A method for concrete irregular crack characterization and calculation

Abstract

The present application belongs to the technical field of concrete crack detection, and particularly relates to a concrete irregular crack characterization and calculation method. The concrete irregular crack characterization and calculation method comprises the following steps: preparing a concrete test piece and performing a concrete thin plate crack resistance test on the test piece; performing surface treatment on the concrete test piece after the crack resistance test, and spraying white self-spraying paint; taking a photo of the concrete test piece to obtain a concrete test piece image; image cropping and preprocessing; image processing and conversion; crack information extraction and calculation, counting the number of all crack pixel points in the concrete test piece image, and determining the cracking area and crack width of the cracks. The present application is simple to operate, highly convenient and highly intelligent, and only needs to take a photo of the concrete thin plate surface crack morphology by using a mobile phone, a camera or the like to determine the concrete crack cracking area, which is more comprehensive and more accurate compared with the crack calculation result in the existing specification.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of concrete crack detection, and particularly relates to a concrete irregular crack characterization and calculation method. BACKGROUND

[0002] Cracks are an important indicator for measuring the durability and safety of concrete structures, and timely and accurate inspection of cracks on the surface of concrete is a very important work. The hydraulic concrete test regulation (DL / T 5150-2017 and SL / T 352-2020) indicates that the early crack resistance of concrete can be determined by using the plane thin plate test method, which uses a microscope to manually observe and read the crack width and length, and evaluates the crack resistance of the test piece by the average cracking area and the number of cracks per unit area. Among them, the average cracking area of each crack is calculated by the maximum width of the crack, but the crack dispersion calculated by this method is too large, and it is difficult to reflect the real crack resistance of the concrete material.

[0003] On the other hand, with the maturity of the application technology of fibers in concrete, a series of new concrete materials with excellent crack resistance have been gradually applied in practical engineering, such as ultra-high performance concrete (UHPC), engineering cement-based composite material (ECC) and self-compacting concrete (SCC), etc. The cracks are multiple-point tiny irregular cracks, and the crack width is often in microns. The precision of crack width and crack area in the specification is 0.01mm 2 , which is difficult to accurately quantify the crack information of such concrete materials. SUMMARY

[0004] In view of the above problems, the purpose of the present application is to provide a concrete irregular crack characterization and calculation method, which is simple, convenient and intelligent. Only by taking pictures of the crack morphology on the surface of the concrete thin plate with a mobile phone, a camera or other devices, the cracking area of the concrete crack can be determined, which is more comprehensive and more accurate than the crack calculation results in the existing specification.

[0005] The technical scheme of the present application is as follows: a concrete irregular crack characterization and calculation method, comprising the following steps:

[0006] S1: preparing a concrete test piece and performing a concrete thin plate crack resistance test on it;

[0007] S2: performing surface treatment on the concrete test piece after the crack resistance test, using white self-spraying paint to cover the non-crack information such as honeycomb pitting, oil stains and stains on the surface of the concrete test piece, and highlighting the crack direction and details;

[0008] S3: taking pictures of the concrete test piece after the surface treatment in step S2 to obtain the image of the concrete test piece;

[0009] S4: image cropping and preprocessing, cropping the concrete specimen image, only keeping the concrete specimen surface area;

[0010] S5: image processing and conversion, image enhancement and image noise reduction filtering processing are performed on the cropped specimen image;

[0011] S6: crack image extraction, after image enhancement and image noise reduction filtering processing, the crack appears black, the crack image is mathematically segmented to obtain a crack binary image with only black and white pixels;

[0012] S7: crack information extraction and calculation, a coordinate system is established on the crack image to discretely count the crack information, i.e. the digital image crack length direction is taken as the x direction, and the direction perpendicular to the crack length direction is taken as the y direction, a discrete statistical method is used, each pixel column in the y direction of the image is taken as a reference line, the number of pixels in the y axis direction of the crack image is m, the number of pixels in the x axis direction is n, the black and white pixels of the binary image correspond to the 0 value and 1 value in the digital matrix respectively, the crack pixels in the digital image, i.e. the white pixels, are scanned and identified point by point in the order from top to bottom and from left to right, the specific identification principle is: if the pixel point D(x, y) at the pixel coordinate (x, y) is 1, the point represents a crack pixel; if D(x, y) = 0, it is a non-crack pixel, i.e. the substrate part of the concrete surface not cracked, between the two white pixels above and below each reference line is a crack, finally the data in the scanning process is extracted, the label value of each crack on the x reference line can be obtained n x , and the number of crack pixel points corresponding to each label, the average crack width W mean is calculated according to the following formula:

