Stripping prediction device, method, and program

Abstract

Provided are a peeling prediction device, method, and program with which peeling of a material on the surface of a building can be predicted with high accuracy. A processor of the peeling prediction device performs: a process for detecting the amount of protrusion of the surface at one or more points of interest on the surface on the basis of a plurality of pieces of three-dimensional measurement data measured each time the building is overhauled, i.e., three-dimensional measurement data in which the three-dimensional shape of the surface of the building is measured; and predicting a future upheaval amount of the point of interest based on the elapsed time period of the maintenance and the upheaval amount of each maintenance. Furthermore, a first curve indicating a change over time in the amount of heave of the point of interest and a second curve indicating a change over time in the predicted amount of heave are created, and the created first curve and second curve are output to a display.

Description

Technical Field

[0001] The present invention relates to a spalling prediction device, method and program, and more particularly to a technology for predicting spalling of a material (such as concrete) on a building surface. Background Art

[0002] Conventionally, in order to grasp the floating of concrete or the internal cavity that causes the spalling of concrete pieces, the techniques described in Patent Documents 1 to 3 have been proposed.

[0003] The spalling prediction and diagnosis method described in Patent Document 1 uses an infrared camera to capture an infrared thermal image of the concrete building surface while simultaneously measuring the ambient air temperature near the surface. Based on the infrared thermal image and ambient air temperature, the method calculates the temperature difference between the intact and the spalled area (the spalling area temperature difference) and the measured temperature environment (the difference between the surface temperature of the intact area and the ambient air temperature). The method then calculates a temperature environment coefficient as the ratio of the calculated spalling area temperature difference to the calculated measured temperature environment. Based on the temperature environment coefficient, the risk of spalling of the covering concrete (the area from the reinforcement surface to the concrete surface) is quantitatively evaluated. Furthermore, the spalling risk calculated previously is compared with the current spalling risk to predict the spalling time.

[0004] In the inspection method described in Patent Document 2, an inspection object is struck by an inspection hammer device, and the state of the inspection object is determined based on time history data of the sound pressure generated by the impact.

[0005] In the non-destructive inspection method for concrete structures described in Patent Document 3, an ultrasonic transmitter and receiver are brought into contact with a partially or completely submerged portion of a concrete structure, transverse ultrasonic waves are emitted from the transmitter into the concrete structure, and the resonant vibration of the concrete structure is detected by the receiver. Damage to the back and / or internal damage of the concrete structure is determined based on the waveform detected by the receiver.

[0006] Previous technical literature

[0007] Patent Literature

[0008] Patent Document 1: Japanese Patent Application Laid-Open No. 2016-006398

[0009] Patent Document 2: Japanese Patent Application Laid-Open No. 2020-098098

[0010] Patent Document 3: Japanese Patent Application Laid-Open No. 2006-105680 Summary of the Invention

[0011] Technical issues to be solved by the invention

[0012] The spalling prediction and diagnosis method described in Patent Document 1 uses thermal images of concrete buildings captured with an infrared camera, making it difficult to accurately predict spalling. For example, depending on various environmental conditions, such as whether sunlight reaches the building, the intensity of sunlight, and the outside air temperature, the temperature of the spalled area can be higher or lower than that of the intact area. Furthermore, when capturing thermal images with an infrared camera, it is difficult to capture the building under the same environmental conditions both previously and currently. This makes it difficult to accurately predict spalling by comparing the spalling risk calculated previously with the current one.

[0013] The inspection method described in Patent Document 2 suffers from the time-consuming problem of determining the health of a large object using only tapping sounds. Furthermore, the non-destructive inspection method for concrete structures described in Patent Document 3 requires contacting an ultrasonic transmitter and receiver with a portion or the entirety of a concrete structure submerged in water. Furthermore, neither Patent Document 2 nor Patent Document 3 describes any method for predicting the detachment of material from the structure's surface.

[0014] One embodiment of the technology according to the present invention provides a spalling prediction device, method, and program capable of predicting spalling of a material on a building surface with high accuracy.

[0015] Means for solving technical problems

[0016] The invention involved in the first embodiment is a spalling prediction device, which comprises: a processor; and a memory storing a program for the processor to execute, wherein the processor performs the following processing: detecting the surface uplift amount at one or more points of interest on the surface based on three-dimensional measurement data for measuring the three-dimensional shape of the surface of a building, that is, multiple three-dimensional measurement data measured each time the building is inspected; predicting the future uplift amount of the point of interest based on the inspection time period and the uplift amount of each inspection; creating a first curve representing the change of the uplift amount of the point of interest over time and a second curve representing the change of the predicted uplift amount over time; and outputting the created first curve and second curve.

[0017] According to the first method of the present invention, the surface bulge at one or more points of interest on the surface is detected based on a plurality of three-dimensional measurement data measured each time a building is inspected, and the future bulge of the point of interest is predicted based on the duration of the inspection and the bulge of each inspection. In addition, a first curve representing the change in the bulge of the point of interest over time and a second curve representing the change in the predicted bulge over time are produced, and the produced first and second curves are output. The user (maintenance worker) can grasp the bulge (floating amount) that changes over time from the first and second curves, and can also predict the future period of peeling of the material on the surface of the building. Therefore, it is possible to take measures such as giving priority to repairing the parts that peel off earlier.

[0018] In the peeling prediction device involved in the second embodiment of the present invention, in the first embodiment, the processor performs the following processing: detecting the floating amount of the focus point based on the difference between the lifting amount at the start time of maintenance at the focus point and the lifting amount during maintenance after the start time; and predicting the future floating amount of the focus point based on the maintenance period and the lifting amount of each maintenance after the start time, the first curve is a curve representing the change of the floating amount of the focus point over time, and the second curve is a curve representing the change of the predicted floating amount over time.

