Concrete irregular crack characterization and calculation method

Abstract

The invention belongs to the technical field of concrete crack detection, and particularly relates to a concrete irregular crack characterization and calculation method. A concrete irregular crack characterization and calculation method comprises the following steps that a concrete test piece is manufactured, and a concrete thin plate crack resistance test is conducted on the concrete test piece; performing surface treatment on the concrete test piece subjected to the anti-cracking test, and spraying white spray paint; photographing the concrete test piece to obtain a concrete test piece image; cutting and preprocessing the image; image processing and conversion; and crack information extraction and calculation: counting the number of all crack pixel points in the concrete test piece image, and determining the crack area and the crack width of the crack. The method is simple in operation, high in convenience and high in intelligence, the crack shape of the surface of the concrete thin plate is photographed and recorded only through equipment such as a mobile phone and a camera, the cracking area of the concrete crack can be determined, and compared with a crack calculation result in an existing specification, the method is more comprehensive and more accurate.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete crack detection, and in particular relates to a method for characterizing and calculating irregular cracks in concrete. Background Art

[0002] Cracks are an important indicator for measuring the durability and safety of concrete structures. It is very important to detect cracks on the concrete surface in a timely and accurate manner. The hydraulic concrete test procedures (DL / T 5150-2017 and SL / T 352-2020) point out that the early crack resistance of concrete can be determined by the plane thin plate test method. This method uses a microscope to manually visually read the crack width and length, and evaluates the crack resistance of the specimen by the average crack area and the number of cracks per unit area. Among them, the average crack area of ​​each crack is calculated by the maximum width of the crack. However, the crack discreteness calculated by this method is too large to reflect the true crack resistance of the concrete material.

[0003] On the other hand, with the maturity of fiber application technology in concrete, a series of new concrete materials with excellent crack resistance have gradually been applied in practical projects, such as ultra-high performance concrete (UHPC), engineered cement-based composites (ECC) and self-compacting concrete (SCC). The cracks in these concrete materials are characterized by multiple tiny irregular cracks, and the crack width is often at the micron level. The accuracy of the crack width and crack area in the method in the specification is 0.01mm and 0.01mm, respectively. 2 , it is difficult to accurately quantify the crack information of this type of concrete material. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a method for characterizing and calculating irregular cracks in concrete. The method is simple to operate, convenient and intelligent. The crack morphology on the surface of the concrete thin plate can be recorded by taking pictures with mobile phones, cameras and other devices to determine the crack area of ​​the concrete. Compared with the crack calculation results in the existing specifications, the method is more comprehensive and accurate.

[0005] The technical solution of the present invention is: a method for characterizing and calculating irregular cracks in concrete, comprising the following steps: S1: Make concrete specimens and conduct concrete thin slab crack resistance test on them; S2: The concrete specimens after the crack resistance test were treated with white spray paint to cover the non-crack information such as honeycomb surface, oil stains and spots on the surface of the concrete specimens, highlighting the crack direction and details; 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 keep only the surface area of ​​the concrete specimen; S5: Image processing and conversion, 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 by mathematical morphology to obtain a crack binary image with only black and white pixels. S7: Crack information extraction and calculation. A coordinate system is established on the crack image to discretize and count 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 to take each pixel column in the y direction of the image 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 values ​​0 and 1 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 the 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 in the scanning process is extracted to obtain the crack of each crack in the first x Label values ​​on reference lines n x , as well as the number of crack pixels corresponding to each label, the average crack width W mean Calculate according to the following formula: (1) (2) 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 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 reference line i The number of pixels contained in each crack label; The number of all crack pixels in the concrete specimen image is counted, and the crack area is determined. The crack area of ​​a single crack in the concrete specimen is calculated as follows: (3) Where: a i is the area of ​​the i-th crack; l i 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 crack area on the surface of the concrete specimen; n is the total number of cracks.

[0006] After the concrete specimens are formed in step S1, a fan is used for ventilation for at least 24 hours.

[0007] The concrete slab crack resistance test in step S1 is carried out according to specification DL / T 5150-2017.

[0008] In the 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.

[0009] In step S5, the image enhancement is adjusted by the imadjust function in MATLAB, and the grayscale value range [0.25, 0.60] in the original image is mapped to between [0, 1]. The image denoising filtering is performed by the imbothat function in MATLAB and the gussisian function in the Gaussian filter.

[0010] In step S6, the crack image is segmented by mathematical morphology using the imreconstruct function in MATLAB, the threshold of large cracks is 20, the threshold of 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, and finally a crack binary image with only black and white pixels is obtained.

[0011] 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.

[0012] The technical effect of the present invention is that the present invention is simple to operate, highly convenient and intelligent. It can determine the crack area and width of the concrete cracks by only taking pictures with a mobile phone, camera or other equipment to record the crack morphology on the surface of the concrete thin plate. Compared with the crack calculation results in the existing specifications, it is more comprehensive and accurate.

[0013] The following is a further description with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a surface photograph of a concrete specimen according to an embodiment of the present invention.

[0015] Figure 2 This is a photo of a concrete specimen according to an embodiment of the present invention. DETAILED DESCRIPTION Example 1

[0016] A method for characterizing and calculating irregular cracks in concrete, comprising the following steps: S1: Make concrete specimens and conduct concrete thin slab crack resistance test on them; S2: The concrete specimens after the crack resistance test were treated with white spray paint to cover the non-crack information such as honeycomb surface, oil stains and spots on the surface of the concrete specimens, highlighting the crack direction and details; 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 keep only the surface area of ​​the concrete specimen; S5: Image processing and conversion, 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 by mathematical morphology to obtain a crack binary image with only black and white pixels. S7: Crack information extraction and calculation. A coordinate system is established on the crack image to discretize and count 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 to take each pixel column in the y direction of the image 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 values ​​0 and 1 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 the 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 in the scanning process is extracted to obtain the crack of each crack in the first x Label values ​​on reference lines n x , as well as the number of crack pixels corresponding to each label, the average crack width W mean Calculate according to the following formula: (1) (2) 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 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 reference line i The number of pixels contained in each crack label; The number of all crack pixels in the concrete specimen image is counted, and the crack area is determined. The crack area of ​​a single crack in the concrete specimen is calculated as follows: (3) Where: a i is the area of ​​the i-th crack; l i 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 crack area on the surface of the concrete specimen; n is the total number of cracks.