[0013] (1)

[0014] (2)

[0015] In the formula, L is the actual length of the specimen in the image, mm; m is the number of pixels contained in the specimen length L, mm; x is the number of the reference line; n is the total number of pixel columns, n x is the label value of the crack on the x reference line; W mean is the average crack width, δ is the actual width of each pixel point,Q xi For the first x The number of pixels contained in the first i Crack label of the reference line;

[0016] The number of all crack pixel points in the concrete specimen image is counted, and the cracking area of the crack is determined. The cracking area of a single crack of a concrete specimen is calculated according to the following formula:

[0017] (3)

[0018] In the formula: a i The area of the i-th crack; l i The total number of pixel points of the i-th crack;

[0019] The total cracking area of the surface of the concrete specimen is calculated according to the following formula:

[0020] (4)

[0021] In the formula: c The total cracking area of the surface of the concrete specimen; n The total number of cracks.

[0022] In the step S1, the concrete specimen is formed, and then fan ventilation is adopted for at least 24 hours.

[0023] In the step S1, the concrete thin plate anti-cracking test is carried out according to the specification DL / T 5150-2017.

[0024] In the step S3, the concrete specimen is photographed, the position of the photographing equipment is adjusted according to the focal length and resolution, and the light supplement measure is taken according to the test environment to ensure the clarity of the crack image.

[0025] In the step S5, the image enhancement is adjusted by the imadjust function in MATLAB, and the gray value [0.25, 0.60] in the original image is mapped to [0, 1], and the image noise reduction filter is realized by the imbothat function in MATLAB and the gussisian function in the Gaussian filter.

[0026] In the step S6, the crack image is subjected to mathematical morphology segmentation by the imreconstruct function in MATLAB, the threshold value of large cracks is 20, and the threshold value of small cracks is 160. The crack image with the threshold value adjusted to 160 is used as the mask image in the imreconstruct function, and the crack image with the threshold value adjusted to 20 is used as the marker image in the imreconstruct function, and finally the crack binary image with only black and white pixels is obtained.

[0027] In the crack image extraction in the step S6, the concrete specimen image includes black and white colors, wherein the crack of the concrete specimen is a white pixel point, and the base material of the concrete specimen is a black pixel point.

[0028] The technical effect of the present application is that the present application is simple and convenient, and has high intelligence. The crack area and width of the concrete can be determined by taking a picture of the crack shape on the surface of the concrete thin plate through a mobile phone, a camera or the like, and the result is more comprehensive and accurate compared with the crack calculation result in the existing specification.

[0029] Further description will be made below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 It is a surface photo of the concrete specimen of the present application.

[0031] Figure 2 It is a concrete specimen image photo of the present application. DETAILED DESCRIPTION Embodiment 1

[0032] A concrete irregular crack characterization and calculation method comprises the following steps:

[0033] S1: making a concrete specimen and performing a concrete thin plate crack resistance test on the same;

[0034] S2: performing surface treatment on the concrete specimen after the crack resistance test, using white self-spraying paint to spray, covering the non-crack information such as honeycomb pitting, oil stains and stains on the surface of the concrete specimen, and highlighting the crack direction and details;

[0035] S3: taking a picture of the concrete specimen after the surface treatment in the step S2 to obtain a concrete specimen image;

[0036] S4: image cropping and preprocessing, cropping the concrete specimen image to only keep the surface area of the concrete specimen;

[0037] S5: image processing and conversion, performing image enhancement and image noise reduction filtering processing on the cropped specimen image;

[0038] S6: crack image extraction, after the image enhancement and image noise reduction filtering processing, the crack is black, the crack image is subjected to mathematical morphology segmentation to obtain a crack binary image with only black and white pixels;

[0039] S7: Crack information extraction and calculation. A coordinate system is established on the crack image to discretize the crack information. That is, the crack length direction of the digital image is the x direction, and the direction perpendicular to the crack length is the y direction. A discretized statistical method is used, and each pixel column in the y direction of the image is used as a reference line. The number of pixels in the y-axis direction of the crack image is m, and the number of pixels in the x-axis direction is n. The black and white pixels of the binary image correspond to the 0 and 1 values ​​in the digital matrix respectively. The crack pixels in the digital image, that is, the white pixels, are scanned and identified point by point from top to bottom and from left to right. The specific identification principle is: if the pixel point D(x, y)=1 at the pixel coordinate (x, y), then the point represents a crack pixel point; if D(x, y)=0, it is a non-crack pixel point, that is, the substrate part of the concrete surface that has not cracked. Taking each reference line as the standard, the connected domain between the upper and lower white pixels is a crack. Finally, the data during the scanning process is extracted to obtain the crack position of each crack. x Label values ​​on reference lines n x , and the number of crack pixels corresponding to each label, the average crack width W mean Calculate according to the following formula:

[0040] (1)

[0041] (2)

[0042] Where, L is the actual length of the specimen in the image, mm; m is the number of pixels contained in the specimen length L in mm; x is the reference line number; n is the total number of pixel columns, n x For the x The label value of the crack on the reference line; W mean is the average crack width, δ is the actual width of each pixel, Q xi For the x The first reference line i The number of pixels contained in each crack label;

[0043] Count the number of all crack pixels in the concrete specimen image and determine the crack area. The crack area of ​​a single crack in the concrete specimen is calculated as follows:

[0044] (3)

[0045] Where: ai is the area of the i-th crack; l i is the total number of pixels of the i-th crack;

[0046] The total cracking area of the surface of the concrete specimen is calculated according to the following formula:

[0047] (4)

[0048] In the formula: c is the total cracking area of the surface of the concrete specimen; n is the total number of cracks.

[0049] The concrete specimen is formed in step S1 and is ventilated by a fan for at least 24 hours.

[0050] The concrete thin plate cracking resistance test in step S1 is performed according to the specification DL / T 5150-2017.

[0051] In step S3, the concrete specimen is photographed, the position of the photographing equipment is adjusted according to the focal length and resolution of the photographing equipment, and light supplement measures are taken according to the test environment to ensure the clarity of the crack image.

[0052] In step S5, the image enhancement is adjusted by the imadjust function in MATLAB, and the gray value [0.25, 0.60] in the original image is mapped to [0, 1], and the image noise reduction filter is filtered by the imbothat function in MATLAB and the gussisian function in the Gaussian filter.

[0053] In step S6, the crack image is mathematically segmented by the imreconstruct function in MATLAB, the threshold value of large cracks is 20, and the threshold value of small cracks is 160. The crack image with a threshold value of 160 is used as the mask image in the imreconstruct function, and the crack image with a threshold value of 20 is used as the marker image in the imreconstruct function, and finally a crack binary image with only black and white pixels is obtained.

[0054] In the crack image extraction in step S6, the concrete specimen image includes black and white colors, wherein the crack of the concrete specimen is a white pixel point, and the base material of the concrete specimen is a black pixel point. Example 2

[0055] A concrete irregular crack characterization and calculation method is used to characterize and calculate the irregular cracks of the XX concrete specimen, and the specific process is as follows:

[0056] S1: make a concrete test piece, the size of the test piece is 800mm*600mm, and a concrete thin plate crack resistance test is performed on the test piece;

[0057] S2: the concrete test piece after the crack resistance test is surface treated, white self-spraying paint is used for spraying, non-crack information such as concrete surface honeycomb pitting, oil stains and stains is covered, and crack direction and details are highlighted;

[0058] S3: the concrete test piece after the surface treatment in step S2 is photographed by using a mobile phone, and a concrete test piece image is obtained, as shown in FIG. 3; Figure 1

[0059] S4: image cropping and pretreatment, and the specific process is as follows: the concrete test piece image is cropped, and only the surface area of the concrete test piece is reserved;

[0060] S5: image processing and conversion, and the specific process is as follows: the cropped test piece image is subjected to image enhancement and image noise reduction filtering processing, wherein: the image enhancement is adjusted by using the imadjust function in MATLAB, and the gray value [0.25, 0.60] in the original image is mapped to [0, 1]; the image noise reduction filtering is performed by using the imbothat function in MATLAB and the gussisian function in the Gaussian filter;

[0061] S6: crack image extraction, and the specific process is as follows: the image after filtering and enhancement is black, the crack image is subjected to mathematical morphology segmentation by using the imreconstruct function in MATLAB, the threshold value of large cracks is 20, the threshold value of small cracks is 160, the image with the threshold value of 160 is taken as a mask image in the imreconstruct function, the image with the threshold value of 20 is taken as a marker image in the imreconstruct function, and finally a crack binary image with only black and white pixels is obtained, the black color is a base material, and the white color is a crack, as shown in FIG. 4; Figure 2

[0062] S7: crack information extraction and calculation, the total number of pixel points in the concrete test piece image and the average width of cracks are counted, the total number of pixels is 4000*3000, and the cracking area of the cracks is determined, and the results are shown in Table 1; the concrete crack cracking area can be determined only by recording the concrete thin plate surface crack morphology by using the mobile phone photographing, and the results are more comprehensive and more accurate compared with the crack calculation results in the existing specification.