[0019] According to the second aspect of the present invention, the actual temporal change in the amount of uplift at a point of interest on the surface of a building and the predicted temporal change in the amount of uplift at a point of interest can be understood using the first and second curves, respectively. This makes it easy to distinguish between areas that have been uplifted since construction and areas that have been uplifted later due to rusting of steel bars, etc.

[0020] In the spalling prediction device according to the third aspect of the present invention, in the first aspect or the second aspect, it is preferable that the first curve and the second curve are continuous curves created using different line types.

[0021] In the spalling prediction device according to the fourth aspect of the present invention, in the second aspect, the processor preferably performs the following processing: creating a spalling risk line or spalling risk area based on a set spalling risk threshold; and synthesizing the spalling risk line or spalling risk area into the first and second curves. This allows the user to identify the time when the second curve exceeds the spalling risk line or enters the spalling risk area as a future spalling period.

[0022] In the spalling prediction device according to the fifth aspect of the present invention, in the second aspect, the processor preferably performs the following processing: comparing the second curve with a set spalling risk threshold, predicting a period when the second curve exceeds the spalling risk threshold as a spalling period; and notifying the spalling period.

[0023] In the spalling prediction device according to the sixth aspect of the present invention, in the fourth or fifth aspect, the processor preferably receives a spalling risk threshold value or automatically predicts a spalling risk threshold value through user input, and uses the received spalling risk threshold value or the predicted spalling risk threshold value as the set spalling risk threshold value. Sometimes, the user may know the amount of floating during spalling based on experience, and a spalling risk threshold value consistent with the user's experience may be set, or an automatically optimized spalling risk threshold value may be set.

[0024] In the second mode of the spalling prediction device involved in the seventh mode of the present invention, the processor preferably performs the following processing: creating a surface property image that visualizes the size of the floating amount of the surface based on the floating amount of the building surface at the time of inspection; displaying the surface property image on the display; and if any position on the surface property image displayed on the display is accepted as a focus point through user input, displaying the first curve and the second curve created corresponding to the accepted focus point on the display.

[0025] According to the seventh method of the present invention, the user can easily indicate the point of interest of concern, and the first curve and the second curve corresponding to the point of interest indicated by the user are displayed on the display, thereby allowing the user to understand the change in the floating amount of the point of interest over time and the period of future peeling, etc.

[0026] In the spalling prediction device according to the eighth aspect of the present invention in the seventh aspect, it is preferable that the surface property image is an image having regions whose brightness or color varies depending on the amount of floating, or a contour map corresponding to the amount of floating.

[0027] In the peeling prediction device according to the ninth aspect of the present invention, in the first aspect, the three-dimensional measurement data can be data measured by LiDAR or a stereo camera.

[0028] In the spalling prediction device according to the tenth aspect of the present invention, in the first aspect, the three-dimensional measurement data is preferably data measured by FMCW (Frequency Modulated Continuous Wave) LiDAR, thereby enabling detection of a floating amount that cannot be visually observed.

[0029] In the spalling prediction device according to the eleventh aspect of the present invention, in the first aspect, the plurality of three-dimensional measurement data are preferably aligned so that the plurality of three-dimensional measurement data at the same location on the building surface where the amount of uplift has not changed are consistent. This is to align the plurality of three-dimensional measurement data measured during each inspection so as to accurately detect locations where the amount of uplift has changed.

[0030] In the spalling prediction device according to the twelfth aspect of the present invention, in the first aspect, it is preferable that the material of the surface of the building includes concrete or a concrete repair material.

[0031] The invention involved in the 13th embodiment is a spalling prediction method for predicting spalling of the surface of a building, wherein a processor executes the following steps respectively: detecting the surface uplift amount at one or more focus points on the surface based on three-dimensional measurement data for measuring the three-dimensional shape of the surface of the building, i.e., multiple three-dimensional measurement data measured each time the building is inspected; predicting the future uplift amount of the focus point based on the inspection time period and the uplift amount of each inspection; creating a first curve representing the change of the uplift amount of the focus point over time and a second curve representing the change of the predicted uplift amount over time; and outputting the created first curve and second curve.

[0032] In the 13th mode, the peeling prediction method involved in the 14th mode of the present invention is preferably performed by the processor respectively performing the following steps: detecting the floating amount of the focus point based on the difference between the lifting amount at the start time of maintenance at the focus point and the lifting amount during maintenance after the start time; and predicting the future floating amount of the focus point based on the maintenance period and the lifting amount of each maintenance after the start time, the first curve is a curve representing the change of the floating amount of the focus point over time, and the second curve is a curve representing the change of the predicted floating amount over time.

[0033] In the 14th mode, the spalling prediction method involved in the 15th mode of the present invention is preferably performed by the processor respectively performing the following steps: creating a spalling danger line or a spalling danger area according to the set spalling danger threshold; and synthesizing the spalling danger line or the spalling danger area into the first curve and the second curve.

[0034] In the 14th mode, the spalling prediction method according to the 16th mode of the present invention is preferably configured such that the processor performs the following steps respectively: comparing the second curve with the set spalling risk threshold, and predicting the period when the second curve exceeds the spalling risk threshold as the spalling period; and notifying the spalling period.

