WO2024171600A1 - 剥落予測装置、方法及びプログラム - Google Patents

剥落予測装置、方法及びプログラム Download PDF

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Publication number
WO2024171600A1
WO2024171600A1 PCT/JP2023/045221 JP2023045221W WO2024171600A1 WO 2024171600 A1 WO2024171600 A1 WO 2024171600A1 JP 2023045221 W JP2023045221 W JP 2023045221W WO 2024171600 A1 WO2024171600 A1 WO 2024171600A1
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Prior art keywords
peeling
amount
graph
inspection
point
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Ceased
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PCT/JP2023/045221
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English (en)
French (fr)
Japanese (ja)
Inventor
洋輔 西浦
孝一郎 中村
悠也 石塚
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Fujifilm Corp
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Fujifilm Corp
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Priority to JP2025500677A priority Critical patent/JPWO2024171600A1/ja
Priority to CN202380093787.8A priority patent/CN120677379A/zh
Publication of WO2024171600A1 publication Critical patent/WO2024171600A1/ja
Priority to US19/294,256 priority patent/US20250363686A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—Two-dimensional [2D] image generation
    • G06T11/20—Drawing from basic elements
    • G06T11/26—Drawing of charts or graphs
    • E—FIXED CONSTRUCTIONS
    • E04—BUILDING
    • E04G—SCAFFOLDING; FORMS; SHUTTERING; BUILDING IMPLEMENTS OR AIDS, OR THEIR USE; HANDLING BUILDING MATERIALS ON THE SITE; REPAIRING, BREAKING-UP OR OTHER WORK ON EXISTING BUILDINGS
    • E04G23/00—Working measures on existing buildings
    • E04G23/02—Repairing, e.g. filling cracks; Restoring; Altering; Enlarging
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
    • E21D11/00—Lining tunnels, galleries or other underground cavities, e.g. large underground chambers; Linings therefor; Making such linings in situ, e.g. by assembling
    • E21D11/04—Lining with building materials
    • E21D11/10—Lining with building materials with concrete cast in situ; Shuttering also lost shutterings, e.g. made of blocks, of metal plates or other equipment adapted therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction

Definitions

  • the present invention relates to a spalling prediction device, method and program, and in particular to a technology for predicting spalling of materials (such as concrete) on the surface of a building.
  • Patent Documents 1-3 have been proposed to detect concrete floating or internal cavities that can lead to concrete fragments spalling.
  • the spalling prediction and diagnosis method described in Patent Document 1 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. Based on the infrared thermal image and the outside air temperature, it calculates the temperature difference at the peeling part as the temperature difference between the healthy part and the peeling part, and the measured temperature environment as the difference between the surface temperature of the healthy part and the outside air temperature. It calculates a temperature environment coefficient as the ratio of the calculated temperature difference at the peeling part to the calculated measured temperature environment, and quantitatively evaluates the risk of the cover concrete (concrete from the rebar surface to the concrete surface) spalling according to the temperature environment coefficient. It also predicts the time of spalling by comparing the spalling risk calculated last time with the spalling risk calculated this time.
  • Patent Document 2 The inspection method described in Patent Document 2 involves striking the object to be inspected with an inspection hammer device, and judging the condition of the object to be inspected based on the time history data of the sound pressure generated by the strike.
  • the non-destructive inspection method for concrete structures described in Patent Document 3 involves contacting an ultrasonic transmitter and receiver with the submerged portion of a concrete structure that is partially or completely submerged in water, irradiating shear ultrasonic waves from the transmitter into the concrete structure while detecting the resonant vibration of the concrete structure with the receiver, and judging rear and/or internal damage to the concrete structure based on the waveform detected by the receiver.
  • the spalling prediction and diagnosis method described in Patent Document 1 uses thermal images of a concrete structure taken with an infrared camera, making it difficult to predict spalling with high accuracy.
  • the temperature of the peeled part may be higher or lower than that of the healthy part depending on various environmental conditions, such as whether the structure is exposed to sunlight, the intensity of sunlight, and the outside air temperature.
  • thermal images with an infrared camera it is difficult to take images of a structure under the same environmental conditions both the previous time and the current time, making it difficult to predict spalling with high accuracy by comparing the spalling risk calculated last time with the spalling risk calculated this time.
