Method, device, equipment and storage medium for evaluating impermeability performance of recycled concrete

Abstract

The present application relates to a method, device, equipment and storage medium for evaluating the impermeability performance of recycled concrete. The method includes: performing image enhancement and denoising preprocessing on the acquired three-dimensional image data of recycled concrete to obtain a microscopic structure data set of recycled concrete; reading the microscopic structure data set through OpenCV to obtain the image edge data and pore boundary data of recycled concrete; calculating the porosity of recycled concrete based on the image edge data and pore boundary data; evaluating the impermeability performance of recycled concrete according to the porosity of recycled concrete; adopting this method can effectively solve the problem that the evaluation method of the impermeability performance of recycled concrete is inaccurate due to the unclear structure of recycled concrete.

Description

Technical Field

[0001] The present application relates to the technical field of building material testing, and particularly to a method, device, equipment and storage medium for evaluating the impermeability performance of recycled concrete. Background Art

[0002] With the advancement of the dual-carbon goal, the construction industry urgently needs to strengthen energy conservation and emission reduction work. Recycled aggregate concrete that can reuse waste aggregates not only realizes the resource utilization of construction waste but also reduces the exploitation of natural aggregates, which is of great significance for promoting the low-carbon development of the concrete industry.

[0003] However, recycled concrete has poor compactness. It not only cannot cut off the continuity of pore channels like natural aggregates but even provides new channels for the flow of moisture, resulting in an increase in cracks and a deterioration in reliability compared to natural aggregate concrete. Therefore, evaluating the impermeability performance of recycled concrete is crucial for the application of recycled concrete. At the same time, the impermeability performance of recycled concrete is closely related to its porosity. CT scanning can not only perform three-dimensional reconstruction of recycled concrete but also calculate and restore its porosity. However, current three-dimensional reconstruction software has poor processing effects on non-homogeneous materials such as recycled concrete. Therefore, a CT scanning image processing technology for recycled concrete is needed to effectively utilize test results to calculate the porosity of recycled concrete and establish a prediction model for the impermeability performance of recycled concrete based on this. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a method, device, equipment and storage medium for evaluating the impermeability performance of recycled concrete.

[0005] In a first aspect, the present application provides a method for evaluating the impermeability performance of recycled concrete, characterized in that the method includes:

[0006] Performing image enhancement and denoising preprocessing on the obtained three-dimensional image data of recycled concrete to obtain a microscopic structure data set of recycled concrete;

[0007] Reading the microscopic structure data set through OpenCV to obtain image edge data and pore boundary data of recycled concrete;

[0008] Calculating the porosity of recycled concrete based on the image edge data and pore boundary data;

[0009] Evaluating the impermeability performance of recycled concrete based on the porosity of recycled concrete.

[0010] In one of the embodiments, the steps of image enhancement and denoising preprocessing include:

[0011] Enhance the contrast of the three-dimensional image data and perform the first median denoising process; enhancing the contrast of the three-dimensional image data includes brightening the three-dimensional image data and darkening the shadows of the three-dimensional image data; the formula is as follows:

[0012]

[0013] ksize = 3

[0014] In the formula, S a is the shadow value after darkening the three-dimensional image data; S 0 is the initial shadow value of the three-dimensional image data; s is the first preset shadow value; H a is the highlight value after brightening the three-dimensional image data; H S is the highlight value after darkening the image in the three-dimensional image data; h is the first preset highlight value; ksize is the filter kernel size of the first median denoising process.

[0015] In one embodiment, the steps of image enhancement and denoising preprocessing further include:

[0016] Perform color inversion processing and the second median denoising process on the three-dimensional image data after the first median denoising process; the color inversion processing includes adjusting the gray value of the three-dimensional image data, darkening the shadow of the three-dimensional image data again, and brightening the three-dimensional image data again; the formula is as follows:

[0017] Ca ij = 255 - C ij

[0018]

[0019] In the formula, Ca ij is the adjusted gray value of the pixel at the i-th row and j-th column in the three-dimensional image data; C ij is the initial gray value of the pixel at the i-th row and j-th column in the three-dimensional image data; S a ’ is the shadow value after darkening the image in the three-dimensional image data; S 0 ’ is the initial shadow value of the image in the three-dimensional image data; s’ is the second preset shadow value; H a ’ is the highlight value after brightening the image in the three-dimensional image data; H S ’ is the highlight value after darkening the image in the three-dimensional image data; h’ is the second preset highlight value.