[0035] The invention involved in the 17th embodiment is a spalling prediction program, which enables a computer to perform the following functions respectively: detecting the surface uplift amount at one or more focus points on the surface based on three-dimensional measurement data for measuring the three-dimensional shape of the surface of a building, that is, multiple three-dimensional measurement data measured each time the building is inspected; predicting the future uplift amount of the focus point based on the inspection time period and the uplift amount of each inspection; creating a first curve representing the change of the uplift amount of the focus point over time and a second curve representing the change of the predicted uplift amount over time; and outputting the created first curve and second curve.

[0036] Effects of the Invention

[0037] According to the present invention, it is possible to predict with high accuracy whether material will peel off from a building surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a diagram showing an example of a curve indicating the relationship between the elapse of time after construction of a building and the surface displacement of the building, and a cross section of the building at each time of maintenance.

[0039] Figure 2 This is a schematic diagram of a building maintenance system including the spalling prediction device according to the present invention.

[0040] Figure 3 This is an external view of an FMCW LiDAR including one embodiment of a three-dimensional measurement device.

[0041] Figure 4 This is a diagram showing an embodiment of measuring the three-dimensional shape of a building surface using a stereo camera.

[0042] Figure 5 This is a cross-sectional view near the surface of a building, illustrating an example of a mechanism of surface peeling of a building.

[0043] Figure 6 This is a cross-sectional view near the surface of a building, showing another example of the mechanism of surface peeling of a building.

[0044] Figure 7 This is a block diagram showing an embodiment of the hardware configuration of the spalling prediction device according to the present invention.

[0045] Figure 8 This figure shows a method for determining the bulge and rise of a building surface.

[0046] Figure 9 This is a diagram showing an example of a surface texture image displayed on a display.

[0047] Figure 10 These are the first and second curves showing the amount of uplift of the surface of a point of interest of a building that changes over time.

[0048] Figure 11 These are the first and second curves showing the amount of uplift that changes over time on the surface of a point of interest of a building.

[0049] Figure 12 These are the first and second curves showing the rate of change of the lift amount of the surface of the point of interest of the building over time.

[0050] Figure 13 This is a flowchart showing an embodiment of the spalling prediction method according to the present invention. DETAILED DESCRIPTION

[0051] Hereinafter, preferred embodiments of the spalling prediction device, method, and program according to the present invention will be described with reference to the accompanying drawings.

[0052] [Summary of the Invention]

[0053] Figure 1 This is a diagram showing an example of a curve indicating the relationship between the elapse of time after construction of a building and the surface displacement of the building, and a cross section of the building at each time of maintenance.

[0054] exist Figure 1 In this system, displacement of the building surface is measured at the start of maintenance (t1) and at subsequent maintenance times (t2, t3, t4, t5, and so on). By comparing displacements at the same location on the building surface, it is possible to detect areas of surface bulge that have occurred over time since the start of construction.

[0055] exist Figure 1 In the example shown, the building was in a normal state (A) at measurement start time t1. However, at maintenance time t2, the building's surface was slightly bulging due to deterioration ((B) cracking). The bulging during this period was too severe to be visually detected. Furthermore, cracking is typically caused by corrosion and thickening of the steel (rebar) within the building.

[0056] In the initial stage of lifting (C) shown at the inspection time t3, as the corrosion of the reinforcing bars progresses, "cracking" also progresses, and the surface of the building rises ("lifting" occurs).

[0057] In the final stage of the lifting (D) shown at the inspection time t4, the "cracks" further progress and reach the surface of the building, and the "lifting" further increases.

[0058] Inspection time t5 indicates the time when the covering concrete (concrete from the reinforcement surface to the concrete surface) falls off ((E) peeling / flaking off).

[0059] exist Figure 1 In the example shown, it can be seen that the displacement (uplift) of the building surface measured at each inspection gradually increases, causing peeling / flaking of the covering concrete.

[0060] Building degradation phenomena include not only steel deterioration due to steel corrosion, but also concrete degradation through strength loss, cracking, and surface deterioration. All building degradation phenomena result in surface swelling, and by analyzing the temporal changes in the amount of surface swelling, it is possible to predict the timing of peeling / flaking regardless of the cause of degradation.

[0061] Furthermore, if the timing of peeling / flaking can be predicted, an appropriate repair plan can be formulated.

[0062] Therefore, the present invention detects the surface uplift at more than one point of interest on the surface based on multiple three-dimensional measurement data measured each time the building is inspected, predicts the future uplift of the point of interest based on the inspection time period and the uplift amount of each inspection, creates a first curve representing the change of the uplift of the point of interest over time and a second curve representing the change of the predicted uplift over time, and outputs the created first and second curves.

[0063] [Overview of the maintenance system structure]

[0064] Figure 2 This is a schematic diagram of a building maintenance system including the spalling prediction device according to the present invention.

[0065] Figure 2 The shown maintenance system is a system for inspecting a railway tunnel, and includes a three-dimensional measuring device 10 , a data processing device 14 , and a power supply device 16 .

[0066] 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 line.

[0067] In this example, the three-dimensional measurement device 10 is a LiDAR (Light Detection And Ranging), specifically a FMCW (Frequency Modulated Continuous Wave) LiDAR capable of measuring distances at the order of several hundred μm. However, the present invention is not limited to the use of ranging data (three-dimensional measurement data) measured by an FMCW LiDAR.

[0068] [Three-dimensional measurement device]

[0069] Figure 3 This is an external view of an FMCW LiDAR including one embodiment of a three-dimensional measurement device.

[0070] exist Figure 3 In, such as Figure 2 As shown, the three-dimensional measuring device 10 is mounted on a carriage 18 that travels on a railway line, and measures the distance to the surface of a tunnel that is a railway structure.