  • Patent Document 2 has the problem that it takes a long time to determine the soundness of a large-area object using tapping sounds alone, and the non-destructive inspection method for concrete structures described in Patent Document 3 requires that an ultrasonic transmitter and receiver be brought into contact with the partially or entirely submerged portion of the concrete structure. Furthermore, Patent Documents 2 and 3 do not mention predicting the spalling of material from the surface of the structure.
  • One embodiment of the technology disclosed herein provides a spalling prediction device, method, and program that can accurately predict the spalling of material on the surface of a building.
  • the invention according to the first aspect is a spalling prediction device that includes a processor and a memory that stores a program to be executed by the processor, the processor detects the amount of surface swelling at one or more points of interest on the surface based on a plurality of pieces of three-dimensional measurement data that are three-dimensional measurement data that measure the three-dimensional shape of the surface of a structure, measured at each inspection of the structure, predicts the future amount of swelling at the points of interest based on the inspection period and the amount of swelling at each inspection, creates a first graph showing the change in the amount of swelling at the points of interest over time and a second graph showing the change in the predicted amount of swelling over time, and outputs the first and second graphs that have been created.
  • the amount of surface swelling at one or more points of interest on the surface is detected based on multiple three-dimensional measurement data measured at each inspection of a structure, and the future amount of swelling at the points of interest is predicted based on the inspection period and the amount of swelling at each inspection.
  • a first graph showing the change in the amount of swelling at the points of interest over time and a second graph showing the change in the predicted amount of swelling over time are then created, and the first and second graphs created are output. From the first and second graphs, the user (inspector) can understand the amount of swelling (amount of lift) that changes over time, and can also predict when material will peel off from the surface of the structure in the future. Therefore, measures can be taken such as prioritizing repairs for areas where peeling is likely to occur early.
  • the processor detects the amount of lift at the point of interest from the difference between the amount of lift at the point of interest at the start of inspection and the amount of lift at each inspection after the start of inspection, and predicts the future amount of lift at the point of interest based on the duration of the inspection and the amount of lift at each inspection after the start of inspection, the first graph being a graph showing the change in the amount of lift at the point of interest over time, and the second graph being a graph showing the change in the predicted amount of lift over time.
  • the actual change over time in the amount of lift at a point of interest on the surface of a structure and the predicted change over time in the amount of lift in the future can be grasped from the first graph and the second graph, respectively. This makes it easy to distinguish between areas that have been raised since construction and areas that have subsequently risen (lifted) due to rust on rebar, etc.
  • the first graph and the second graph are preferably continuous graphs created using different line types.
  • the processor creates a peeling danger line or peeling danger area based on a set peeling danger threshold, and synthesizes the peeling danger line or peeling danger area with the first graph and the second graph. This allows the user to grasp the time when the second graph crosses the peeling danger line or enters the peeling danger area as the time of future peeling.
  • the processor compares the second graph with a set peeling risk threshold, predicts the time when the second graph exceeds the peeling risk threshold as the time of peeling, and notifies the time of peeling.
  • the processor accepts a peeling risk threshold by user input or automatically predicts the peeling risk threshold, and uses the accepted or predicted peeling risk threshold as the set peeling risk threshold.
  • the amount of lift that occurs when peeling occurs may be known by the user from experience, and a peeling risk threshold that matches the user's experience can be set, or an automatically optimized peeling risk threshold can be set.
  • the processor preferably creates a surface property image that visualizes the amount of surface lift based on the amount of lift at the time of inspection of the surface of the building, displays the surface property image on a display, and, upon receiving user input of a position on the surface property image displayed on the display as a focus point, displays on the display a first graph and a second graph created corresponding to the received focus point.
  • the user can easily indicate a point of interest, and by displaying on the display the first and second graphs created in response to the point of interest indicated by the user, the user can grasp the change in the amount of lifting of the point of interest over time and the timing of future peeling, etc.
  • the surface property image is preferably an image having areas with different brightness or color depending on the amount of lifting, or a contour map depending on the amount of lifting.
  • the three-dimensional measurement data can be data measured by LiDAR or a stereo camera.