[0020] In one embodiment, the steps of calculating the porosity of the recycled concrete based on the image edge data and the pore boundary data include:

[0021] Obtain the cross-sectional area data of the corresponding recycled concrete according to the image edge data;

[0022] Obtain the cross-sectional area data of each corresponding pore in the recycled concrete according to the pore boundary data;

[0023] Obtain the porosity of the recycled concrete according to the cross-sectional area data of the recycled concrete and the cross-sectional area data of each pore in the recycled concrete.

[0024] In one embodiment, the steps of evaluating the impermeability performance of recycled concrete according to the porosity of recycled concrete include:

[0025] Predict the seepage height of recycled concrete under the seepage height method for different porosities according to the following formula:

[0026] D m = 4.88 - 0.37P + 0.04P 2

[0027] In the formula, D m is the predicted value of the seepage height of recycled concrete under the seepage height method, with the unit of mm; P is the porosity of recycled concrete;

[0028] Calculate the relative permeability coefficient of recycled concrete as an evaluation index of impermeability performance according to the following formula;

[0029]

[0030] In the formula, K r is the relative permeability coefficient, with the unit of mm / h; a is the water absorption rate of recycled concrete; t is the constant pressure time, with the unit of h; H is the water pressure, expressed as the height of the water column, with the unit of mm; t takes 24h and H takes 122400mm;

[0031] Evaluate the impermeability performance of recycled concrete according to the relative permeability coefficient.

[0032] In one embodiment, the method further includes:

[0033] Cut the dried recycled concrete into cubes with side lengths of 10 mm to 25 mm or spheres with diameters of 10 mm to 25 mm to obtain recycled concrete specimens;

[0034] Perform a CT scan on the recycled concrete specimens to obtain three-dimensional image data of the recycled concrete.

[0035] The second aspect of this application also provides a device for extracting the pore boundaries of recycled concrete. The device includes:

[0036] A preprocessing module for performing image enhancement and denoising preprocessing on the acquired three-dimensional image data of recycled concrete to obtain a microscopic structure dataset of recycled concrete;

[0037] A data reading module for reading the microscopic structure dataset through OpenCV to obtain the image edge data and pore boundary data of recycled concrete;

[0038] A calculation module for calculating the porosity of recycled concrete based on the image edge data and pore boundary data;

[0039] An evaluation module for evaluating the impermeability performance of recycled concrete based on the porosity of recycled concrete.

[0040] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method provided in the first aspect of the present application.

[0041] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the method provided in the first aspect.

[0042] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method provided in the first aspect.

[0043] The above-mentioned method, device, equipment and storage medium for evaluating the impermeability performance of recycled concrete perform image enhancement and denoising preprocessing on the three-dimensional image data of recycled concrete, and obtain a microscopic structure dataset that can fully reflect the pore structure of recycled concrete without cropping the three-dimensional image data. Then, the image edge data and pore boundary data of recycled concrete are read through OpenCV, and the porosity of the recycled concrete is calculated through the image edge data and pore boundary data respectively, so as to further obtain the impermeability performance of recycled concrete with porosity as the main parameter, effectively solving the problem that the method for evaluating the impermeability performance of recycled concrete is inaccurate due to the unclear structure of recycled concrete. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0045] Figure 1 It is a flowchart of the steps of the method for evaluating the impermeability performance of recycled concrete in an embodiment;

[0046] Figure 2 It is a flowchart of the steps for calculating the porosity in an embodiment;

[0047] Figure 3 It is a comparison chart of the predicted values and test values of the water seepage height of recycled concrete at different porosities in an embodiment;

[0048] Figure 4 It is a flowchart of the steps for obtaining the three-dimensional image data of recycled concrete in an embodiment;

[0049] Figure 5 It is a pore structure diagram of recycled concrete processed by the existing image analysis method in an embodiment;

[0050] Figure 6 It is a flowchart for obtaining the microscopic structure data set of recycled concrete in an embodiment;

[0051] Figure 7 It is an unprocessed pore structure diagram of recycled concrete in an embodiment;

[0052] Figure 8 It is a flowchart of gray level adjustment in an embodiment;

[0053] Figure 9 It is a pore structure diagram of recycled concrete obtained according to the read microscopic structure data set in an embodiment;

[0054] Figure 10 It is a structure diagram of the anti-seepage performance evaluation device of recycled concrete in an embodiment. Detailed implementation manners

[0055] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] The application and promotion of recycled concrete are closely related to its physical properties and durability. Since recycled aggregates are not as single, complete, and structurally dense as natural aggregates, with some old mortar adhering to their surfaces and cracks generated during the crushing process, the pore structure of recycled concrete is undoubtedly more complex than that of natural aggregate concrete. At the same time, the old mortar adhering to the surface is not conducive to the bonding between recycled aggregates and fresh mortar, resulting in poor compactness of recycled concrete. It not only cannot cut off the continuity of pore channels like natural aggregates but even provides new channels for the flow of water. The old mortar adhering to the surface of recycled aggregates also makes recycled concrete have higher water absorption, and more water is lost from the fresh cement paste during the setting and hardening process, creating more pores and cracks in the interfacial transition zone of recycled concrete. Therefore, relevant modification studies on recycled concrete are all related to improving the internal pore structure of recycled concrete.