[0071] In addition to the three-dimensional measuring device 10 , the data processing device 14 and the power supply device 16 are mounted on the carriage 18 . The power supply device 16 supplies power to the three-dimensional measuring device 10 and the data processing device 14 .

[0072] The three-dimensional measurement device 10 measures the distance to the wall (surface) 20 of the tunnel, thereby acquiring three-dimensional measurement data indicating the shape of the wall 20 of the tunnel.

[0073] exist Figure 3 In the example shown, the three-dimensional measuring device 10 is Figure 3 The FMCW laser beam is scanned at high speed in the left-right direction (main scanning direction) of the wall surface 20 shown, while the scanning line is moved in the up-down direction (sub-scanning direction) of the wall surface 20. This method measures the distance from the measuring head of the three-dimensional measurement device 10 to multiple measurement points on each scanning line of the laser beam. Furthermore, by converting the three-dimensional data in the polar coordinate system, which is composed of the laser beam irradiation direction and the measured distance, into three-dimensional data in the orthogonal coordinate system, three-dimensional measurement data representing the shape of the wall surface 20 is obtained. In this example, three-dimensional measurement data (point cloud data) of multiple measurement points is obtained as the three-dimensional measurement data.

[0074] The three-dimensional measuring device 10 can measure the concavo-convex shape of the minute wall surface 20 under the following conditions.

[0075] Measurement accuracy: 50μm

[0076] Measuring distance: 2 to 7 meters

[0077] Measurement speed: 10m in area 2 / second (the speed of the laser beam is equivalent to 4000rpm)

[0078] Furthermore, the three-dimensional measurement device 10 acquires three-dimensional data of the wall surface 20 at predetermined intervals while the carriage 18 moves. Preferably, the three-dimensional data acquired at each interval is acquired so that the measurement areas partially overlap. This is to enable panoramic synthesis of the three-dimensional data acquired at each interval.

[0079] The three-dimensional measurement device 10 can achieve the above-mentioned measurement accuracy by being set to a LiDAR of FMCW mode, but the measurement accuracy and other conditions of the three-dimensional measurement data required in the present invention are not limited to the above example, and the three-dimensional measurement device is not limited to the LiDAR of FMCW mode, and various devices can be applied.

[0080] For example, instead of FMCW LiDAR, TOF (Time of Flight) LiDAR can be used, which measures the flight time of pulsed light to measure the distance to the wall 20. Furthermore, the three-dimensional shape of the wall 20 can be measured using a stereo camera.

[0081] Figure 4 This is a diagram showing an embodiment of measuring the three-dimensional shape of a building surface using a stereo camera.

[0082] Figure 4The stereo camera shown is composed of a left camera 30L and a right camera 30R, and measures the distance to the wall surface 20 of the imaging target by triangulation.

[0083] In addition, as a three-dimensional measurement device for measuring the distance to the wall 20 (i.e., three-dimensional measurement data of the wall), various three-dimensional measurement devices can be applied, such as the laser radar three-dimensional shape measurement device described in Japanese Patent Gazette No. 9-297014, the photographic device described in Japanese Patent Gazette No. 2021-2016-31249, and the measurement device based on the light cutting method using a slit laser projector.

[0084] The three-dimensional shape of the tunnel wall 20 is measured by the three-dimensional measuring device 10 at the start of tunnel surveying (construction) and during periodic maintenance 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 surveying and during periodic maintenance.

[0085] [Mechanism of peeling of building surface materials]

[0086] Figure 5 This is a cross-sectional view near the surface of a building, illustrating an example of a mechanism of surface peeling of a building.

[0087] Figure 5 The "(A) normal state" of the present invention refers to a normal state such as during construction of a building. The surface in this state is set as the reference surface. Figure 5 Among them, 40 is steel (rebar).

[0088] Figure 5 The causes of "(B) cracking", "(C) initial stage of floating / steel fracture", "(D) final stage of floating" and "(C) peeling" are corrosion of the steel bars 40 (e.g., salt damage / water leakage), etc., which occur over the years from the construction of the tunnel.

[0089] exist Figure 5 After the "(C) initial stage of floating / steel fracture", the surface of the building gradually rose above the reference level (generating "floating"), causing the overlying concrete to peel off.

[0090] Figure 6 This is a cross-sectional view near the surface of a building, showing another example of the mechanism of surface peeling of a building.

[0091] Figure 6 The "(A) normal state" of the present invention refers to a normal state such as during construction of a building. The surface in this state is set as the reference surface. Figure 6 In the figure, 50 represents the reactive skeleton and 60 represents the steel.

[0092] Figure 5 The causes of "(B) Cracking", "(C) Initial stage of floating / steel fracture", "(D) Final stage of floating" and "(C) Peeling" are due to the deterioration of the strength of the concrete (for example, alkali reaction of the reactive skeleton 50), etc., which occur as the years pass from the time of construction.

[0093] exist Figure 6 After the "(C) initial stage of floating / steel fracture", the surface of the building gradually rises above the reference plane (producing "floating"), causing peeling of the covering concrete from the surface of the steel 60 to the surface.

[0094] like Figure 5 and Figure 6 As shown, if a building's surface "lifts" over time, it will eventually "peel off." This applies regardless of the cause of the lifting. This applies regardless of the presence, material, or shape of steel bars, the concrete's material and shape, how the steel bars are incorporated into the concrete, construction methods, or corrosion (neutralization, freezing damage, poor construction, etc.).