  • the three-dimensional measurement data is data measured by LiDAR using the FMCW (Frequency Modulated Continuous Wave) method. This makes it possible to detect the amount of lifting that cannot be confirmed by visual inspection.
  • FMCW Frequency Modulated Continuous Wave
  • the multiple pieces of three-dimensional measurement data are adjusted so that the multiple pieces of three-dimensional measurement data for the same position on the surface of the structure and at a position where the amount of protrusion has not changed match. This is to align the multiple pieces of three-dimensional measurement data measured at each inspection, so that the locations where the amount of protrusion changes can be accurately detected.
  • the material of the surface of the building includes concrete or a concrete repair material.
  • the invention according to the thirteenth aspect is a spalling prediction method for predicting spalling on the surface of a structure, the method including the steps of: detecting the amount of surface bulge at one or more points of interest on the surface based on a plurality of three-dimensional measurement data that are obtained by measuring the three-dimensional shape of the surface of the structure and that are measured at each inspection of the structure; predicting the future amount of bulge at the points of interest based on the inspection period and the amount of bulge at each inspection; creating a first graph showing the change in the amount of bulge at the points of interest over time and a second graph showing the change in the predicted amount of bulge over time; and outputting the created first and second graphs.
  • the peeling prediction method is the 13th aspect, in which a processor executes the steps of detecting the amount of lift at the point of interest from the difference between the amount of lift at the point of interest at the start of inspection and the amount of lift at each inspection after the start of inspection, and predicting the future amount of lift at the point of interest based on the inspection period and the amount of lift at each inspection after the start of inspection, and it is preferable that the first graph is a graph showing the change in the amount of lift at the point of interest over time and the second graph is a graph showing the change in the predicted amount of lift over time.
  • a processor executes the steps of creating a peeling danger line or a peeling danger area based on a set peeling danger threshold, and of synthesizing the peeling danger line or the peeling danger area with the first graph and the second graph.
  • the method for predicting peeling is preferably the 14th aspect, in which a processor executes the steps of comparing the second graph with a set peeling risk threshold, predicting the time when the second graph exceeds the peeling risk threshold as the time of peeling, and notifying the time of peeling.
  • the invention according to the seventeenth aspect is a spalling prediction program that causes a computer to execute the following functions: detect the amount of surface bulge at one or more points of interest on the surface based on a plurality of three-dimensional measurement data that are three-dimensional measurement data that measure the three-dimensional shape of the surface of a structure and are measured each time the structure is inspected; predict the future amount of bulge at the points of interest based on the inspection time period and the amount of bulge at each inspection; create a first graph that shows the change in the amount of bulge at the points of interest over time and a second graph that shows the change in the predicted amount of bulge over time; and output the first and second graphs that have been created.
  • the present invention makes it possible to accurately predict the loss of material from the surface of a building.
  • FIG. 1 is a graph showing the relationship between the time elapsed since 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.
  • FIG. 2 is a schematic diagram of a structure inspection system including a spalling prediction device according to the present invention.
  • FIG. 3 is an external view of one embodiment of a three-dimensional measuring device including an FMCW LiDAR.
  • FIG. 4 is a diagram showing an embodiment in which a three-dimensional shape of the surface of a building is measured by a stereo camera.
  • FIG. 5 is a cross-sectional view of the vicinity of the surface of a structure, showing an example of a mechanism by which the surface of a structure peels off.
  • FIG. 1 is a graph showing the relationship between the time elapsed since 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.
  • FIG. 2 is a schematic diagram of a structure inspection
  • FIG. 6 is a cross-sectional view of the vicinity of the surface of a structure, showing another example of the mechanism by which the surface of a structure peels off.
  • FIG. 7 is a block diagram showing an embodiment of the hardware configuration of the peeling prediction device according to the present invention.
  • FIG. 8 is a diagram illustrating a method for identifying bumps and reliefs on the surface of a structure.
  • FIG. 9 is a diagram showing an example of a surface texture image displayed on the display unit.
  • FIG. 10 is a first graph and a second graph showing the amount of swelling that changes over time on the surface of a target point of a building.
