Detection device

Abstract

A detection device (100) according to the present disclosure comprises: an acquisition unit (101) that acquires point cloud data of a surface of a reinforced concrete structure and reflectance of a laser beam from the surface of the reinforced concrete structure; an Ra, Rz calculation unit (105) that, on the basis of the point cloud data, calculates the average roughness Ra and the maximum roughness depth Rz of the surface of the reinforced concrete structure; and a detection unit (106) that, on the basis of the average roughness Ra, the maximum roughness depth Rz, and the reflectance, detects an exposed rebar portion on the surface of the reinforced concrete structure.

Description

Detection Device

[0001] The present disclosure relates to a detection device.

[0002] The strength of reinforced concrete structures decreases due to deterioration of exposed reinforcement, so safety assessment is necessary. Structural analysis using the finite element method (FEM (Finite Element Method) analysis) is an effective method for assessing the safety of target structures.

[0003] To perform FEM analysis, it is necessary to create a model that mimics the target structure. Generally, the shape of the target structure is represented by a model consisting of elements (mesh), and deterioration information such as exposed reinforcement bars is reproduced by modifying the boundary conditions of the target area or the model. Shape information of the target structure can be obtained from drawings, etc. Furthermore, deterioration information is often obtained from photographs or sketches taken during inspection of the target structure. In this case, shape information and deterioration information of the target structure are obtained separately, which is inefficient.

[0004] Methods for mechanically acquiring shape information of a structure include methods for acquiring point cloud data such as LiDAR (Light Detection and Ranging). Non-Patent Document 1 proposes a method for mechanically creating a CAD model from point cloud data. The method described in Non-Patent Document 1 allows a model for structural analysis to be created without specialized skills.

[0005] “Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds”, [online], [Retrieved August 14, 2024], Internet<URL: https: / / arxiv.org / pdf / 2312.04962>

[0006] Because point cloud data is coordinate information, deterioration information that can be determined by the shape of cracks or peeling in the concrete can be reflected in the model along with the shape of the structure.

[0007] However, it may be difficult to detect exposed reinforcement bars using point cloud data alone. For example, as shown in Fig. 11A, exposed reinforcement bars where rebar 1 is exposed are usually recessed from the surface of concrete 2. Therefore, even in the point cloud data of the exposed reinforcement bars shown in Fig. 11A, the areas corresponding to the exposed reinforcement bars are often recessed, as shown in Fig. 11B. However, it is not possible to determine whether the recesses are due to exposed reinforcement bars or spalling of the concrete using point cloud data alone.

[0008] Furthermore, for example, when corrosion products 1a accumulate around the exposed rebar 1 as shown in Fig. 12A, the exposed rebar may appear flat in the point cloud data as shown in Fig. 12B. In this case, the presence or absence of the exposed rebar cannot be determined from the point cloud data alone. Therefore, there is a need for a technology that can detect the exposed rebar of a reinforced concrete structure from point cloud data.

[0009] The object of the present disclosure, made in consideration of the above-mentioned problems, is to provide a detection device that can detect exposed reinforcement portions of reinforced concrete structures from point cloud data.

[0010] A detection device in one embodiment is a detection device that detects exposed reinforcement portions in a reinforced concrete structure, and includes an acquisition unit that acquires point cloud data of the surface of the reinforced concrete structure and the reflectivity of laser light on the surface of the reinforced concrete structure, a calculation unit that calculates the average roughness and maximum roughness depth of the surface of the reinforced concrete structure based on the point cloud data, and a detection unit that detects exposed reinforcement portions on the surface of the reinforced concrete structure based on the average roughness, the maximum roughness depth, and the reflectivity.

[0011] According to the present disclosure, exposed reinforcement portions of reinforced concrete structures can be detected from point cloud data.

[0012] 1A is a diagram showing an example of the configuration of a detection device according to an embodiment of the present disclosure; FIG. 1B is a diagram showing an example of average roughness Ra and maximum roughness depth Rz of concrete and corroded reinforcing bar; FIG. 1C is a diagram showing an example of reflectivity of concrete and corroded reinforcing bar; FIG. 1D is a diagram showing an example of reflectivity of corroded reinforcing bar depending on the presence or absence of salt damage; FIG. 1E is a diagram showing an example of a determination table used by the detection unit shown in FIG. 1 to detect exposed reinforcement portions; FIG. 1F is a diagram for explaining estimation of reinforcing bar diameter by the estimating unit shown in FIG. 1, showing an example of the relationship between the reinforcing bar diameter of an exposed reinforcement portion and the reinforcing bar diameter of a non-corroded reinforcing bar in the exposed reinforcement portion; FIG. 1G is a flowchart showing an example of the operation of the detection device shown in FIG. 1G; FIG. 1H is a diagram showing an example of the operation of the estimating unit shown in FIG. 1G; FIG. 1H is a diagram showing an example of the configuration of a computer functioning as a detection device according to the present disclosure; FIG. 1I is a diagram showing an example of an exposed reinforcement portion; FIG. 1I is a diagram showing an example of point cloud data of the exposed reinforcement portion shown in FIG. 11A; FIG. 1I is a diagram showing another example of an exposed reinforcement portion; FIG. 12A is a diagram showing an example of point cloud data of the exposed reinforcement portion shown in FIG.