[0095] [Hardware structure of the spalling prediction device]

[0096] Figure 7 This is a block diagram showing an embodiment of the hardware configuration of the spalling prediction device according to the present invention.

[0097] Figure 7 The peeling prediction device 100 shown is composed of, for example, a personal computer, a workstation, etc., and includes a processor 110, a memory 120, a display 130, an input / output interface 140, and an operation unit 150. The peeling prediction device 100 can be used as Figure 2 The data processing device 14 shown is assembled to perform one function.

[0098] Processor 110, comprised of a CPU (Central Processing Unit), centrally controls various components of spalling prediction device 100 and executes a spalling prediction program to perform various processes for predicting spalling on a building surface. Details of the various processes performed by processor 110 will be described later.

[0099] The memory 120 includes flash memory, ROM (Read-only Memory), RAM (Random Access Memory), a hard disk drive, and other devices. Flash memory, ROM, or the hard disk drive is non-volatile memory that stores an operating system and various programs, including the spalling prediction program involved in the present invention. Furthermore, the non-volatile memory (storage device) such as the flash memory and hard disk drive stores three-dimensional measurement data of the building surface measured by the three-dimensional measurement device 10 at the start of building measurement and during periodic maintenance, along with the measurement time.

[0100] The RAM functions as a work area for processing by the processor 110. It also temporarily stores various programs stored in flash memory, three-dimensional measurement data of the building surface, etc. The processor 110 may also incorporate a portion of the memory 120 (RAM).

[0101] In addition to displaying the operating screen of the spalling prediction device 100, the display 130 also displays the curves produced by the spalling prediction device 100, and displays the surface property image of the building, etc., and is also used as part of the GUI (Graphical User Interface) when receiving user input of the focus point of the surface of the building from the operating unit 150.

[0102] The input / output interface 140 includes a connection unit capable of connecting to an external device and a communication unit capable of connecting to a network. As the connection unit capable of connecting to an external device, a USB (Universal Serial Bus) or an HDMI (High-Definition Multimedia Interface) (HDMI is a registered trademark) can be used.

[0103] The spalling prediction device 100 can be configured as a device independent of the data processing device 14. In this case, the processor 110 can obtain the three-dimensional measurement data of the building surface from the data processing device 14 via the input / output interface 140. Alternatively, if the three-dimensional measurement data is stored in the cloud, the processor 110 can obtain the three-dimensional measurement data of the building surface from the cloud via the input / output interface 140. Furthermore, the processor 110 can store the three-dimensional measurement data thus obtained in the memory 120.

[0104] The operation unit 150 includes a pointing device such as a mouse, a keyboard, and the like, and functions as a part of a GUI that receives instruction inputs based on user operations using the display screen of the display 130 .

[0105] Figure 8This figure shows a method for determining the bulge and rise of a building surface.

[0106] Figure 8 (A) is a diagram showing the surface of a building and scanning lines of a laser beam scanning the surface.

[0107] The three-dimensional measuring device 10 acquires three-dimensional measurement data (point cloud data) of a plurality of measurement points on a scanning line of a laser beam.

[0108] The processor 110 calculates the Figure 5 The distance in the normal direction of the reference plane of the point cloud data of the reference plane shown in FIG.

[0109] Figure 8 (B) is a waveform diagram showing the height of the building surface obtained from the point group data on the scan line.

[0110] Figure 8 (C) means according to Figure 8 (B) is a waveform diagram of the height of the building surface obtained from point cloud data on the same scanning line measured after the start of the inspection of the building surface.

[0111] Figure 8 (D) means from Figure 8 The waveform showing the height of the surface shown in (C) is minus Figure 8 (B) is a waveform diagram showing the difference between the waveform of the surface height and the waveform of the surface height.

[0112] Figure 8 The waveform shown in (D) is Figure 8 (B) and (C) are waveform graphs showing the amount of surface change (lift) of the building surface over time between measurement times.

[0113] Therefore, in Figure 8 In (D), the portion not shown as "floating" is the portion where the surface height does not change over time. Also, the portion where the surface height does not change over time and is raised can be considered as the portion that has been raised since the construction (refer to Figure 8 (B)).

[0114] Furthermore, it is preferable that the plurality of three-dimensional measurement data of the building surface at each measurement time be adjusted so that the plurality of three-dimensional measurement data at the same position on the building surface and at a position where the amount of uplift does not change are consistent. Figure 8 As shown in (D), the difference in the amount of swelling at the position where the amount of swelling does not change, that is, the amount of floating can be set to zero (that is, the three-dimensional measurement data can be made consistent).

[0115] The processor 110 creates a surface texture image that visualizes the magnitude of the surface bulge based on three-dimensional measurement data (point cloud data) of the building surface acquired during the latest inspection, for example.

[0116] The processor 110 displays the generated surface texture image on the display 130 .

[0117] Figure 9 This is a diagram showing an example of a surface texture image displayed on a display.

[0118] Figure 9 The surface texture image shown is an image composed of a plurality of points evenly distributed on the surface of the building. Each point in the image is composed of brightness data or color data whose brightness or color varies depending on the amount of ridges at each point.

[0119] Therefore, the surface texture image displayed on the display 130 is an image composed of a plurality of points evenly distributed on the surface of the building, and each point is an image having a region (point region) whose brightness or color varies depending on the amount of protrusion at its position.

[0120] exist Figure 9 In the example, the images of the dots included in frames A1 to A4 have different brightness or color than the images of dots in other regions. This allows the user to recognize that the amount of bulge in the dots included in frames A1 to A4 is greater than that in other regions.