  • FIG. 11 is a first graph and a second graph showing the amount of lift that changes over time on the surface of a target point of a building.
  • FIG. 12 is a first graph and a second graph showing the rate of change in the amount of lift that changes over time on the surface of a target point of a building.
  • FIG. 13 is a flow chart showing an embodiment of
  • FIG. 1 is a graph showing the relationship between the time elapsed since 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.
  • the displacement of the structure's surface is measured at t1, the start point of inspection of the structure (the start point of measurements during construction), and at each inspection point after the start of measurements (t2, t3, t4, t5, ).
  • the building is in a normal state (A) at the start of measurement time t1, but at the inspection time t2, the surface bulges slightly due to deterioration of the building (cracks (B)). The bulge at this stage is not noticeable to the naked eye. Note that "cracks" are often caused by corrosion and thickening of the steel (reinforcing bars) inside the building.
  • the inspection time t5 indicates the time when the concrete cover (concrete from the rebar surface to the concrete surface) falls (E) Peeling/falling off).
  • the displacement (amount of rise) of the structure's surface measured at each inspection gradually increases, causing the covering concrete to peel off and fall off.
  • deterioration of buildings can also include deterioration of concrete strength, cracks, and surface deterioration. All deterioration of buildings causes 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.
  • the present invention therefore detects the amount of surface bulge at one or more points of interest on the surface based on multiple pieces of three-dimensional measurement data measured at each inspection of a building, predicts the future amount of bulge at the points of interest based on the inspection period and the amount of bulge at each inspection, creates a first graph showing the change in the amount of bulge at the points of interest over time and a second graph showing the change in the predicted amount of bulge over time, and outputs the first and second graphs that have been created.
  • FIG. 2 is a schematic diagram of a structure inspection system including a spalling prediction device according to the present invention.
  • the inspection system shown in Figure 2 is a system for inspecting railway tunnels, and is equipped with a three-dimensional measuring device 10, a data processing device 14, and a power supply device 16.
  • the three-dimensional measuring device 10 is mounted on a tripod 12, but it may also be mounted on a carriage 18 that runs on the track.
  • the three-dimensional measuring device 10 is a LiDAR (Light Detection And Ranging), and in particular, a FMCW (Frequency Modulated Continuous Wave) LiDAR capable of measuring distances on the order of several hundred ⁇ m, but the present invention is not limited to the use of distance data (three-dimensional measurement data) measured by an FMCW LiDAR.
  • LiDAR Light Detection And Ranging
  • FMCW Frequency Modulated Continuous Wave
  • FIG. 3 is an external view of one embodiment of a three-dimensional measuring device including an FMCW LiDAR.
  • the three-dimensional measuring device 10 is mounted on a bogie 18 that runs on the tracks as shown in Figure 2, and measures the distance to the surface of a tunnel, which is a railway structure.
  • the cart 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 3D measuring device 10 and the data processing device 14.
  • the three-dimensional measuring device 10 measures the distance to the tunnel wall (surface) 20, thereby obtaining three-dimensional measurement data indicating the shape of the tunnel wall 20.
  • the three-dimensional measuring device 10 scans the FMCW type laser light at high speed in the left-right direction (main scanning direction) of the wall surface 20 shown in FIG. 3, and also moves the scanning line in the up-down direction (sub-scanning direction) of the wall surface 20 to scan. 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 light. Then, three-dimensional data in a polar coordinate system consisting of the irradiation direction of the laser light and the measured distance is converted into three-dimensional data in a Cartesian coordinate system to obtain 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.
  • the three-dimensional measuring device 10 measures the uneven shape of the minute wall surface 20 under the following conditions. Measurement accuracy: 50 ⁇ m Measurement distance: 2 to 7 m Measurement speed: 10 m 2 /sec in area (laser light speed is equivalent to 4000 rpm)
  • the three-dimensional measuring device 10 acquires three-dimensional data of the wall surface 20 at regular intervals while the cart 18 is moving, for example, and it is preferable to acquire the three-dimensional data so that the measurement areas of the three-dimensional data acquired at each interval overlap in part. This is to create a panoramic composite of the three-dimensional data acquired at each interval.