[0057] In an exemplary embodiment, as Figure 1 shown, a method for evaluating the impermeability performance of recycled concrete is provided, which may include the following steps 102 to 108. Among them:

[0058] Step 102, perform image enhancement and denoising preprocessing on the obtained three-dimensional image data of recycled concrete to obtain the microscopic structure data set of recycled concrete.

[0059] Specifically, the image enhancement of the three-dimensional image data can be a processing method for enhancing image contrast, and the image denoising of the three-dimensional image data can be a preprocessing method for eliminating image distortion.

[0060] Step 104, read the microscopic structure data set through OpenCV to obtain the image edge data and pore boundary data of recycled concrete.

[0061] Specifically, OpenCV is a cross-platform computer vision and machine learning software library, which can be used to read the microscopic structure data set by using the cv2.imread function and extract the image edge data and pore boundary data of recycled concrete through the cv2.Canny function.

[0062] Furthermore, the microscopic structure data set may include the image edge data and pore boundary data of recycled concrete.

[0063] Step 106, calculate the porosity of recycled concrete based on the image edge data and pore boundary data.

[0064] Specifically, the porosity of recycled concrete can be directly calculated based on the image edge data and pore boundary data, or the image information reflected by the image edge data and pore boundary data can be measured proportionally to obtain the data for calculating the porosity of recycled concrete.

[0065] Step 108: Evaluate the impermeability of recycled concrete based on its porosity.

[0066] It can be understood that the pores in recycled concrete have a crucial impact on its impermeability. Whether it is highly porous recycled concrete or recycled concrete with a large cross-sectional area of pores, there are fatal effects on its impermeability. Therefore, evaluating the impermeability of recycled concrete through the porosity measured by the cross-sectional area of pores has become an important part of the related modification research on recycled concrete. On the other hand, the impermeability of recycled concrete also determines its structural strength. Therefore, evaluating the impermeability of recycled concrete is very important.

[0067] The above method for evaluating the impermeability of recycled concrete preprocesses the three-dimensional image data of recycled concrete by image enhancement and denoising to obtain a microscopic structure dataset that can fully reflect the pore structure of recycled concrete without cropping the three-dimensional image data. Then, the image edge data and pore boundary data of recycled concrete are obtained by reading through OpenCV, and the porosity of the recycled concrete is calculated respectively through the image edge data and pore boundary data, so as to further obtain the impermeability of recycled concrete with porosity as the main parameter, effectively solving the problem that the method for evaluating the impermeability of recycled concrete is inaccurate due to the unclear structure of recycled concrete.

[0068] In an exemplary embodiment, the steps of image enhancement and denoising preprocessing include:

[0069] Enhance the contrast of the three-dimensional image data and perform the first median denoising process. Enhancing the contrast of the three-dimensional image data includes brightening the three-dimensional image data and dimming the shadows of the three-dimensional image data. The formula is as follows:

[0070]

[0071] ksize = 3

[0072] In the formula, S a is the shadow value after dimming the three-dimensional image data; S 0 is the initial shadow value of the three-dimensional image data; s is the first preset shadow value; H a is the highlight value after brightening the three-dimensional image data; H S is the highlight value after dimming the image in the three-dimensional image data; h is the first preset highlight value; ksize is the filter kernel size of the first median denoising process.

[0073] Among them, the first preset shadow value and the first preset highlight value can be specifically set based on the image characteristics of concrete in a large number of practices to obtain parameters that can reflect the best image effect. Exemplarily, in the embodiments of the present application, the best set shadow value can be 80, the best set highlight value can be 50, and the best filter kernel size can be 3 to retain more image details.

[0074] Specifically, brightening the three-dimensional image data highlights the details and features in the image, improves the overall brightness of the image, attempts to make it more saturated while repairing the details in the dark parts of the image, and improves the overall quality and visual effect of the image; then, combining with dimming the shadows of the three-dimensional image data can enhance the contrast in the image, and the details and textures of the shadows will be clearer and more prominent. Compared with the prior art method of magnifying and repairing the image, it can fully retain the integrity of the image so that the data volume is not compressed, and more accurately reflect the impact of pores on the impermeability performance.

