time, regardless of the cause of deterioration.

[0033] <Schematic Configuration of Inspection System> FIG. 2 is a schematic diagram of a structure inspection system including a determination device.

[0034] The inspection system shown in FIG. 2 is a system for inspecting railway tunnels, and includes a three-dimensional measuring device (measuring device) 10 , a data processing device 14 , and a power supply device 16 .

[0035] The three-dimensional measuring device 10 is mounted on a tripod 12, but may also be mounted on a carriage 18 that travels on a railroad track.

[0036] In this example, the three-dimensional measuring device 10 is a LiDAR (Light Detection And Ranging), and in particular, a FMCW (Frequency Modulated Continuous Wave) LiDAR that can measure distances on the order of several hundred μm, but the present invention is not limited to using distance data (three-dimensional measurement data) measured by an FMCW LiDAR.

[0037] <Three-dimensional measuring device> FIG. 3 is an external view of one embodiment of a three-dimensional measuring device 10 including an FMCW LiDAR.

[0038] In FIG. 3, the three-dimensional measuring device 10 is mounted on a carriage 18 that travels on the railroad tracks as shown in FIG. 2, and measures the distance to the surface of a tunnel, which is a railway structure.

[0039] In addition to the three-dimensional measuring device 10, the carriage 18 is also equipped with a data processing device 14 and a power supply device 16. The power supply device 16 supplies power to the three-dimensional measuring device 10 and the data processing device 14.

[0040] The three-dimensional measuring device 10 measures the distance to a tunnel wall (surface) 20 to obtain three-dimensional measurement data indicating the shape of the tunnel wall 20 .

[0041] In the example shown in Fig. 3, the three-dimensional measuring device 10 scans a wall surface 20 shown in Fig. 3 in the left-right direction (main scanning direction) with an FMCW laser beam at high speed, while also moving the scanning line up and down (sub-scanning direction) across the wall surface 20. This measures the distance from the measurement head of the three-dimensional measuring device 10 to multiple measurement points on each scanning line of the laser beam. Then, three-dimensional measurement data in a polar coordinate system consisting of the irradiation direction of the laser beam and the measured distance is converted into three-dimensional measurement data in a Cartesian coordinate system, thereby obtaining three-dimensional measurement data that indicates the shape of the wall surface 20. In this example, three-dimensional measurement data (point cloud data) of multiple measurement points is obtained as the three-dimensional measurement data.

[0042] The three-dimensional measuring device 10 is considered to measure the uneven shape of the minute wall surface 20 under the following conditions: Measurement accuracy: 50 μm Measurement distance: 2 to 7 m Measurement speed: 10 m for area 2 / sec (laser light speed is equivalent to 4000 rpm)

[0043] Furthermore, the 3D measurement device 10 acquires 3D measurement data of the wall surface 20 at regular intervals while the carriage 18 is moving, for example, and it is preferable to acquire the 3D measurement data so that the measurement areas of the 3D measurement data acquired at each interval partially overlap, in order to create a panoramic composite of the 3D measurement data acquired at each interval.

[0044] The three-dimensional measuring device 10 can achieve the above measurement accuracy by using an FMCW LiDAR, but the conditions such as the measurement accuracy of the three-dimensional measurement data required in the present invention are not limited to the above example, and the three-dimensional measuring device 10 is not limited to an FMCW LiDAR, and various types can be applied.

[0045] For example, instead of the FMCW LiDAR, a TOF (Time of Flight) LiDAR can be used, which measures the time of flight of pulsed projected light to measure the distance to the wall surface 20. Also, the three-dimensional shape of the wall surface 20 can be measured using a stereo camera.

[0046] FIG. 4 is a diagram showing an embodiment in which the three-dimensional shape of the surface of a building is measured using a stereo camera.

[0047] The stereo camera shown in FIG. 4 is composed of a left camera 30L and a right camera 30R, and measures the distance to the wall surface 20 of the photographing target by triangulation.

[0048] In addition, various types of three-dimensional measuring devices 10 can be applied as three-dimensional measuring devices 10 that measure the distance to the wall surface 20 (i.e., three-dimensional measurement data of the wall surface), such as the laser radar three-dimensional shape measuring device described in Japanese Patent Laid-Open No. 9-297014, the imaging device described in Japanese Patent Laid-Open No. 2016-31249, the measuring device using a light-section method using a slit laser light projector, the FM laser radar type distance measuring device described in Japanese Patent No. 3194586, and the optical rangefinder described in Japanese Patent No. 3583906.