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0014] 1 is a diagram illustrating an example of the configuration of a detection device 100 according to an embodiment of the present disclosure. The detection device 100 according to the present disclosure is a device that detects exposed reinforcement portions in a reinforced concrete structure. The detection device 100 according to the present embodiment creates a model of the reinforced concrete structure for FEM analysis or the like based on point cloud data, the detection results of the exposed reinforcement portions, and the like.

[0015] As shown in FIG. 1 , the detection device 100 according to this embodiment includes an acquisition unit 101, a position coordinate storage unit 102, a reflectance storage unit 103, a Zave calculation unit 104, an Ra and Rz calculation unit 105 as a calculation unit, a detection unit 106, an exposed bar information storage unit 107, an estimation unit 108, a reinforcing bar diameter storage unit 109, and a model creation unit 110.

[0016] The acquisition unit 101 acquires point cloud data of the surface of a reinforced concrete structure and the reflectance of laser light on the surface of the reinforced concrete structure. The point cloud data is acquired using, for example, LiDAR. LiDAR generally uses laser light with wavelengths of 905 nm and 1550 nm. The reflectance can be measured by receiving the light reflected from the surface of the reinforced concrete structure by these laser lights. The reflectance is measured, for example, for each point constituting the point cloud data. The acquisition unit 101 outputs the acquired point cloud data to the position coordinate storage unit 102 and the model creation unit 110. The acquisition unit 101 also outputs the acquired reflectance to the reflectance storage unit 103.

[0017] The position coordinate storage unit 102 stores the position coordinates of each point constituting the point cloud data output from the acquisition unit 101 .

[0018] The reflectance storage unit 103 stores the reflectance output from the acquisition unit 101 .

[0019] The Zave calculation unit 104 calculates the average value Zave of the heights of each point included in each predetermined region on the surface of the reinforced concrete structure (height of the unevenness on the surface of the reinforced concrete) based on the position coordinates of each point constituting the point cloud data stored in the position coordinate storage unit 102. If the total number of points in the region is N and the height of each point in the region is Zi, the Zave calculation unit 104 calculates the average value Zave based on the following formula (1):

[0020]

[0021] The Zave calculation unit 104 outputs the calculation result of the average value Zave to the Ra, Rz calculation unit 105 .

[0022] The Ra, Rz calculation unit 105 calculates the average roughness Ra (Roughness Average) and maximum roughness depth Rz (Maximum Roughness Depth) of the surface of the reinforced concrete structure based on the point cloud data.

[0023] The average roughness Ra is a value indicating the average roughness of the surface of a reinforced concrete structure, and is calculated as the average value of the absolute deviation of the height of each point within a predetermined area on the surface of the reinforced concrete structure. That is, the Ra, Rz calculation unit 105 calculates the average roughness Ra based on the following equation (2).

[0024]

[0025] The maximum roughness depth Rz is the maximum value of the depth in a predetermined area on the surface of a reinforced concrete structure, and is the difference between the height of the highest point (maximum height) and the height of the lowest point (minimum height) among all points in the predetermined area. ｍａｘ Let the minimum height be z ｍｉｎ Then, the Ra, Rz calculation unit 105 calculates the maximum roughness depth Rz based on the following formula (3).

[0026]

[0027] The Ra, Rz calculation unit 105 outputs the calculation results of the average roughness Ra and the maximum roughness depth Rz to the detection unit 106 .

[0028] The detection unit 106 detects exposed reinforcement portions on the surface of the reinforced concrete structure based on the average roughness Ra, the maximum roughness depth Rz, and the reflectance stored in the reflectance storage unit 103. Detection of exposed reinforcement portions by the detection unit 106 will be described below.