[0075] Specifically, denoising the image can eliminate possible image distortion and noise.

[0076] In an exemplary embodiment, the steps of image enhancement and denoising preprocessing further include:

[0077] Performing color inversion processing and second median denoising processing on the three-dimensional image data after the first median denoising processing; the color inversion processing includes adjusting the gray value of the three-dimensional image data, dimming the shadows of the three-dimensional image data again, and brightening the three-dimensional image data again; the formula is as follows:

[0078] Ca ij = 255 - C ij

[0079]

[0080] In the formula, Ca ij is the adjusted gray value of the pixel at the i-th row and j-th column in the three-dimensional image data; C ij is the initial gray value of the pixel at the i-th row and j-th column in the three-dimensional image data; S a ’ is the shadow value after the image in the three-dimensional image data is darkened; S 0 ’ is the initial shadow value of the image in the three-dimensional image data; s’ is the second preset shadow value; H a ’ is the highlight value after the image in the three-dimensional image data is brightened; H S ’ is the highlight value after the image in the three-dimensional image data is darkened; h’ is the second preset highlight value.

[0081] Specifically, by further adjusting the gray value, shadow value, and highlight value, the difference between pores and the concrete matrix can be highlighted, and the information expression ability and analysis effect of the image can be improved.

[0082] Furthermore, in the specific implementation process, the second preset shadow value for the secondary adjustment can be 20, and the second preset highlight value can be 80.

[0083] Furthermore, after brightening the three-dimensional image and darkening the shadow again, the formula for the second median denoising process of the three-dimensional image is as follows:

[0084] ksize = 15

[0085] Specifically, the optimal filter kernel size for the second median denoising can be 15 to remove the relatively large amount of noise caused by image adjustment.

[0086] In an exemplary embodiment, as Figure 2 shown, the steps of calculating the porosity of recycled concrete based on the image edge data and pore boundary data include steps 202 to 204 as follows. Among them:

[0087] Step 202, obtaining the cross-sectional area data of the corresponding recycled concrete according to the image edge data.

[0088] Specifically, the size data of the recycled concrete specimen can be obtained through the image edge data, and the cross-sectional area data can be calculated based on the size data.

[0089] Step 204, obtaining the cross-sectional area data of each corresponding pore in the recycled concrete according to the pore boundary data.

[0090] Step 206, obtaining the porosity of the recycled concrete based on the cross-sectional area data of the recycled concrete and the cross-sectional area data of each pore in the recycled concrete.

[0091] Specifically, the porosity of the recycled concrete can be obtained by dividing the sum of the areas of the pores in the recycled concrete by the cross-section of the recycled concrete.

[0092] In an exemplary embodiment, the steps of evaluating the impermeability performance of recycled concrete based on the porosity of recycled concrete include steps 302 to 306 as follows. Among them:

[0093] Step 302, predicting the seepage height of recycled concrete under the seepage height method with different porosities according to the following formula:

[0094] D m = 4.88 - 0.37P + 0.04P 2

[0095] In the formula, Dm is the predicted water seepage height of recycled concrete under the water seepage height method, with the unit of mm; P is the porosity of recycled concrete.

[0096] Among them, the water seepage height method is used to measure the water impermeability of recycled concrete or other materials. It is used to determine the water impermeability grade of materials by measuring the water seepage height, so as to judge their water penetration performance under different water pressures.

[0097] Furthermore, the relationship between the predicted water seepage height and recycled concrete can be fitted by a linear regression model.

[0098] Step 304, calculate the relative permeability coefficient of recycled concrete according to the following formula as an evaluation index of water impermeability performance;

[0099]

[0100] In the formula, K r is the relative permeability coefficient, with the unit of mm / h; a is the water absorption rate of recycled concrete; t is the constant pressure time, with the unit of h; H is the water pressure, expressed as the height of the water column, with the unit of mm; t takes 24 h, and H takes 122400 mm.

[0101] Among them, since the above formula is the predicted water seepage height of recycled concrete under the standard water seepage height method, t takes 24 h and H takes 122400 mm.

[0102] Step 306, evaluate the water impermeability performance of recycled concrete according to the relative permeability coefficient.

[0103] Specifically, a preset parameter can be set as a threshold value for the water impermeability performance of recycled concrete in combination with a limited number of experiments or the relative permeability coefficient in the specific implementation process. When the calculation result of the relative permeability coefficient exceeds the threshold value, a prompt message indicating that the water impermeability performance of recycled concrete is poor is output, otherwise a prompt message indicating normal water impermeability performance is output. The setting of the preset parameter is not specifically limited here.