[0049] The three-dimensional shape of the tunnel wall surface 20 is measured by the three-dimensional measuring device 10 at the start of measurement (construction) of the tunnel and at the time of periodic inspection after construction. The three-dimensional measurement data of the measured wall surface is stored in a storage device within the data processing device 14 or an external storage device at the start of measurement and at the time of periodic inspection.

[0050] <Mechanism of Peeling of Surface Material of Building> FIG. 5 is a cross-sectional view of the vicinity of the surface of a building, showing an example of the mechanism by which the surface of a building peels off.

[0051] "(A) Normal state" in Figure 5 refers to a normal state such as during construction of a building. The surface in this state is taken as the reference surface. In Figure 5, reference numeral 40 denotes steel material (reinforcing bars).

[0052] The "(B) Cracks," "(C) Early stage of lifting / steel fracture," "(D) Final stage of lifting," and "(C) Peeling" in Figure 5 are caused by corrosion of the reinforcing bars 40 (e.g., salt damage or water leakage), and occur over time since the tunnel was constructed.

[0053] After "(C) Early stage of lifting / steel fracture" in Figure 5, the surface of the building gradually becomes higher than the reference level ("lifting" occurs), leading to the peeling of the cover concrete.

[0054] FIG. 6 is a cross-sectional view of the vicinity of the surface of a building, showing another example of the mechanism by which the surface of the building peels off.

[0055] "(A) Normal state" in Figure 6 refers to a normal state, such as during construction of a building. The surface in this state is taken as the reference surface. In Figure 6, 50 denotes reactive aggregate, and 60 denotes steel.

[0056] The "(B) Cracks," "(C) Early stage of lifting / steel fracture," "(D) Final stage of lifting," and "(C) Peeling" in Figure 5 are caused by deterioration of the concrete strength (e.g., alkali reaction of reactive aggregate 50), and occur over time since construction.

[0057] After "(C) Initial stage of lifting / steel fracture" in Figure 6, the surface of the building gradually becomes higher than the reference level ("lifting" occurs), leading to the peeling of the cover concrete from the surface of the steel 60 to the surface.

[0058] As shown in Figures 5 and 6, if the surface of a building changes over time and "lifting" occurs, "peeling" will occur in the future. This applies regardless of the cause of the "lifting." In other words, it does not matter whether there is reinforcing bars, their material, or shape, or the material and shape of the concrete, how the reinforcing bars are inserted into the concrete, the construction method, or the cause of corrosion (neutralization, frost damage, poor construction, etc.).

[0059] <Hardware Configuration of Determination Apparatus> FIG. 7 is a block diagram showing an embodiment of the hardware configuration of a determination apparatus according to the present invention.

[0060] 7 is configured by, for example, a personal computer, a workstation, or the like, and includes a processor 110, a memory 120, a display 130, an input / output interface 140, and an operation unit 150. This determination device 100 can be incorporated as one function of the data processing device 14 shown in FIG.

[0061] The processor 110 is composed of a CPU (Central Processing Unit) and the like, and controls each part of the determination device 100, and executes various processes for predicting the spalling of the surface of the building by executing a program that performs a determination method. Details of the various processes performed by the processor 110 will be described later.

[0062] The memory 120 includes a flash memory, a read-only memory (ROM), a random access memory (RAM), a hard disk drive, etc. The flash memory, ROM, or hard disk drive is a non-volatile memory that stores an operating system, various programs including a program for executing the determination method according to the present invention, etc. In addition, the non-volatile memory (storage device) such as the flash memory or hard disk drive stores three-dimensional measurement data of the surface of the structure measured by the three-dimensional measurement device 10 at the start of measurement of the structure and during periodic inspections, along with the measurement time.

[0063] The RAM functions as a working area for processing by the processor 110. It also temporarily stores various programs stored in flash memory or the like, and three-dimensional measurement data of the surface of the structure, etc. Note that the processor 110 may have part of the memory 120 (RAM) built in.

[0064] The display 130 displays a screen for operating the determination device 100, as well as graphs created by the determination device 100, and also displays images of the surface properties of the building, and is also used as part of a GUI (Graphical User Interface) when receiving user input of points of interest on the surface of the building from the operation unit 150.