[0029] It is known that the ranges of average roughness Ra and maximum roughness depth Rz differ between concrete and corroded rebar. For example, as shown in Figure 2, it is known that the average roughness Ra of concrete is 250 μm or more and the maximum roughness depth Rz is 2000 μm or more (see Reference 1). It is also known that the average roughness Ra of corroded rebar is 150 μm or less and the maximum roughness depth Rz is 1300 μm or less (see Reference 2). [Reference 1] Kato et al., "Investigation Method for Wear of Irrigation Canal Concrete," Annual Proceedings of the Japan Concrete Institute, Vol. 31 (jci-net.or.jp), [online], [searched August 14, 2024], Internet<URL:https: / / data.jci-net.or.jp / data_pdf / 31 / 031-01-1149.pdf> [Reference 2] Miura et al., Corrosion evaluation of weather-resistant bridges using surface roughness measurement, [online], [searched August 14, 2024], Internet<URL:http: / / library.jsce.or.jp / jsce / open / 00035 / 2012 / 67-01 / 67-01-0155.pdf>

[0030] It is also known that concrete and corroded rebar reflect laser light differently. For example, as shown in Figure 3, concrete is known to have a reflectance of 40% or less for laser light with a wavelength of 1550 nm (see Reference 3). Corroded rebar is known to have a reflectance of 80% or more for laser light with a wavelength of 1550 nm (see Reference 4). [Reference 3] Arita et al., "Concrete Deterioration Methods Using Hyperspectral Remote Sensing," [online], [searched August 14, 2024], Internet<URL: https: / / www.jstage.jst.go.jp / article / seisankenkyu / 53 / 11 / 53_11_615 / _pdf / -char / ja> [Reference 4] Kobayashi et al., Application of hyperspectral analysis to corrosion resistance evaluation of steel materials in atmospheric environments, [online], [searched August 14, 2024], Internet<URL: https: / / www.jstage.jst.go.jp / article / jcorr / 70 / 11 / 70_354 / _pdf / -char / ja>

[0031] The detection unit 106 compares these values ​​with the average roughness Ra, maximum roughness depth Rz, and reflectance of the reinforced concrete structure to detect exposed reinforcement portions on the surface of the reinforced concrete structure.

[0032] The average roughness Ra and maximum roughness depth Rz differ depending on the materials of the concrete and reinforcing bars. Therefore, it is preferable to measure the average roughness Ra and maximum roughness depth Rz of the concrete and exposed reinforcing bars of the reinforced concrete structure to be detected for exposed reinforcing bars, and use the measured values ​​as the threshold values ​​for detecting exposed reinforcing bars. If measurement is difficult, for example, the values ​​shown in Figure 2 may be used as the threshold values ​​for detecting exposed reinforcing bars.

[0033] Furthermore, the reflectivity of laser light varies depending on the materials of concrete and reinforcing bars. Therefore, it is preferable to measure the reflectivity of laser light from the concrete and exposed reinforcing bars of a reinforced concrete structure, which is the target of exposed reinforcing bars detection, and use the measured value as the threshold for detecting exposed reinforcing bars. If measurement is difficult, for example, the values ​​shown in Figure 3 may be used as the threshold for detecting exposed reinforcing bars.

[0034] When rebar corrodes due to chlorides, tetragonal β-FeOOH is produced. It is known that β-FeOOH has a different reflectance than α-FeOOH and γ-FeOOH, which are produced when rebar corrodes without chlorides (Reference 4). Therefore, the values ​​shown in Figure 4 may be used as thresholds for detecting exposed reinforcement, depending on whether the reinforced concrete structure to be detected for exposed reinforcement is in a salt-damaged environment.

[0035] An example in which the values ​​shown in FIGS. 2 and 3 are used as threshold values ​​for detecting exposed line portions will be described below.

[0036] The detection unit 106 compares the average roughness Ra, maximum roughness depth Rz, and reflectance for each predetermined range of the reinforced concrete structure with the thresholds for detecting exposed reinforcement for each of the average roughness Ra, maximum roughness depth Rz, and reflectance described with reference to Figures 2 and 3, and determines whether the predetermined range is exposed reinforcement or concrete. The detection unit 106 determines, for each predetermined range of the reinforced concrete structure, whether the predetermined range is exposed reinforcement or concrete, for example, by referring to the determination table shown in Figure 5.

[0037] 5, the detection unit 106 determines that a predetermined area is concrete if, for example, the average roughness Ra of the predetermined area is 250 μm or more, the reflectance of the predetermined area is 80% or more, and the maximum roughness depth Rz of the predetermined area is 2000 μm or more. Also, the detection unit 106 determines that a predetermined area is exposed reinforcement if, for example, the average roughness Ra of the predetermined area is 250 μm or more, the reflectance of the predetermined area is 80% or more, and the maximum roughness depth Rz of the predetermined area is less than 2000 μm.