[0121] Here, a case will be described in which the user inputs an arbitrary position (in this example, a point position) on the surface texture image displayed on the display 130 as a focus point through the operation unit 150 .

[0122] In addition, Figure 9 In the illustrated embodiment, the surface texture image displayed on the display 130 is an image composed of a plurality of points evenly distributed on the building surface. However, the image is not limited thereto. For example, a heat map, a shading image, or a contour map corresponding to the amount of surface elevation of the building can be used. Furthermore, a surface texture image can be generated based on the amount of elevation instead of the amount of surface elevation of the building.

[0123] When the processor 110 receives an arbitrary position on the surface texture image displayed on the display 130 as a focus point through user input, it creates a curve corresponding to the received focus point.

[0124] <Curve Generation and Display>

[0125] The processor 110 detects the amount of uplift based on the three-dimensional measurement data at the position of the point of interest input received from the user, among the three-dimensional measurement data of the building measured at the start of building measurement and during periodic maintenance.

[0126] Next, processor 110 predicts the future amount of uplift at the point of interest based on the duration of the inspection and the amount of uplift at each inspection. A curve (first curve) is created showing the temporal changes in the measured uplift at the point of interest, and a curve (second curve) shows the temporal changes in the predicted uplift at the point of interest.

[0127] Figure 10 These are the first and second curves showing the amount of uplift of a point of interest of a building that has changed over time.

[0128] exist Figure 10 In the curve a1, Figure 9 When the position of any point within the upper frame A1 is input by the user as a focus point, the processor 110 plots points representing the uplift amount of the surface corresponding to the focus point at the start of measurement (year 0) and the uplift amount of the surface during periodic inspections after 2, 4, 6 and 8 years, and creates a first curve representing the change over time of the measured uplift amount of the surface of the focus point by connecting the plotted points. In addition, based on the uplift amount of the surface corresponding to the focus point at the start of measurement and the uplift amount of the surface during each inspection, the future uplift amount is predicted to create a second curve representing the change over time of the predicted uplift amount.

[0129] When the plotted points are (N+1) discrete points, the first curve can be, for example, an Nth-order spline curve smoothly connecting the (N+1) discrete points, and the second curve can be a curve on the spline curve.

[0130] If the processor 110 creates the first curve and the second curve, it outputs the first curve and the second curve to the display 130. The first curve and the second curve are continuous curves created by different line types. Figure 10 In the example shown, the first curve representing the change in the measured surface uplift amount of the focus point over time is displayed as a solid line, and the second curve representing the change in the predicted uplift amount over time is displayed as a dotted line. The user can distinguish between the first and second curves by the difference in their line types.

[0131] exist Figure 10 There are four curves a1 to a4 in the figure, but Figure 9 When the position of an arbitrary point within the upper frame A1 is input by the user as a focus point, only the curve a1 is displayed on the display 130. Figure 9 When the position of any point within the upper frame A2 is input by the user as a focus point, the processor 110 plots points representing the amount of uplift at the start of measurement and at the time of periodic maintenance corresponding to the focus point, creates a first curve and a second curve based on the plotted points, and displays the curve a2 on the display 130. Similarly, Figure 9 When the position of any point in the upper frame A3 is input by the user as the focus point, or when Figure 9 When the position of an arbitrary point within the upper frame A4 is input by the user as a focus point, the curve a3 or the curve a4 is also displayed on the display 130 .

[0132] In the above embodiment, a first curve representing the change over time of the surface uplift amount of the measured point of interest and a second curve representing the change over time of the predicted uplift amount are produced and displayed on the display 130, but a first curve representing the change over time of the surface uplift amount of the measured point of interest and a second curve representing the change over time of the predicted uplift amount can also be produced and displayed on the display 130.

[0133] If used Figure 8 As described above, the "floating amount" can be calculated by subtracting the amount of uplift at the start of measurement at the same position from the amount of uplift measured at each inspection.

[0134] The processor 110 can create a first curve showing the temporal change of the thus calculated “floating amount” and a second curve showing the temporal change of the predicted “floating amount”, and display the created first and second curves on the display 130 .

[0135] Figure 11 These are the first and second curves showing the amount of uplift that changes over time on the surface of a point of interest of a building.

[0136] exist Figure 11 The four curves a1 to a4 are shown in Figure 10 In the same manner as shown, according to the user's instructions Figure 9 For a focus point within any of the frames A1 to A4 shown, a first curve and a second curve corresponding to the temporal change in the floating amount at the designated focus point are created and displayed on the display 130 .

[0137] exist Figure 11 The floating amount of a building is zero at the beginning of measurement, and then increases as the building deteriorates over time.

[0138] And, in Figure 11 , a spalling risk region B is displayed. The processor 110 creates the spalling risk region B according to the set spalling risk threshold, and visually combines the created spalling risk region with the first and second curves, and displays the result on the display 130 .

[0139] Here, the peeling risk threshold value may be arbitrarily set by the user using the operation unit 150 .

[0140] Sometimes the user knows the amount of peeling based on experience, and the peeling risk threshold value can be set according to the user's experience.

[0141] Furthermore, the spalling risk threshold can be automatically predicted and used as the set spalling risk threshold. The spalling risk threshold can be predicted by regression analysis using statistical data including the amount of lift during past spalling, or by using images and AI (artificial intelligence).

[0142] Figure 11 The spalling risk area B shown is an area where the floating amount exceeds the set spalling risk threshold.