[0065] The input / output interface 140 includes a connection unit connectable to an external device, a communication unit connectable to a network, etc. As the connection unit connectable to an external device, a Universal Serial Bus (USB), a High-Definition Multimedia Interface (HDMI) (HDMI is a registered trademark), etc. can be applied.

[0066] The determination device 100 can be configured as a device independent of the data processing device 14. In this case, the processor 110 can acquire 3D measurement data of the surface of the structure from the data processing device 14 via the input / output interface 140, or, if the 3D measurement data is stored in the cloud, can acquire the 3D measurement data of the surface of the structure from the cloud via the input / output interface 140. The processor 110 can also store the 3D measurement data acquired in this way in the memory 120.

[0067] The operation unit 150 includes a pointing device such as a mouse, a keyboard, and the like, and functions as part of a GUI that uses the display screen of the display unit 130 to accept instruction inputs by user operations.

[0068] First Embodiment A first embodiment of the present invention will be described.

[0069] FIG. 8 is a block diagram showing functions realized by the processor 110 executing a dedicated program.

[0070] The processor 110 implements a three-dimensional measurement data acquisition unit 110A, a contour diagram generation unit 110B, a raised area detection unit 110C, a size estimation unit 110D, a weight estimation unit 110E, and a risk determination unit 110F.

[0071] 9 is a flow diagram illustrating a determination method using the determination device 100. The determination method is performed by the processor 110 of the determination device 100 executing a dedicated program. Each step described below is performed by each functional block diagram of the processor 110 described above.

[0072] The 3D measurement data acquisition unit 110A acquires 3D measurement data of the surface of the structure acquired by the 3D measurement device 10 (step S10: 3D measurement data acquisition step). The contour diagram generation unit 110B then generates a contour diagram based on the acquired 3D measurement data (step S11: contour diagram generation step). Next, the raised area detection unit 110C detects raised areas, which are precursors to falling material, based on the contour diagram (step S12: detection step). Next, the size estimation unit 110D estimates the predicted size (area) of the falling material based on the raised areas (step S13: size estimation step). Then, the weight is estimated from the size estimated by the size estimation unit 110D and the cover depth (step S14: weight estimation step). Then, the estimated weight is used to determine the risk of the falling material falling (step S15: risk determination step).

[0073] Each step will be described in detail below.

[0074] <Three-dimensional measurement data acquisition step and contour diagram generation step> First, the three-dimensional measurement data acquisition step and the contour diagram generation step will be described. The three-dimensional measurement data acquisition step is performed by three-dimensional measurement data acquisition unit 110A, and the contour diagram generation step is performed by contour diagram generation unit 110B.

[0075] 10 to 12 are diagrams illustrating three-dimensional measurement data and contour diagrams. FIGS. 10 to 12 show a model diagram 204 and a contour diagram 206 for cases where the state changes from a normal state ((A) described in FIG. 1), to floating ((C) or (D) described in FIG. 1), to the occurrence of spalling ((E) described in FIG. 1). The model diagram 204 schematically illustrates the reinforcing steel 202 and concrete 200, and although not shown, the surface 200a of the concrete 200 is on the XY plane. The contour diagram 206 is a diagram showing the surface displacement of the surface 200a on the XY plane of the model diagram 204, and illustrates the rise from the reference plane as described in FIG. 1.

[0076] FIG. 10 is a diagram illustrating a specific example 1 of the surface displacement of a building.

[0077] 10A shows a model diagram 204 of a part of a building in a normal state with no floating and a contour diagram 206. As shown in the corresponding contour diagram 206, in the normal state, no protrusion of the surface 200a from the reference surface is detected.

[0078] 10(B) shows a model diagram 204 and a contour diagram 206 of a part of a building where a lift has occurred on the surface 200a. When a lift occurs on the surface 200a, the contour diagram 206 shows an area (raised area) that is raised above the reference surface. Specifically, the contour diagram 206 shows raised area 210a, raised area 210b, and raised area 210c in descending order of surface displacement from the reference surface. The contour diagram 206 also shows a crack 200b that has occurred on the surface 200a.

[0079] Fig. 10(C) shows a model diagram 204 illustrating the occurrence of spalling. In the case shown in Fig. 10(C), spalling 212 is generated due to the lifting shown in Fig. 10(B). As the lifting occurs and grows, some of the concrete between the reinforcing bar 202 and the surface 200a falls as spalling 212.