[0038] Depending on the finish condition of the concrete surface (such as junk), the range of the average roughness Ra of concrete may overlap with the range of the average roughness Ra of corroded rebar. Therefore, it is difficult to detect exposed rebar using only the average roughness Ra. Furthermore, corroded rebar often contains moisture. Therefore, the range of reflectance of corroded rebar may overlap with the range of reflectance of concrete. Therefore, it is difficult to detect exposed rebar using only the reflectance.

[0039] On the other hand, in this embodiment, the detection unit 106 detects exposed reinforcement in a reinforced concrete structure based on a plurality of parameters (average roughness Ra, maximum roughness depth Rz, and reflectance), thereby enabling more accurate detection of exposed reinforcement.

[0040] As shown in FIG. 5, the detection unit 106 determines whether the portion is an exposed reinforcement or concrete without referring to the value of the maximum roughness depth Rz in the following cases: the average roughness Ra is 250 μm or more and the reflectivity is less than 80%; the average roughness Ra is greater than 150 μm and less than 250 μm and the reflectivity is 40% or less; the average roughness Ra is greater than 150 μm and less than 250 μm and the reflectivity is 80% or more; and the average roughness Ra is 150 nm or less and the reflectivity is less than 40%.

[0041] In this way, the detection unit 106 refers to the average roughness Ra and the reflectance first, and when it is not possible to make a determination based on the average roughness Ra and the reflectance alone, it refers to the maximum roughness depth Rz. In other words, when it is not possible to determine whether or not a part is an exposed streak part based on the average roughness Ra and the reflectance, the detection unit 106 further uses the maximum roughness depth Rz to detect the exposed streak part.

[0042] It is generally known that the maximum roughness depth Rz varies widely (References 1 and 2). Therefore, consistently using the maximum roughness depth Rz may result in inaccurate detection of exposed lines. On the other hand, as in the present embodiment, when priority is given to the average roughness Ra and reflectance and it is not possible to determine whether or not an exposed line is present based on the average roughness Ra and reflectance, the maximum roughness depth Rz is also used to detect the exposed line, thereby improving the accuracy of detecting the exposed line.

[0043] In addition, it may not be possible to determine whether a surface is concrete based on the average roughness Ra, maximum roughness depth Rz, and reflectance. For example, in the example shown in Figure 5, if the average roughness Ra is 250 μm or more, the reflectance is 80% or more, and the maximum roughness depth Rz is less than 2000 μm, the average roughness Ra falls within the average roughness range of concrete, the reflectance falls within the reflectance range of corroded rebar, and the maximum roughness depth Rz partially overlaps with the reflectance range of corroded rebar. In this case, it is difficult to determine whether the surface is exposed reinforcement or concrete.

[0044] As in the example described above, the detection unit 106 may determine that a portion that cannot be determined to be concrete is an exposed reinforcement portion based on the average roughness Ra, the maximum roughness depth Rz, and the reflectance. This makes it possible to prevent the exposed reinforcement portion from being overlooked.

[0045] Referring back to FIG. 1 , the detection unit 106 stores exposed bar information indicating the position of the detected exposed bar portion, the diameter of the reinforcing bar in the exposed bar portion, etc. in the exposed bar information storage unit 107 .

[0046] The estimation unit 108 estimates the rebar diameter of the uncorroded rebar in the exposed reinforcement portion based on the rebar diameter of the rebar in the exposed reinforcement portion stored in the exposed reinforcement information storage unit 107 and the rebar diameter of the rebar in the reinforced concrete structure when it is in good condition.

[0047] When evaluating the strength of reinforced concrete structures, it is necessary to determine the extent to which uncorroded rebar exists. While it is possible to determine the extent to which uncorroded rebar exists by chipping away the corroded areas on-site and measuring the diameter of the rebar inside, this is inefficient. Furthermore, it is not possible to directly obtain information on uncorroded rebar from point cloud data.

[0048] There are several methods for modeling reinforced concrete structures for structural analysis, but the most common method is to model rebars as embedded elements. This method requires information such as the strength, stiffness, and size of the rebars. The rebar size is generally expressed as the diameter of the rebar, as it is expressed as a cylindrical shape.