[0063] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this, any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application.​​

Claims

1. A method for characterizing and calculating irregular cracks in concrete, characterized by: The following steps are involved: S1: Prepare concrete specimens and conduct concrete thin slab crack resistance test on them; S2: Surface treatment of concrete specimens after crack resistance test, spraying with white spray paint; S3: taking a photo of the concrete specimen after the surface treatment in step S2 to obtain an image of the concrete specimen; S4: Image cropping and preprocessing, cropping the concrete specimen image to retain only the surface area of ​​the concrete specimen; S5: Image processing and transformation, image enhancement and image noise reduction filtering are performed on the cropped specimen image; S6: Crack image extraction. After image enhancement and image noise reduction filtering, the cracks appear black. The crack image is segmented using mathematical morphology to obtain a crack binary image consisting of only black and white pixels. S7: Crack information extraction and calculation. A coordinate system is established on the crack image to discretize the crack information. That is, the crack length direction of the digital image is the x direction, and the direction perpendicular to the crack length is the y direction. A discretized statistical method is used, and each pixel column in the y direction of the image is used as a reference line. The number of pixels in the y-axis direction of the crack image is m, and the number of pixels in the x-axis direction is n. The black and white pixels of the binary image correspond to the 0 and 1 values ​​in the digital matrix respectively. The crack pixels in the digital image, that is, the white pixels, are scanned and identified point by point from top to bottom and from left to right. The specific identification principle is: if the pixel point D(x, y)=1 at the pixel coordinate (x, y), then the point represents a crack pixel point; if D(x, y)=0, it is a non-crack pixel point, that is, the substrate part of the concrete surface that has not cracked. Taking each reference line as the standard, the connected domain between the upper and lower white pixels is a crack. Finally, the data during the scanning process is extracted to obtain the crack position of each crack. x Label values ​​on reference lines , and the number of crack pixels corresponding to each label, the average crack width Calculate according to the following formula: （1） （2） Where, L is the actual length of the specimen in the image; m is the number of pixels contained in the specimen length L; x is the reference line number; n is the total number of pixel columns, For the x The label value of the crack on the reference line; is the average crack width, δ is the actual width of each pixel, For the x The first reference line i The number of pixels contained in each crack label; Count the number of all crack pixels in the concrete specimen image and determine the crack area. The crack area of ​​a single crack in the concrete specimen is calculated as follows: （3） Where: is the area of ​​the i-th crack; is the total number of pixels of the i-th crack; The total crack area on the surface of the concrete specimen is calculated as follows: （4） Where: c is the total cracked area on the surface of the concrete specimen; n is the total number of cracks.

2. The method for characterizing and calculating irregular cracks in concrete according to claim 1, wherein: After the concrete specimens are formed in step S1, they are ventilated with a fan for at least 24 hours.

3. The method for characterizing and calculating irregular cracks in concrete according to claim 1, wherein: The concrete slab crack resistance test in step S1 is performed in accordance with the specification DL / T 5150-2017.

4. The method for characterizing and calculating irregular cracks in concrete according to claim 1, wherein: In step S3, the concrete specimen is photographed, the position of the photographing device is adjusted according to the focal length and resolution of the photographing device, and supplementary lighting measures are taken according to the test environment to ensure the clarity of the crack image.

5. The method for characterizing and calculating irregular cracks in concrete according to claim 1, wherein: In step S5, the image enhancement is adjusted by the imadjust function in MATLAB to map the grayscale value range of [0.25, 0.60] in the original image to [0, 1]. The image denoising filter is performed by the imbothat function in MATLAB and the gussisian function in the Gaussian filter.

6. The method for characterizing and calculating irregular cracks in concrete according to claim 1, characterized in that: In step S6, the crack image is segmented using mathematical morphology using the imreconstruct function in MATLAB. The threshold for large cracks is 20, and the threshold for small cracks is 160. The crack image with the threshold adjusted to 160 is used as the mask image in the imreconstruct function, and the crack image with the threshold adjusted to 20 is used as the marker image in the imreconstruct function, ultimately obtaining a crack binary image consisting of only black and white pixels.

7. The method for characterizing and calculating irregular cracks in concrete according to claim 1, wherein: In the crack image extraction in step S6, the concrete specimen image includes two colors, black and white, wherein the cracks of the concrete specimen are white pixels and the base material of the concrete specimen is black pixels.

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