[0080] FIG. 11 is a diagram illustrating a second specific example of the surface displacement of a building.

[0081] 11A shows a model diagram 204 of a part of a building in a normal state with no floating and a contour diagram 206. As shown in the contour diagram 206, in the normal state, no protrusion of the surface 200a from the reference surface is detected.

[0082] 11(B) shows a model diagram 204 and a contour diagram 206 of a part of a building where a lift has occurred on the surface 200a. When a lift occurs on the surface 200a, the contour diagram 206 shows an area (raised area 210a) that is raised above the reference surface. The contour diagram 206 also shows a crack 200b that has occurred on the surface 200a.

[0083] Fig. 11(C) shows a model diagram 204 illustrating the occurrence of spalling. In the case shown in Fig. 11(C), spalling 212 is generated due to the lifting shown in Fig. 11(B). As the lifting occurs and grows, part of the concrete between the reinforcing bar 202 and the surface 200a falls as spalling 212.

[0084] FIG. 12 is a diagram illustrating a specific example 3 of the surface displacement of a building.

[0085] 12A shows a model diagram 204 of a part of a building in a normal state with no floating, and a contour diagram 206. As shown in the contour diagram 206, in the normal state, no protrusion of the surface 200a from the reference surface is detected.

[0086] 12(B) shows a model diagram 204 and a contour diagram 206 of a part of a building where a lift has occurred on the surface 200a. When a lift occurs on the surface 200a, the contour diagram 206 shows an area (raised area) that is raised above the reference surface. Specifically, the contour diagram 206 shows raised area 210a, raised area 210b, and raised area 210c in descending order of surface displacement from the reference surface. The contour diagram 206 also shows a crack 200b that has occurred on the surface 200a.

[0087] Fig. 12(C) shows a model diagram 204 illustrating the occurrence of spalling. In the case shown in Fig. 12(C), spalling 212 is generated due to the lifting shown in Fig. 12(B). As the lifting occurs and grows, some of the concrete between the reinforcing bar 202 and the surface 200a falls as spalling 212.

[0088] As described above, the three-dimensional measurement data acquisition unit 110A acquires three-dimensional measurement data from the three-dimensional measurement device 10, and the contour diagram generation unit 110B generates a contour diagram based on the acquired three-dimensional measurement data. The contour diagram 206 generated by the contour diagram generation unit 110B allows for more accurate detection of raised areas. While the above description has been given of the case where the contour diagram generation unit 110B generates a contour diagram, the application of the present invention is not limited to this. In the present invention, the three-dimensional measurement data acquisition unit 110A may also detect raised areas directly from the three-dimensional measurement data.

[0089] <Detection Step, Size Estimation Step, and Weight Estimation Step> Next, the detection step, size estimation step, and weight estimation step will be described. The detection step is performed by raised region detection unit 110C, the size estimation step is performed by size estimation unit 110D, and the weight estimation step is performed by weight estimation unit 110E.

[0090] 13 and 14 are diagrams illustrating the detection step and the size estimation step.

[0091] FIG. 13 shows a model diagram and a contour diagram when floating occurs.

[0092] The raised area detection unit 110C detects areas where a raised area of ​​a predetermined threshold value (for example, 0.5 mm) or more is detected from the contour map 206 of the surface displacement of the float as raised areas (raised areas 201a to 201d).

[0093] FIG. 14 is a diagram showing surface displacement V in the X-axis direction. In FIG. 14, the horizontal axis represents distance (position) in the X-axis direction, and the vertical axis represents surface displacement V of surface 200a. The dotted line in the figure indicates a surface displacement threshold Th1. Width 1 is acquired according to the position (X1 and X2 in the figure) where the surface displacement V exceeds threshold Th1. The raised region detection unit 110C then detects the width of a region having a raised area equal to or greater than the threshold on the X-axis and Y-axis. In this manner, the raised region detection unit 110C acquires width 1 in the X-axis direction and width 2 in the Y-axis direction of the raised region. The size estimation unit 110D then calculates and estimates the size (area) of the raised region by acquiring the product of width 1 and width 2. Note that raised regions come in a variety of shapes, and an area calculation method suited to each shape may be employed.