[0049] If the rebar is sound (non-corroded), it can be modeled using information about the rebar used in the reinforced concrete structure. On the other hand, for corroded rebar, the amount of wall thinning is taken into account and the rebar diameter of the uncorroded part is often used. However, because exposed rebar is surrounded by corrosion products, it is not possible to directly obtain the rebar diameter of uncorroded rebar in exposed parts from point cloud data.

[0050] Therefore, when creating a model of a reinforced concrete structure, it is possible to set the diameter of the rebar in the exposed reinforcement to 0. In this case, the strength of the reinforced concrete structure will not be overestimated. However, even if there is only a small amount of exposed reinforcement, it will be evaluated as if there is no uncorroded rebar, so there is a possibility that the strength of the reinforced concrete structure will be underestimated.

[0051] On the other hand, in this embodiment, the reinforcing bar diameter of the uncorroded reinforcing bar in the exposed reinforcement portion is estimated by the estimation unit 108. The following describes the estimation of the reinforcing bar diameter of the uncorroded reinforcing bar in the exposed reinforcement portion by the estimation unit 108. In addition, the following description assumes that the reinforcing bar diameter of the reinforcing bar in the concrete structure when it is in good condition is known.

[0052] It is generally known that the volume expansion rate of rebars when corroded is 2.5 times (Reference 5). As shown in Figure 6, if the diameter (radius) of a rebar when sound is r, the reduction in the diameter of the rebar due to corrosion is a, and the increase in the diameter of the rebar due to corrosion products is b, then the area of ​​metal loss due to corrosion × 2.5 = area of ​​corrosion products. In other words, the following equation (4) holds true: {r ２ π-(r-a) ２ π}×2.5=(r+b) ２ π-(r-b) ２ π Equation (4) [Reference 5] Tsutsumi et al., Study on modeling of crack width due to corrosion products, [online], [Retrieved August 14, 2024], Internet <URL: https: / / www.jstage.jst.go.jp / article / jscej1984 / 1998 / 585 / 1998_585_69 / _pdf / -char / ja>

[0053] When equation (4) is solved for b, the following equation (5) is obtained.

[0054]

[0055] The rebar diameter of the exposed rebar obtained from the point cloud data is r + b. The rebar diameter of the uncorroded rebar in the exposed rebar is r - a. For example, if the rebar diameter r when sound is 2 cm, the rebar diameter of the exposed rebar (r + b) and the rebar diameter of the uncorroded rebar in the exposed rebar (r - a) have the relationship shown in Figure 7.

[0056] The estimation unit 108 acquires the rebar diameter (r+b) of the exposed reinforcement from the point cloud data. The estimation unit 108 also substitutes the rebar diameter r of the healthy rebar into the above-mentioned formula (5) to derive the relationship ( FIG. 7 ) between the rebar diameter (r+b) of the exposed reinforcement and the rebar diameter (r−a) of the uncorroded rebar in the exposed reinforcement. The estimation unit 108 then estimates the rebar diameter (r−a) of the uncorroded rebar in the exposed reinforcement based on the derived relationship.

[0057] Referring back to FIG. 1 , the estimation unit 108 stores the estimated reinforcing bar diameter of the uncorroded reinforcing bar in the exposed reinforcing bar portion in the reinforcing bar diameter storage unit 109 .

[0058] The model creation unit 110 creates a model of the reinforced concrete structure. Specifically, the model creation unit 110 creates a model that mimics the shape of the reinforced concrete structure based on the point cloud data output from the acquisition unit 101. Then, the model creation unit 110 reflects the estimated rebar diameters of uncorroded rebars in exposed rebar portions, which are stored in the rebar diameter storage unit 109, at positions on the model that correspond to the exposed rebar portions.

[0059] Next, a description will be given of the operation of the detection device 100 according to this embodiment. Fig. 8 is a flowchart showing an example of the operation of the detection device 100 according to this embodiment, and is a diagram for explaining a detection method executed by the detection device 100 according to this embodiment.

[0060] The acquisition unit 101 acquires point cloud data of a reinforced concrete structure in which exposed reinforcement portions are to be detected and the reflectance of laser light on the surface of the reinforced concrete structure (step S11).

[0061] The position coordinate storage unit 102 stores the position coordinates Zi of each point constituting the point cloud data acquired by the acquisition unit 101 (step S12).

[0062] The Zave calculation unit 104 calculates the average value Zave of the heights of each point included in each predetermined area on the surface of the reinforced concrete structure based on the position coordinates of each point constituting the point cloud data (step S13). The Zave calculation unit 104 calculates the average value Zave based on the above-mentioned formula (1).