  • the 3D measuring device 10 can achieve the above measurement accuracy by using FMCW LiDAR, but the conditions such as the measurement accuracy of the 3D measurement data required in this invention are not limited to the above example, and the 3D measuring device is not limited to FMCW LiDAR, and various types can be used.
  • a TOF (Time of Flight) LiDAR can be used, which measures the flight time of pulsed light to measure the distance to the wall surface 20.
  • a stereo camera can be used to measure the three-dimensional shape of the wall surface 20.
  • Figure 4 shows an embodiment in which a stereo camera is used to measure the three-dimensional shape of the surface of a building.
  • the stereo camera shown in Figure 4 consists of a left camera 30L and a right camera 30R, and measures the distance to the wall surface 20 being photographed using triangulation.
  • three-dimensional measuring devices that can be used to measure the distance to the wall surface 20 (i.e., three-dimensional measurement data of the wall surface) include the laser radar three-dimensional shape measuring device described in JP 09-297014 A, and the measuring device using the light-cutting method with an imaging device and a slit laser light projector described in JP 2021-2016-31249 A.
  • the three-dimensional shape of the tunnel wall 20 is measured by the three-dimensional measuring device 10 when the tunnel measurement starts (construction) and at the time of regular inspection after construction.
  • the measured three-dimensional measurement data of the wall is stored in a storage device within the data processing device 14 or an external storage device at the time of measurement start and at the time of regular inspection.
  • FIG. 5 is a cross-sectional view of the vicinity of the surface of a structure, showing an example of a mechanism by which the surface of a structure peels off.
  • (A) Normal state refers to the normal state, such as during construction of a building.
  • the surface in this state is the reference surface.
  • 40 is steel material (reinforcing bars).
  • Figure 6 is a cross-sectional view of the vicinity of the surface of a structure, showing another example of the mechanism by which the surface of a structure peels off.
  • (A) Normal state refers to a normal state, such as during construction of a building.
  • the surface in this state is the reference surface.
  • 50 indicates reactive aggregate
  • 60 indicates steel.
  • FIG. 7 is a block diagram showing an embodiment of the hardware configuration of the peeling prediction device according to the present invention.
  • the peeling prediction device 100 shown in FIG. 7 is configured, for example, by 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.
  • This peeling prediction device 100 can be incorporated as one function of the data processing device 14 shown in FIG. 2.
  • the processor 110 is composed of a CPU (Central Processing Unit) and other components, and controls each part of the spalling prediction device 100, and executes various processes to predict spalling on the surface of a building structure by executing a spalling prediction program. Details of the various processes performed by the processor 110 will be described later.
  • CPU Central Processing Unit
  • Memory 120 includes flash memory, ROM (Read-only Memory), RAM (Random Access Memory), a hard disk drive, etc.
  • the flash memory, ROM, or hard disk drive is a non-volatile memory that stores an operation system, various programs including the spalling prediction program according to the present invention, etc.
  • non-volatile memory (storage device) such as the flash memory and hard disk drive stores three-dimensional measurement data of the structure's surface measured by the three-dimensional measuring device 10 at the start of measurement of the structure and during regular inspections, together with the time of measurement.
  • the RAM functions as a working area for processing by the processor 110. It also temporarily stores various programs stored in flash memory, etc., and three-dimensional measurement data of the surface of the structure.
  • the processor 110 may also incorporate part of the memory 120 (RAM).
  • the display 130 not only displays a screen for operating the peeling prediction device 100, but also displays graphs created by the peeling prediction 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.
  • GUI Graphic User Interface
  • the input/output interface 140 includes a connection unit that can be connected to an external device, and a communication unit that can be connected to a network.
  • a connection unit that can be connected to an external device a USB (Universal Serial Bus), HDMI (High-Definition Multimedia Interface) (HDMI is a registered trademark), etc. can be applied.
  • the peeling prediction device 100 can be configured as a device independent of the data processing device 14.
  • the processor 110 can acquire three-dimensional measurement data of the surface of the structure from the data processing device 14 via the input/output interface 140, or, if the three-dimensional measurement data is stored in the cloud, acquire the three-dimensional measurement data of the surface of the structure from the cloud via the input/output interface 140.
  • the processor 110 can also store the three-dimensional measurement data acquired in this manner in the memory 120.