[0094] The weight estimation unit 110E estimates the weight from the estimated size and the cover thickness from the surface 200a of the building to the reinforcing bars 202 (or steel material (not shown)).

[0095] In the case shown in Fig. 13, the weight estimation unit 110E can calculate the weight using the following formula (1): Weight of fallen material = Density of concrete x Width 1 x Cover thickness x 1 / 2 x Width 2 (Formula 1)

[0096] The density of concrete can be obtained based on a concrete recipe described in design drawings, etc. Alternatively, the density of concrete may be obtained by collecting samples at the actual site and measuring them.

[0097] The cover thickness can be obtained from design drawings. Alternatively, the cover thickness can be obtained by actually chipping the surface of the concrete to measure the distance to the reinforcing bars (or steel materials), or by using an electromagnetic wave radar method or an electromagnetic induction method.

[0098] As described above, the raised area detection unit 110C detects raised areas that are a sign of falling material based on surface displacement information (e.g., a contour diagram), estimates the size from the raised areas, and estimates the weight of the falling material from the size and cover thickness. This makes it possible to accurately estimate the weight of the expected falling material that has not yet fallen.

[0099] <Danger Determination Step> Next, the danger determination step will be described. The danger determination step is performed by the danger determination unit 110F. The danger determination unit 110F determines the danger based on the estimated weight of the fallen object.

[0100] For example, the risk determination unit 110F can quantitatively determine the risk using the impact load F calculated by the following equation (2) based on the estimated weight of the fallen object.

[0101]

[0102] Here, M is the "estimated weight of the fallen object," Δt is the "impact time," and H is the "fall height," and known values ​​are input for Δt and H.

[0103] Note that this impact load F is the average of the impact loads, and generally, as shown in Figure 15, the impact changes over time, and the maximum impact load corresponding to the risk level is calculated by calculating Fmax using Gaussian fitting or the like. Regarding risk assessment using the above formula (2), the following reference is also referred to: "Experimental Study on Risk Assessment in the Event of Collision with Spalled Concrete" [online] [searched August 29, 2023] (Internet: http: / / library.jsce.or.jp / jsce / open / 00035 / 2015 / 70-05 / 70-05-0415.pdf) from the 70th Annual Academic Conference of the Japan Society of Civil Engineers (September 2015).

[0104] For example, the risk assessment unit 110F may use, as the risk level, the HIC (head injury criteria) calculated using the following formula (3), which is widely known for assessing the possibility of head injury in automobile dummy experiments.

[0105] Here, a is "acceleration calculated based on the weight of the falling object (unit: gravitational acceleration g)", t1 is "any time of the pulse", and t2 is "the time when HIC is maximized when t1 is determined."

[0106] Regarding the assessment of risk using the above formula (3), the following literature can also be referred to: Abstracts of Academic Lectures at the Architectural Institute of Japan Annual Meeting (Hokuriku), September 2010, "Basic Research on Safety Assessment of Ceiling Materials Using Human Body Resistance Indices, Part 3: Ceiling Material Fall Experiment 2" [online] [Retrieved August 29, 2023] (Internet: http: / / space.iis.u-tokyo.ac.jp / main / report / 20441.pdf).

[0107] As described above, the risk determination unit 110F can determine the risk based on the predicted weight of the falling object.

[0108] Second Embodiment Next, a second embodiment of the present invention will be described. In this embodiment, crack information is acquired, and the crack information is reflected in a contour diagram to correct the estimated size (area) of the flaking object.

[0109] 16 is a block diagram showing the functions realized by the processor 110 in this embodiment. Note that the same reference numerals are used to denote parts that have already been described, and the description thereof will be omitted.

[0110] The processor 110 realizes a three-dimensional measurement data acquisition unit 110A, a contour diagram generation unit 110B, a crack information acquisition unit 110G, a raised area detection unit 110C, a size estimation unit 110D, a weight estimation unit 110E, and a risk determination unit 110F.

[0111] For example, in places where a large amount of lifting has occurred or where a large amount of bulging that is not lifting has occurred due to poor construction, it may be difficult to identify the lifting-caused bulging area using only a contour map. In such cases, crack information is acquired and superimposed on the contour map to identify the location of the crack, and the size of the bulging area is corrected based on the crack location. The crack information acquisition unit 110G acquires an image of the surface of the building as crack information and detects cracks based on the acquired image.