[0063] The Ra, Rz calculation unit 105 calculates the average roughness Ra and the maximum roughness depth Rz of the surface of the reinforced concrete structure (step S14). The Ra, Rz calculation unit 105 calculates the average roughness Ra based on the above-mentioned formula (2). The Ra, Rz calculation unit 105 also calculates the maximum roughness depth Rz based on the above-mentioned formula (3).

[0064] The detection unit 106 detects exposed reinforcement on the surface of the reinforced concrete structure based on the average roughness Ra, the maximum roughness depth Rave, and the reflectance (step S15). The detection unit 106 detects exposed reinforcement on the surface of the reinforced concrete structure based on a determination table such as that shown in FIG.

[0065] The estimation unit 108 estimates the diameter of the reinforcing bars that are not corroded in the exposed reinforcement portion based on the width of the detected exposed reinforcement portion and the reinforcing bar diameter r of the reinforcing bars in the reinforced concrete structure when they are in good condition (step S16). Figure 9 is a flowchart showing an example of the operation of the estimation unit 108.

[0066] The estimation unit 108 acquires the reinforcing bar diameter (r+b) of the exposed reinforcement portion from the point cloud data. The estimation unit 108 also acquires the reinforcing bar diameter r of the reinforced concrete structure when the reinforcing bar is in good condition (step S161).

[0067] The estimation unit 108 substitutes the rebar diameter r when the steel is in good condition into the above-mentioned equation (5) and derives the relationship between the rebar diameter (r+b) of the exposed rebar and the rebar diameter (r-a) of the uncorroded rebar in the exposed rebar (step S162).

[0068] The estimation unit 108 substitutes the width (r+b) of the exposed reinforcement portion into the derived relationship to estimate the rebar diameter (r−a) of the uncorroded rebar in the exposed reinforcement portion (step 163).

[0069] The estimation unit 108 stores the reinforcing bar diameter (r−a) of the uncorroded reinforcing bar in the exposed reinforcing bar portion in the reinforcing bar diameter storage unit 109, and ends the process.

[0070] 8 , the model creation unit 110 creates a model of the reinforced concrete structure (step S17). Specifically, the model creation unit 110 creates a model that mimics the shape of the reinforced concrete structure based on the point cloud data. The model creation unit 110 then reflects the estimated rebar diameters of the uncorroded rebars in the exposed reinforcement portions at positions on the model that correspond to the exposed reinforcement portions.

[0071] As described above, the detection device 100 according to this embodiment includes an acquisition unit 101, an Ra / Rz calculation unit 105 as a calculation unit, and a detection unit 106. The acquisition unit 101 acquires point cloud data of the surface of a reinforced concrete structure and the reflectance of laser light on the surface of the reinforced concrete structure. The Ra / Rz calculation unit 105 calculates the average roughness Ra and maximum roughness depth Rz of the surface of the reinforced concrete structure based on the point cloud data. The detection unit 106 detects exposed reinforcement on the surface of the reinforced concrete structure based on the average roughness Ra, maximum roughness depth Rz, and reflectance.

[0072] By using the average roughness Ra, maximum roughness depth Rz, and reflectance, exposed reinforcement parts of reinforced concrete structures can be detected from point cloud data.

[0073] A test was conducted to evaluate the accuracy of detecting exposed reinforcement using the detection device 100 according to the present disclosure. A concrete specimen containing exposed reinforcement was prepared as a sample for the test. The exposed reinforcement was a rectangle measuring approximately 5 × 10 cm. The average roughness Ra, maximum roughness depth Rz, and reflectance were measured at 10 locations each on the concrete and exposed reinforcement of the concrete specimen. The average roughness Ra and maximum roughness depth Rz were measured using a surface roughness measuring device. The reflectance was measured by irradiating the sample with a laser beam having a wavelength of 1550 nm from a position 3 cm from the surface. A total of 20 locations, 10 locations each on the concrete and exposed reinforcement of the concrete specimen, were determined to be exposed reinforcement or concrete, and the results were evaluated.

[0074] In Comparative Example 1, exposed reinforcement was detected based only on the average roughness Ra. In Comparative Example 1, the number of correct answers was 10. In Comparative Example 1, incorrect answers were obtained due to the presence of pores in the concrete.

[0075] In Comparative Example 2, exposed reinforcement portions were detected based only on the maximum roughness depth Rz. In Comparative Example 2, the number of correct answers was 6. In Comparative Example 2, the maximum and minimum height values ​​of each point within a specified area on the surface of the reinforced concrete structure were used, so the maximum roughness depth Rz often took on extreme values ​​(outliers), and incorrect answers were obtained in areas where the maximum roughness depth Rz was an outlier.