[0017] After the concrete specimens are formed in step S1, a fan is used for ventilation for at least 24 hours.

[0018] The concrete slab crack resistance test in step S1 is carried out according to specification DL / T 5150-2017.

[0019] In the 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.

[0020] In step S5, the image enhancement is adjusted by the imadjust function in MATLAB, and the grayscale value range [0.25, 0.60] in the original image is mapped to between [0, 1]. The image denoising filtering is performed by the imbothat function in MATLAB and the gussisian function in the Gaussian filter.

[0021] In step S6, the crack image is segmented by mathematical morphology using the imreconstruct function in MATLAB, the threshold of large cracks is 20, the threshold of 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, and finally a crack binary image with only black and white pixels is obtained.

[0022] 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. Example 2

[0023] A method for characterizing and calculating irregular cracks in concrete in Example 1 is used to characterize and calculate the irregular cracks in the XX concrete specimen. The specific process is as follows: S1: Prepare concrete specimens with a size of 800mm x 600mm in length x width, and conduct concrete thin plate crack resistance test on them; S2: The concrete specimens after the crack resistance test were treated with white spray paint to cover the non-crack information such as honeycomb surface, oil stains and spots on the concrete surface, highlighting the crack direction and details; S3: Take a photo of the concrete specimen after the surface treatment in step S2 with a mobile phone to obtain an image of the concrete specimen, such as Figure 1 As shown; S4: Image cropping and preprocessing, the specific process is: cropping the concrete specimen image to retain only the surface area of ​​the concrete specimen; S5: Image processing and conversion. The specific process is as follows: image enhancement and image noise reduction filtering are performed on the cropped specimen image, wherein: image enhancement is adjusted by the imadjust function in MATLAB to map the grayscale value [0.25, 0.60] range in the original image to [0, 1]; image noise reduction filtering is performed by the imbothat function in MATLAB and the gussisian function in the Gaussian filter; S6: Crack image extraction. The specific process is as follows: after filtering and enhancing the image, the cracks appear black. The crack image is segmented mathematically using the imreconstruct function in MATLAB. The threshold of large cracks is 20, and the threshold of small cracks is 160. The image with the threshold adjusted to 160 is used as the mask image in the imreconstruct function, and the image with the threshold adjusted to 20 is used as the marker image in the imreconstruct function. Finally, a crack binary image with only black and white pixels is obtained, where black is the substrate and white is the crack. Figure 2 As shown; S7: Crack information extraction and calculation, counting the number of all crack pixels and the average crack width in the concrete specimen image, the total number of pixels length × width is 4000 × 3000, and determining the crack area of ​​the crack. The results are shown in Table 1. The present invention can determine the crack area of ​​the concrete crack only by recording the crack morphology on the surface of the concrete thin plate through mobile phone photography, which is more comprehensive and accurate than the crack calculation results in the existing specifications. The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for characterizing and calculating irregular cracks in concrete, characterized by: The following steps are involved: S1: Make concrete specimens and conduct concrete thin slab crack resistance test on them; S2: The concrete specimens after the crack resistance test were treated with white spray paint to cover the non-crack information such as honeycomb surface, oil stains and spots on the surface of the concrete specimens, highlighting the crack direction and details; 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 keep only the surface area of ​​the concrete specimen; S5: Image processing and conversion, 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 by mathematical morphology to obtain a crack binary image with only black and white pixels. S7: Crack information extraction and calculation. A coordinate system is established on the crack image to discretize and count 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 to take each pixel column in the y direction of the image 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 values ​​0 and 1 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 the 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 in the scanning process is extracted to obtain the crack of each crack in the first x Label values ​​on reference lines n x , as well as the number of crack pixels corresponding to each label, the average crack width W mean Calculate according to the following formula: （1） （2） 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 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 reference line i The number of pixels contained in each crack label; The number of all crack pixels in the concrete specimen image is counted, and the crack area is determined. The crack area of ​​a single crack in the concrete specimen is calculated as follows: （3） Where: a i is the area of ​​the i-th crack; l i 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 crack area on the surface of the concrete specimen; n is the total number of cracks.

2. According to claim 1, a method for characterizing and calculating irregular cracks in concrete is characterized by: After the concrete specimens are formed in step S1, a fan is used for ventilation for at least 24 hours.

3. According to claim 1, a method for characterizing and calculating irregular cracks in concrete is characterized by: The concrete slab crack resistance test in step S1 is carried out according to specification DL / T 5150-2017.

4. The method for characterizing and calculating irregular cracks in concrete according to claim 1, characterized in that: In the 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, characterized in that: In step S5, the image enhancement is adjusted by the imadjust function in MATLAB, and the grayscale value range [0.25, 0.60] in the original image is mapped to between [0, 1]. The image denoising filtering 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 by mathematical morphology using the imreconstruct function in MATLAB, the threshold of large cracks is 20, the threshold of 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, and finally a crack binary image with only black and white pixels is obtained.

7. The method for characterizing and calculating irregular cracks in concrete according to claim 1, characterized in that: 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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