  • the operation unit 150 includes a pointing device such as a mouse, a keyboard, etc., and functions as part of a GUI that uses the display screen of the display unit 130 and accepts instruction input by user operation.
  • a pointing device such as a mouse, a keyboard, etc.
  • Figure 8 shows a method for identifying bumps and floats on the surface of a structure.
  • Figure 8 (A) shows the surface of a building and the scanning line of the laser light scanning that surface.
  • the three-dimensional measuring device 10 acquires three-dimensional measurement data (point cloud data) of multiple measurement points on the scanning line of the laser light.
  • the processor 110 calculates the distance in the normal direction of the reference plane of the point cloud data relative to the reference plane shown in FIG. 5, etc., as the height of the surface of the structure.
  • the reference plane can be defined as appropriate.
  • Figure 8 (B) is a waveform diagram showing the surface height of a building obtained from point cloud data on a scan line.
  • Figure 8 (C) is a waveform diagram showing the height of the structure's surface obtained from point cloud data on the same scan line measured after the start of inspection of the structure's surface shown in Figure 8 (B).
  • Figure 8 (D) is a waveform diagram showing the difference obtained by subtracting the waveform showing the surface height shown in Figure 8 (B) from the waveform showing the surface height shown in Figure 8 (C).
  • the waveform shown in Figure 8 (D) is a waveform diagram that shows the amount of change (floating) in the surface of the structure shown in Figures 8 (B) and (C) over time between each measurement point.
  • the multiple 3D measurement data for each measurement point on the surface of the structure are adjusted so that the multiple 3D measurement data for the same position on the surface of the structure, at a position where the amount of protrusion has not changed, match.
  • the processor 110 creates a surface property image that visualizes the amount of surface protrusion based on, for example, three-dimensional measurement data (point cloud data) of the surface of the building acquired during the most recent inspection.
  • three-dimensional measurement data point cloud data
  • the processor 110 displays the created surface property image on the display 130.
  • Figure 9 shows an example of a surface property image displayed on the display.
  • the surface property image shown in Figure 9 is an image made up of multiple points that are uniformly distributed on the surface of a building. Each point in this image is made up of brightness data or color data that differs in brightness or color depending on the amount of protrusion at each point.
  • the surface property image displayed on the display 130 is therefore an image made up of multiple points uniformly distributed over the surface of the building, with each point having an area (point area) that differs in brightness or color depending on the amount of protrusion at that position.
  • the images of the points included in frames A1 to A4 have different brightness or color compared to the images of the points in other areas. This allows the user to recognize that the amount of bulging at the positions of the points included in frames A1 to A4 is greater than the amount of bulging at the positions of the points in other areas.
  • the surface property image displayed on the display 130 is an image made up of multiple points uniformly distributed on the surface of the structure, but is not limited to this.
  • the image can be a heat map, a grayscale image, or a contour map according to the amount of protrusion on the surface of the structure.
  • the surface property image can be created according to the amount of floating instead of the amount of protrusion on the surface of the structure.
  • the processor 110 When the processor 110 receives user input specifying a position on the surface property image displayed on the display 130 as a point of interest, it creates a graph corresponding to the received point of interest.
  • the processor 110 detects the amount of bulge based on the three-dimensional measurement data of the structure measured at the start of measurement of the structure and during regular inspections, at the position of the attention point for which user input has been accepted.
  • processor 110 predicts the future amount of rise at the point of interest based on the inspection period and the amount of rise at each inspection. It creates a graph (first graph) showing the measured change in the amount of rise at the point of interest over time, and a graph (second graph) showing the predicted change in the amount of rise at the point of interest over time.
  • Figure 10 shows the first and second graphs that show the amount of rise that changes over time at a focal point on a building.
  • processor 110 when the position of any point within frame A1 in FIG. 9 is input by the user as a focus point, processor 110 plots points indicating the amount of surface bulge at the start of measurement of the surface corresponding to the focus point (year 0) and the amount of surface bulge at regular inspections 2, 4, 6, and 8 years after the start of measurement, and creates a first graph by connecting each plotted point to show the change over time in the amount of surface bulge measured at the focus point, and also predicts the amount of future bulge from the amount of surface bulge at the start of measurement of the surface corresponding to the focus point and the amount of surface bulge at each inspection, and creates a second graph showing the change over time in the predicted amount of bulge.