[0143] according to Figure 11 In the example shown, the focus points corresponding to curves a1 and a2 can be identified as malignant levitation with rapid progression, and the time at which spalling will occur (the spalling time when the second curve exceeds the spalling risk threshold) can also be predicted. Such malignant levitation is preferably repaired at an early stage, before it interferes with the function of the building. On the other hand, the focus points corresponding to curves a3 and a4 can be identified as benign levitation with slow progression.

[0144] And, in Figure 11 In the figure, the spalling danger zone B is displayed together with the curve. However, a spalling danger line indicating a set spalling danger threshold value (floating amount of the lower limit of the spalling danger zone B) may be displayed instead of the spalling danger zone B.

[0145] Figure 12 These are the first and second curves showing the rate of change of the lift amount of the surface of the point of interest of the building over time.

[0146] The processor 110 can calculate Figure 11 The processor 110 generates a first curve showing the temporal change of the "rate of change of the floating amount" thus calculated and a second curve showing the temporal change of the "rate of change of the floating amount" expected, as shown in FIG. Figure 12 As shown in FIG. 1 , the generated first and second curves are displayed on the display 130 .

[0147] In addition, Figure 12 2 shows a peeling risk line C indicating a peeling risk threshold. The peeling risk line C is a threshold value set as a rate of change in the amount of floating that indicates the risk of peeling / stripping of the surface of the building.

[0148] Users can also Figure 12 The indicators shown are used to predict the period of spalling of the building surface.

[0149] [Spalling prediction method]

[0150] Figure 13 This is a flowchart showing an embodiment of the spalling prediction method according to the present invention.

[0151] in addition, Figure 13 The spalling prediction method shown is achieved by Figure 7 The processor 110 of the spalling prediction apparatus 100 performs the method shown.

[0152] exist Figure 13 In the process, the processor 110 obtains the information of the three-dimensional measuring device 10 (reference Figure 3 The processor 110 may obtain the three-dimensional measurement data from the storage device of the data processing device 14, the cloud, etc., or from the memory 120 of the spalling prediction device 100.

[0153] The processor 110 detects the amount of surface elevation from the reference plane at one or more points of interest on the building surface based on the three-dimensional measurement data (step S20). The one or more points of interest on the building surface can be specified by the user.

[0154] The processor 110 predicts the future uplift amount of the point of interest based on the elapsed time of the inspection and the uplift amount during each inspection (step S30 ).

[0155] The processor 110 creates a graph (first graph) showing the temporal change of the measured amount of uplift at the point of interest and a graph (second graph) showing the temporal change of the predicted amount of uplift at the point of interest (step S40 ).

[0156] The processor 110 outputs the generated first and second curves to the display 130 (step S50). Figure 10 As shown, a first curve showing the temporal change of the measured swelling amount of the attention point is displayed by a solid line, and a second curve showing the temporal change of the predicted swelling amount of the attention point is displayed by a dotted line. It is preferable to display both curves in a distinguishable manner.

[0157] The user can determine from the first and second curves whether the focus point is a malignant swelling with a rapid growth rate, a benign swelling that has grown since the construction, or a slow growth rate.

[0158] [other]

[0159] The building in this embodiment is a tunnel, but it is not limited thereto and can be any building as long as it is a building to be repaired, such as a bridge or a dam. The surface material of the building includes reinforced concrete, concrete and concrete repair materials such as mortar.

[0160] In addition, the spalling prediction device displays the first curve and the second curve representing the change in the amount of surface uplift of the point of interest (including the difference in the amount of uplift for each inspection, i.e., the amount of floating, the rate of change of the floating, etc.) over time on the display in a visually recognizable manner, but it can also be set to be displayed together with the first curve and the second curve, or separately from the first curve and the second curve to compare the second curve with the set spalling risk threshold, predict the period when the second curve exceeds the spalling risk threshold as the spalling period, and notify the spalling period.

[0161] Furthermore, in this embodiment, the hardware configuration of a processing unit (processing unit) that performs various processes, such as a CPU (Central Processing Unit), includes the following various processors. These processors include general-purpose processors (CPUs) that execute software (programs) to function as various processing units, processors whose circuit configuration can be modified after manufacturing (such as FPGAs (Field Programmable Gate Arrays), which are programmable logic devices (PLDs), and processors (such as application-specific integrated circuits) with circuit configurations specifically designed to perform specific processes (such as ASICs).

[0162] One processing unit may be composed of one of these various processors, or may be composed of two or more processors of the same or different types (for example, multiple FPGAs or a combination of a CPU and an FPGA). In addition, multiple processing units may be composed of one processor. As an example of multiple processing units composed of one processor, there is the following method: as represented by computers such as clients and servers, one processor is composed of a combination of one or more CPUs and software, and the processor functions as multiple processing units. Second, there is the following method: as represented by a system on chip (SoC), a processor that implements the functions of the entire system including multiple processing units using one IC (Integrated Circuit) chip is used. In this way, various processing units are composed of one or more of the above-mentioned various processors as a hardware structure.

[0163] Furthermore, more specifically, the hardware structure of these various processors is a circuit (circuitry) formed by combining circuit elements such as semiconductor elements.

[0164] Furthermore, the present invention includes a spalling prediction program that, when installed in a computer, causes the computer to function as the spalling prediction device according to the present invention, and a nonvolatile storage medium having the spalling prediction program recorded thereon.

[0165] Furthermore, the present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the spirit of the present invention.

[0166] Explanation of symbols

[0167] 10-three-dimensional measuring device, 12-tripod, 14-data processing device, 16-power supply device, 18-trolley, 20-wall, 30L-left camera, 30R-right camera, 40-rebar, 50-reactive skeleton, 60-steel, 100-peeling prediction device, 110-processor, 120-memory, 130-display, 140-input and output interface, 150-operating unit, A1~A4-frame, B-peeling danger area, C-peeling danger line, S10~S50-steps, a1~a4-curves.