[0076] In Comparative Example 3, exposed reinforcement was detected based only on reflectance. In Comparative Example 3, the number of correct answers was 10. In Comparative Example 3, incorrect answers were obtained in areas with severe corrosion. In Comparative Examples 1 to 3, if the values ​​of the average roughness Ra, maximum roughness depth Rz, and reflectance were not within the range corresponding to concrete or the range corresponding to exposed reinforcement, the area was determined to be exposed reinforcement.

[0077] In Comparative Example 4, exposed streaks were detected based on the average roughness Ra and the maximum roughness depth Rz. In Comparative Example 4, the number of correct answers was 10. In Comparative Example 4, the correct answer rate did not increase due to the influence of the maximum roughness depth Rz, which often becomes an outlier.

[0078] In Comparative Example 5, the exposed streaks were detected based on the reflectance and the maximum roughness depth Rz. In Comparative Example 5, the number of correct answers was 10. In Comparative Example 5, the correct answer rate did not increase due to the influence of the maximum roughness depth Rz, which often becomes an outlier.

[0079] Next, in Example 1 according to this embodiment, the exposed streaks were detected based on the average roughness Ra and the reflectance. In Example 1, the number of correct answers was 14. In Example 1, the exposed streaks could be correctly detected based on at least one of the average roughness Ra and the reflectance.

[0080] In Example 2 according to this embodiment, exposed reinforcement was detected based on the average roughness Ra, maximum roughness depth Rz, and reflectance. In Example 2, the number of correct answers was 18. In Example 2, an incorrect answer was given when there was a false detection based on the maximum roughness depth Rz. In Comparative Examples 4 and 5 and Examples 1 and 2, a determination was made as to whether the object was concrete, exposed reinforcement, or neither for each of the parameters of average roughness Ra, maximum roughness depth Rz, and reflectance, and the detection result was determined by majority vote.

[0081] In Example 3 according to this embodiment, the average roughness Ra and reflectance were given priority, and when a determination could not be made based on the average roughness Ra and reflectance alone, the maximum roughness depth Rz was used to detect exposed streaks. In Example 3, the number of correct answers was 20.

[0082] The detection apparatus 100 described above can be realized by a computer 20 shown in FIG. 10. A program for causing the computer 20 to function as the detection apparatus 100 may be provided. The program may be stored in a storage medium or provided via a network. FIG. 10 is a block diagram showing a schematic configuration of the computer 20 functioning as the detection apparatus 100. The computer 20 may be a general-purpose computer, a dedicated computer, a workstation, a PC (Personal Computer), an electronic notepad, or the like. The program instructions may be program code, code segments, or the like for performing necessary tasks.

[0083] 10 , the computer 20 includes a processor 21, a ROM (Read Only Memory) 22, a RAM (Random Access Memory) 23, a storage 24, an input unit 25, a display unit 26, and a communication interface (I / F) 27. Each component is communicably connected to one another via a bus 29. The processor 21 is specifically a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), a SoC (System on a Chip), or the like, and may be configured with multiple processors of the same or different types.

[0084] The processor 21 is a control unit that controls each component and performs various arithmetic processing. That is, the processor 21 reads a program from the ROM 22 or the storage 24 and executes the program using the RAM 23 as a work area. The processor 21 controls each component and performs various arithmetic processing according to the program stored in the ROM 22 or the storage 24. In this embodiment, the ROM 22 or the storage 24 stores a program for causing the computer 20 to operate as the detection device 100 according to the present disclosure. The program is read and executed by the processor 21 to realize each component of the detection device 100, such as the acquisition unit 101, the Zave calculation unit 104, the Ra and Rz calculation unit 105, the detection unit 106, and the rebar diameter calculation unit 108.

[0085] The program may be provided in a form stored on a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), a USB (Universal Serial Bus) memory, etc. The program may also be provided in a form downloaded from an external device via a network.

[0086] The ROM 22 stores various programs and various data. The RAM 23 serves as a working area and temporarily stores programs or data. The storage 24 is configured with an HDD (Hard Disk Drive) or SSD (Solid State Drive) and stores various programs and data including an operating system. The RAM 23 and storage 24 store, for example, the position coordinates of each point constituting the point cloud data, reflectance, exposed rebar information, and rebar diameter.

[0087] The input unit 25 includes a pointing device such as a mouse and a keyboard, and is used to input various types of information.