[0112] FIG. 17 is a diagram illustrating a case where a raised area is corrected based on crack information.

[0113] FIG. 17(A) shows a contour diagram 230 before correction and a diagram 234 showing the surface displacement in the X-axis direction, and FIG. 17(B) shows a contour diagram 232 after correction and a diagram 236 showing the surface displacement in the X-axis direction.

[0114] Before correction based on crack information is performed, the detected raised areas are scattered in various locations, as shown in the contour diagram 230, making it impossible to identify raised areas based on the lift. When raised areas are detected based on surface displacement, as shown in FIG. 17, the detected raised areas are scattered. On the other hand, after correction based on crack information is performed, raised areas corresponding to the lift can be identified from the crack information, as shown in the contour diagram 232. Specifically, as shown in FIGS. 234 and 236, which show the surface displacement in the X-axis direction, the width of the raised areas is corrected to be smaller depending on the location of the cracks based on the crack information. For example, this correction determines the boundary of the raised area at the point where the surface displacement changes on either side of the crack, and identifies the width of the raised area.

[0115] As described above, the crack information acquisition unit 110G acquires, for example, an image of the surface of a building as crack information and detects cracks from that image. Then, the crack information is superimposed on a contour diagram to correct the size estimated by the size estimation unit 110D. This allows the present embodiment to accurately estimate the size of the debris.

[0116] <Third Embodiment> Next, a third embodiment of the present invention will be described. In this embodiment, information on changes in the float over time is acquired, and the size is updated based on the information on changes in the float over time. Here, updating the size means estimating how the size will change after a predetermined period of time based on the information on changes in the float over time.

[0117] 18 is a block diagram showing the functions realized by the processor 110 in this embodiment. Note that parts that have already been explained are given the same reference numerals and explanations thereof will be omitted.

[0118] The processor 110 implements a three-dimensional measurement data acquisition unit 110A, a contour diagram generation unit 110B, a raised area detection unit 110C, a size estimation unit 110D, a weight estimation unit 110E, a risk determination unit 110F, and an update unit 110H.

[0119] The update unit 110H acquires information on the change over time of the float, and updates the size and weight based on the information on the change over time of the float.

[0120] FIG. 19 is a diagram showing an example of information on the change in float over time.

[0121] Reference numeral 240 denotes a graph in which the vertical axis indicates surface displacement and the horizontal axis indicates elapsed time (t1 to t5). Reference numeral 242 denotes a schematic diagram of raised areas (S1 to S4; S1 = 0) for each elapsed time (t1 to t5). Reference numeral 244 denotes a graph in which the vertical axis indicates the area of ​​the raised areas (corresponding to S1 to S4 of reference numeral 242) and the horizontal axis indicates elapsed time. Note that while the example shown in FIG. 19 illustrates information on changes in float over time indicated by reference numerals 240, 242, and 244, this is not limiting. For example, the update unit 110H can acquire information on changes in float over time for one or more floats, and can also acquire information on changes in float over time of a different type than the information on changes in float over time shown in FIG. 19.

[0122] The updating unit 110H acquires information on the change in the float over time and updates the size of the previously estimated debris. Specifically, the updating unit 110H acquires the results of periodic inspections as information on the change in the float over time, as shown in FIG. 19 . The updating unit 110H can then predict the size of the raised area at t4 and / or t5 by analyzing the change over time between t1 and t3, for example, and estimate the size of the debris. For example, if the area of ​​the raised area at t3 is estimated to be S3, the updating unit 110H acquires the information on the change in the float over time shown in FIG. 19 and estimates the raised area at t4 to be S4, updating the estimated size.

[0123] As a result, this embodiment can accurately determine the risk of falling off material that changes over time.

[0124] <Others> The structure in this embodiment is a tunnel, but is not limited to this and may be any structure that is inspected, such as a bridge or a dam. The surface material of the structure includes reinforced concrete, concrete, and concrete repair materials such as mortar.

[0125] In the above embodiment, the hardware structure of the processing unit that executes various processes is the following various processors: The various processors include a CPU (Central Processing Unit), which is a general-purpose processor that executes software (programs) and functions as various processing units, a programmable logic device (PLD), such as an FPGA (Field Programmable Gate Array), whose circuit configuration can be changed after manufacture, and a dedicated electrical circuit, such as an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing specific processes.