Claims

1. A spalling prediction device comprising: a processor; and a memory storing a program for execution by the processor, wherein: The processor performs the following processing: detecting an amount of bulge of the surface at one or more points of interest on the surface based on three-dimensional measurement data for measuring the three-dimensional shape of the surface of the building, namely, a plurality of three-dimensional measurement data measured each time the building is inspected; predicting a future uplift amount of the point of interest based on a time period of the inspection and the uplift amount during each inspection; creating a first curve representing a temporal change in the amount of uplift of the attention point and a second curve representing a temporal change in the predicted amount of uplift; and The created first curve and second curve are output.

2. The spalling prediction device according to claim 1, wherein: The processor performs the following processing: detecting a floating amount of the point of interest based on a difference between the swelling amount at the time of starting maintenance at the point of interest and the swelling amount during maintenance after the time of starting maintenance; and predicting the future floating amount of the point of interest based on the elapsed time of the maintenance and the floating amount of each maintenance after the start time of the maintenance, The first curve is a curve showing the temporal change of the floating amount of the attention point, and the second curve is a curve showing the temporal change of the predicted floating amount.

3. The spalling prediction device according to claim 1 or 2, wherein: The first curve and the second curve are continuous curves produced using different line types.

4. The spalling prediction device according to claim 2, wherein: The processor performs the following processing: Creating a spalling hazard line or spalling hazard area based on the set spalling hazard threshold; and The spalling risk line or the spalling risk area is synthesized into the first curve and the second curve.

5. The spalling prediction device according to claim 2, wherein: The processor performs the following processing: comparing the second curve with a set spalling risk threshold, and predicting a period when the second curve exceeds the spalling risk threshold as a spalling period; and The peeling period is notified.

6. The spalling prediction device according to claim 4 or 5, wherein: The processor accepts the spalling risk threshold value through user input or automatically predicts the spalling risk threshold value, and uses the accepted spalling risk threshold value or the predicted spalling risk threshold value as the set spalling risk threshold value.

7. The spalling prediction device according to claim 2, wherein: The processor performs the following processing: creating a surface property image that visualizes the magnitude of the floating amount of the surface based on the floating amount at the time of inspection of the surface of the building; causing the surface texture image to be displayed on a display; and When an arbitrary position on the surface texture image displayed on the display is accepted as the focus point by user input, the first curve and the second curve created corresponding to the accepted focus point are displayed on the display.

8. The spalling prediction device according to claim 7, wherein: The surface texture image is an image having regions whose brightness or color varies according to the floating amount, or a contour map corresponding to the floating amount.

9. The spalling prediction device according to claim 1, wherein: The three-dimensional measurement data is data measured by LiDAR or a stereo camera.

10. The spalling prediction device according to claim 1, wherein: The three-dimensional measurement data is data measured by FMCW LiDAR.

11. The spalling prediction device according to claim 1, wherein: The plurality of three-dimensional measurement data are adjusted so that the plurality of three-dimensional measurement data at the same position on the surface of the building where the amount of uplift does not change match.

12. The spalling prediction device according to claim 1, wherein: The material of the surface of the building comprises concrete or concrete repair material.

13. A method for predicting spalling of a surface of a building, wherein: The processor performs the following steps respectively: detecting an amount of bulge of the surface at one or more points of interest on the surface based on three-dimensional measurement data for measuring the three-dimensional shape of the surface of the building, namely, a plurality of three-dimensional measurement data measured each time the building is inspected; predicting a future uplift amount of the point of interest based on a time period of the inspection and the uplift amount during each inspection; creating a first curve representing a temporal change in the amount of uplift of the attention point and a second curve representing a temporal change in the predicted amount of uplift; and The created first curve and second curve are output.

14. The spalling prediction method according to claim 13, wherein: The processor executes the following steps respectively: detecting a floating amount of the point of interest based on a difference between the swelling amount at the time of starting maintenance at the point of interest and the swelling amount during maintenance after the time of starting maintenance; and predicting the future floating amount of the point of interest based on the elapsed time of the maintenance and the floating amount of each maintenance after the start time of the maintenance, The first curve is a curve showing the temporal change of the floating amount of the attention point, and the second curve is a curve showing the temporal change of the predicted floating amount.

15. The spalling prediction method according to claim 14, wherein: The processor executes the following steps respectively: Creating a spalling hazard line or spalling hazard area based on the set spalling hazard threshold; and The spalling risk line or the spalling risk area is synthesized into the first curve and the second curve.

16. The spalling prediction method according to claim 14, wherein: The processor executes the following steps respectively: comparing the second curve with a set spalling risk threshold, and predicting a period when the second curve exceeds the spalling risk threshold as a spalling period; and The peeling period is notified.

17. A spalling prediction program, which causes a computer to perform the following functions: detecting an amount of bulge of the surface at one or more points of interest on the surface based on three-dimensional measurement data for measuring the three-dimensional shape of the surface of the building, namely, a plurality of three-dimensional measurement data measured each time the building is inspected; predicting a future uplift amount of the point of interest based on a time period of the inspection and the uplift amount during each inspection; creating a first curve representing a temporal change in the amount of uplift of the attention point and a second curve representing a temporal change in the predicted amount of uplift; and The created first curve and second curve are output. 18 . A recording medium which is non-transitory and computer-readable and has recorded thereon the program according to claim 17 .

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