[0088] The display unit 26 is, for example, a liquid crystal display, and displays various information. The display unit 26 may be a touch panel type and function as the input unit 25.

[0089] The communication interface 27 is an interface for communicating with other devices, for example, an interface for a LAN.

[0090] The following additional notes are provided regarding the above-described embodiments.

[0091] [Supplementary Item 1] A detection device for detecting exposed reinforcement portions in a reinforced concrete structure, comprising a control unit, wherein the control unit is configured to: acquire point cloud data of the surface of the reinforced concrete structure and the reflectivity of laser light on the surface of the reinforced concrete structure; calculate the average roughness and maximum roughness depth of the surface of the reinforced concrete structure based on the point cloud data; and detect exposed reinforcement portions on the surface of the reinforced concrete structure based on the average roughness, the maximum roughness depth, and the reflectivity.

[0092] [Supplementary Item 2] In the detection device described in Supplementary Item 1, when it is not possible to determine whether or not a portion is an exposed streak portion based on the average roughness and the reflectance, the control unit further uses the maximum roughness depth to detect the exposed streak portion.

[0093] [Supplementary Item 3] In the detection device according to Supplementary Item 1 or 2, the control unit determines that a portion that cannot be determined to be concrete is an exposed reinforcement portion based on the average roughness, the maximum roughness depth, and the reflectance.

[0094] [Appendix 4] In the detection device described in any one of appendixes 1 to 3, the control unit estimates the reinforcing bar diameter of the uncorroded reinforcing bar in the exposed reinforcing bar portion based on the width of the exposed reinforcing bar portion and the reinforcing bar diameter of the reinforcing bar in the reinforced concrete structure when it is in good condition.

[0095] [Appendix 5] A detection method performed by a detection device that detects exposed reinforcement portions in a reinforced concrete structure, the detection method comprising: acquiring point cloud data of the surface of the reinforced concrete structure and the reflectivity of laser light on the surface of the reinforced concrete structure; calculating the average roughness and maximum roughness depth of the surface of the reinforced concrete structure based on the point cloud data; and detecting exposed reinforcement portions on the surface of the reinforced concrete structure based on the average roughness, the maximum roughness depth, and the reflectivity.

[0096] [Supplementary Item 6] A non-transitory storage medium storing a program executable by a computer, the non-transitory storage medium storing the program causing the computer to operate as the detection device described in any one of Supplementary Items 1 to 4.

[0097] Although the above-described embodiments have been described as typical examples, it will be apparent to those skilled in the art that many modifications and substitutions can be made within the spirit and scope of the present disclosure. Therefore, the present invention should not be interpreted as being limited by the above-described embodiments, and various modifications and alterations are possible without departing from the scope of the claims. For example, multiple building blocks shown in the block diagrams of the embodiments can be combined into one, or one building block can be divided.

[0098] REFERENCE SIGNS LIST 100 Detection device 101 Acquisition unit 102 Position coordinate storage unit 103 Reflectance storage unit 104 Zave calculation unit 105 Ra, Rz calculation unit (calculation unit) 106 Detection unit 107 Exposed bar information storage unit 108 Reinforcement bar diameter calculation unit 109 Reinforcement bar diameter storage unit 110 Model creation unit 20 Computer 21 Processor 22 ROM 23 RAM 24 Storage 25 Input unit 26 Display unit 27 Communication I / F 29 Path

Claims

1. A detection device for detecting exposed reinforcement on a reinforced concrete structure, comprising: an acquisition unit that acquires point cloud data on the surface of the reinforced concrete structure and the reflectivity of laser light on the surface of the reinforced concrete structure; a calculation unit that calculates the average roughness and maximum roughness depth of the surface of the reinforced concrete structure based on the point cloud data; and a detection unit that detects exposed reinforcement on the surface of the reinforced concrete structure based on the average roughness, the maximum roughness depth, and the reflectivity.

2. A detection device according to claim 1, wherein the detection unit further uses the maximum roughness depth to detect exposed streaks when it is not possible to determine whether or not the exposed streaks are present based on the average roughness and the reflectance.

3. A detection device according to claim 1, wherein the detection unit determines that a portion that cannot be determined to be concrete is an exposed reinforcement portion based on the average roughness, the maximum roughness depth, and the reflectance.

4. A detection device as claimed in claim 1, further comprising an estimation unit that estimates the diameter of the uncorroded reinforcing bars in the exposed reinforcing bars based on the width of the exposed reinforcing bars and the diameter of the reinforcing bars in the reinforced concrete structure when they are in good condition.

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