[0126] A single processing unit may be configured with one of these various processors, or may be configured with two or more processors of the same or different types (e.g., multiple FPGAs, or a combination of a CPU and an FPGA). Multiple processing units may also be configured with a single processor. Examples of multiple processing units configured with a single processor include: a first configuration, as typified by client or server computers, in which a single processor is configured with a combination of one or more CPUs and software, and this processor functions as multiple processing units; and a second configuration, as typified by system-on-chip (SoC), in which a processor is used to realize the functions of an entire system including multiple processing units on a single IC (Integrated Circuit) chip. In this way, the various processing units are configured with one or more of the above-mentioned various processors as a hardware structure.

[0127] Furthermore, the hardware structure of these various processors is, more specifically, an electric circuit made up of a combination of circuit elements such as semiconductor elements.

[0128] The above-described configurations and functions can be realized by any hardware, software, or a combination of both. For example, the present invention can be applied to a program that causes a computer to execute the above-described processing steps (processing procedures), a computer-readable recording medium (non-transitory recording medium) on which such a program is recorded, or a computer on which such a program can be installed.

[0129] Although examples of the present invention have been described above, it goes without saying that the present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the present invention.

[0130] 10: Three-dimensional measuring device 100: Determination device 110: Processor 110A: Three-dimensional measurement data acquisition unit 110B: Contour diagram generation unit 110C: Protuberance area detection unit 110D: Size estimation unit 110E: Weight estimation unit 110F: Risk determination unit 110G: Information acquisition unit 110H: Update unit 120: Memory 130: Display unit 140: Input / output interface 150: Operation unit

Claims

1. A judgment device that judges the predicted risk of falling material, comprising a processor and a memory that stores a program to be executed by the processor, wherein the processor: acquires three-dimensional measurement data of the surface of a structure measured by a measuring device; detects raised areas that are a precursor to the falling material based on the three-dimensional measurement data; estimates the size of the falling material based on the raised areas; estimates the weight of the falling material from the size and the cover thickness from the surface of the structure to the reinforcing bars or steel materials; and judges the risk based on the weight.

2. The determination device according to claim 1, wherein the processor acquires surface displacement information from the three-dimensional measurement data and detects the raised area based on the surface displacement information.

3. The determination device according to claim 1, wherein the processor acquires surface displacement information from the three-dimensional measurement data, creates a contour diagram of the surface based on the surface displacement information, detects protrusions on the surface based on the contour diagram, and estimates the size based on the protrusions.

4. The determination device according to claim 3, wherein the processor acquires crack information on the surface and corrects the size based on the crack information and the contour diagram.

5. The determination device according to claim 4, wherein the processor acquires an image of the surface and acquires the crack information from the image.

6. The determination device according to claim 1, wherein the processor corrects the determined degree of danger based on the size.

7. The determination device according to claim 1, wherein the processor acquires information on a change in the float over time, and updates the size based on the information on the change in the float over time.

8. The determination device according to claim 1, wherein the measurement device includes a LiDAR or a stereo camera.

9. The determination device according to claim 8, wherein the LiDAR measures the three-dimensional measurement data using a Frequency Modulated Continuous Wave (FMCW) method.

10. The determination device according to claim 1, wherein the material of the surface of the structure includes concrete or a concrete repair material.

11. A method of assessment using a assessment device having a processor and a memory for storing a program to be executed by the processor, for assessing the predicted risk of falling material, comprising the steps of: acquiring three-dimensional measurement data of the surface of a structure measured by a measuring device, detecting a raised area that is a precursor of the falling material based on the three-dimensional measurement data, estimating the size of the falling material based on the raised area, estimating the weight of the falling material from the size and the cover thickness from the surface of the structure to the reinforcing bars or steel material, and assessing the risk based on the weight, performed by the processor.

12. A program for executing a method for determining the predicted risk of falling material using a determination device having a processor and a memory for storing a program to be executed by the processor, the program causing the processor to execute the following steps: acquiring three-dimensional measurement data of the surface of a structure measured by a measuring device; detecting raised areas that are a precursor to the falling material based on the three-dimensional measurement data; estimating the size of the falling material based on the raised areas; estimating the weight of the falling material from the size and the cover thickness from the surface of the structure to the reinforcing bars or steel materials; and determining the risk based on the weight.

13. A non-transitory computer-readable recording medium having the program according to claim 12 recorded thereon.

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