  • the first graph can be, for example, an Nth-order spline curve that smoothly connects the (N+1) discrete points
  • the second graph can be a graph on that spline curve.
  • the processor 110 After creating the first graph and the second graph, the processor 110 outputs the first graph and the second graph to the display 130.
  • the first graph and the second graph are consecutive graphs created using different line types, and in the example shown in FIG. 10, the first graph showing the measured change over time in the amount of surface bulge at the target point is displayed as a solid line, and the second graph showing the predicted change over time in the amount of bulge is displayed as a dotted line, allowing the user to distinguish between the first graph and the second graph based on the difference in the line type.
  • FIG. 10 Four graphs a1 to a4 are shown in FIG. 10, but when the position of any point in frame A1 in FIG. 9 is input by the user as the point of interest, only graph a1 is displayed on the display 130. Also, when the position of any point in frame A2 in FIG. 9 is input by the user as the point of interest, the processor 110 plots points indicating the amount of swelling at the start of measurement and at the time of regular inspection corresponding to the point of interest, creates a first graph and a second graph based on each plotted point, and displays graph a2 on the display 130. Similarly, when the position of any point in frame A3 in FIG. 9 is input by the user as the point of interest, or when the position of any point in frame A4 in FIG. 9 is input by the user as the point of interest, the processor 110 displays graph a3 or graph a4 on the display 130.
  • a first graph showing the change over time in the amount of surface rise at the point of interest that was actually measured, and a second graph showing the change over time in the predicted amount of rise were created and displayed on the display 130.
  • a first graph showing the change over time in the amount of surface rise at the point of interest that was actually measured, and a second graph showing the change over time in the predicted amount of surface rise may also be created and displayed on the display 130.
  • the "amount of float” can be calculated by subtracting the amount of bulge measured at the start of measurement at the same position from the amount of bulge measured at each inspection.
  • the processor 110 can create a first graph showing the change over time in the "float amount” calculated in this way, and a second graph showing the change over time in the predicted “float amount,” and can display the created first and second graphs on the display 130.
  • Figure 11 shows the first and second graphs that show the amount of floating that changes over time on the surface of a particular point on a building.
  • FIG. 11 four graphs a1 to a4 are shown, and similarly to the case shown in FIG. 10, depending on which of the frames A1 to A4 shown in FIG. 9 the user designates as a focus point, a first graph and a second graph corresponding to the change over time in the amount of float at the designated focus point are created and displayed on the display 130.
  • FIG. 11 shows a peeling danger area B.
  • the processor 110 creates a peeling danger area B based on the set peeling danger threshold, synthesizes the created peeling danger area into the first graph and the second graph so that it can be seen, and displays it on the display 130.
  • the peeling risk threshold may be set arbitrarily by the user using the operation unit 150.
  • the user may know from experience the amount of peeling that will occur, and can set a peeling risk threshold that matches the user's experience.
  • the peeling risk threshold may be automatically predicted, and the predicted peeling risk threshold may be used as the set peeling risk threshold.
  • the peeling risk threshold may be predicted by regression analysis using statistical data including the amount of lifting from past peeling events, and may be predicted using images and AI (artificial intelligence).
  • the peeling danger area B shown in Figure 11 is the area where the amount of lifting exceeds the set peeling danger threshold.
  • the points of interest corresponding to graphs a1 and a2 can be determined to be malignant floating, where the amount of floating is progressing quickly, and it is also possible to predict the time when spalling will occur (the time when spalling will occur when the second graph exceeds the spalling risk threshold). It is preferable to repair such malignant floating as a priority at an early stage while the functionality of the building is not impaired.
  • the points of interest corresponding to graphs a3 and a4 can be determined to be benign floating, where the amount of floating is progressing slowly.
  • the peeling danger area B is displayed along with the graph, but instead of the peeling danger area B, a peeling danger line indicating the set peeling danger threshold (the lower limit of the amount of lift in the peeling danger area B) may be displayed.
  • Figure 12 shows the first and second graphs that show the rate of change in the amount of floating that changes over time on the surface of a point of interest on a building.
  • Processor 110 can calculate the rate of change in the amount of float by calculating the slope (differential value) of the amount of float shown in FIG. 11.
  • Processor 110 creates a first graph showing the change over time in the "rate of change in the amount of float” calculated in this way, and a second graph showing the change over time in the predicted “rate of change in the amount of float,” and displays the created first and second graphs on display 130, as shown in FIG. 12.
  • Figure 12 shows a peeling danger line C, which indicates the peeling danger threshold.
  • the peeling danger line C is a threshold set as the rate of change in the amount of lift that indicates the risk of peeling or falling off the surface of the building.
  • FIG. 13 is a flow chart showing an embodiment of a spalling prediction method according to the present invention.
  • the peeling prediction method shown in FIG. 13 is a method performed by the processor 110 of the peeling prediction device 100 shown in FIG. 7.
  • the processor 110 acquires three-dimensional measurement data measured by the three-dimensional measuring device 10 (see FIG. 3) for each inspection of the building (step S10).
  • the processor 110 may acquire the three-dimensional measurement data from a storage device of the data processing device 14, the cloud, etc., or may acquire the data from the memory 120 of the spalling prediction device 100.
  • the processor 110 detects the amount of rise of the surface of the structure from the reference plane at one or more points of interest on the surface of the structure based on the three-dimensional measurement data (step S20). Note that the one or more points of interest on the surface of the structure can be specified by the user.
  • the processor 110 predicts the future amount of rise at the point of interest based on the elapsed time of the inspection and the amount of rise at each inspection (step S30).
  • the processor 110 creates a graph (first graph) showing the measured change in the amount of rise at the point of interest over time, and a graph (second graph) showing the predicted change in the amount of rise at the point of interest over time (step S40).
  • the processor 110 outputs the created first and second graphs to the display 130 (step S50). For example, as shown in FIG. 10, it is preferable to display the first graph, which shows the measured change over time in the amount of rise at the attention point, as a solid line, and the second graph, which shows the predicted change over time in the amount of rise at the attention point, as a dotted line, so that the two graphs can be distinguished from one another.
  • the user can determine from the first and second graphs whether the point of interest is a malignant bulge that is growing rapidly, whether it has been there since construction, or whether it is a benign bulge that is growing slowly.
  • the structure in this embodiment is a tunnel, but is not limited thereto and may be any structure that is inspected, such as a bridge, a dam, etc.
  • Materials for the surface of the structure include reinforced concrete, concrete, and concrete repair materials such as mortar.
  • the peeling prediction device also visually displays on the display a first graph and a second graph showing the change over time in the amount of surface swelling at the point of interest (including the amount of lift, which is the difference in the amount of swelling for each inspection, the rate of change in the amount of lift, etc.), and may also compare the second graph with a set peeling risk threshold together with, or separately from, the display of the first and second graphs, predict the time when the second graph will exceed the peeling risk threshold as the time of peeling, and notify the time of peeling.
  • the hardware structure of a processing unit that executes various processes is various processors as shown below.
  • the various processors include a CPU, 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 to execute specific processes.
  • a CPU Central Processing Unit
  • PLD programmable logic device
  • FPGA Field Programmable Gate Array
  • ASIC Application Specific Integrated Circuit
  • 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 one processor.
  • multiple processing units may be configured with one processor.
  • first there is a form in which one processor is configured with a combination of one or more CPUs and software, as represented by computers such as clients and servers, and this processor functions as multiple processing units.
  • a processor is used that realizes the functions of the entire system, including multiple processing units, with a single IC (Integrated Circuit) chip, as represented by System On Chip (SoC).
  • SoC System On Chip
  • the hardware structure of these various processors is an electrical circuit that combines circuit elements such as semiconductor elements.
  • the present invention further includes a peeling prediction program that, when installed in a computer, causes the computer to function as the peeling prediction device of the present invention, and a non-volatile storage medium on which this peeling prediction program is recorded.

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JP2002174607A (ja) * 2000-12-07 2002-06-21 Fuji Electric Co Ltd 電気電子機器の保守装置および保守方法
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