[0104] In one embodiment, the steps of evaluating the water impermeability performance of recycled concrete according to the porosity of recycled concrete may include the following scheme:

[0105] 1. Prepare recycled concrete with a label of C30 and recycled aggregate replacement rates of 30%, 50%, 70%, and 100% respectively, and number them as R 30 、R 50 、R 70 、R 100, and NAC made entirely of natural aggregates was used as the control group. Each group included 1 cubic specimen with dimensions of 100mm×100mm×100mm and 6 frustum specimens with an upper diameter of 175mm, a lower diameter of 185mm, and a height of 150mm.

[0106] 2. After the specimens were removed from the mold, the cement slurry films on both end faces of the frustum specimens were polished off with a grinding machine, and the specimens were immediately sent to a standard curing room for curing. When the curing age reached 27 days, the frustum specimens were taken out, wiped clean, air-dried, sealed with paraffin, and pressurized at a constant pressure of 1.2 MPa for 24 hours on an impermeability tester. The water penetration height of the specimens was measured according to the specifications.

[0107] 3. The cubic specimens cured for 28 days under standard conditions were processed into cubes with dimensions of 10mm×10mm×10mm, subjected to CT scans, and the porosity of each group of recycled concrete was calculated according to the method of this patent, and its water penetration height was calculated. Table 1 shows the porosity of each group of recycled concrete, the predicted value D of the water penetration height mp and the test value D of the water penetration height me .

[0108] Table 1 Porosity of Recycled Concrete, Predicted Value of Water Penetration Height and Test Value of Water Penetration Height

[0109]

[0110] As Figure 3 shown, it is a comparison chart of the predicted value and the test value of the water penetration height of recycled concrete under different porosities.

[0111] According to Table 1, the MSE of the predicted result was calculated to be 0.0366 (less than 0.1), and R 2 was 0.9537 (greater than 0.95), indicating that the predicted result was very close to the actual test value.

[0112] Specifically, the strength of the structural parameters of recycled concrete can be used as a key parameter to measure the influence of the pores of recycled concrete on its impermeability performance. Through the impermeability performance parameters of recycled concrete, in combination with other parameters affecting the impermeability performance of recycled concrete, the influence of the pore structure on the impermeability performance of recycled concrete can be effectively distinguished, and it will not interfere with other parameters affecting the impermeability performance of recycled concrete.

[0113] Specifically, the impermeability performance parameters of recycled concrete can be used to measure the influence of pores on the impermeability performance of recycled concrete.

[0114] In an exemplary embodiment, as Figure 4 shown, the method for evaluating the impermeability performance of recycled concrete further includes the following steps 402 to 404. Among them:

[0115] Step 402: Cut the dried recycled concrete into cubes with side lengths ranging from 10 mm to 25 mm or spheres with diameters ranging from 10 mm to 25 mm to obtain recycled concrete specimens.

[0116] Step 404: Conduct a CT scan on the recycled concrete specimens to obtain three-dimensional image data of the recycled concrete.

[0117] To further illustrate the solution of this application, a specific example is given below. As Figure 5 shown, the images obtained by the existing image analysis method for processing images cannot clearly reflect the pores and edge structures of recycled concrete. There will not only be black noise in the recycled concrete matrix, but also white noise in the pores in the black area, resulting in a large error in porosity calculation and seriously affecting the subsequent calculation and evaluation of connectivity. At the same time, the segmentation process of the recycled concrete matrix is relatively rough. The outside of the recycled concrete matrix is the air part, and the existing image analysis method fails to clearly segment the edge of the recycled concrete.

[0118] The process of performing image enhancement and denoising preprocessing on the obtained three-dimensional image data of recycled concrete to obtain a microscopic structure data set of recycled concrete provided by the embodiments of this application is as Figure 6 shown. The steps include:

[0119] Step 1: Cut the recycled concrete specimens and then conduct a CT scan to obtain a microscopic structure image data set (microscopic structure data set) of the corresponding cross-section of the recycled concrete.

[0120] Among them, the image presented by the microscopic structure data set of the recycled concrete after cutting and CT scanning is as Figure 7 shown, with unclear picture edges, indistinct boundaries between aggregates and concrete, and small pores that are difficult to distinguish due to excessive noise.

[0121] Step 2: After performing the first shadow darkening and highlight brightening on the recycled concrete CT scan image (microscopic structure image data set), perform denoising processing to obtain the microscopic structure dark data set of the recycled concrete.

[0122] Specifically, based on OpenCV, use the cv2.imread command to read the three-dimensional reconstruction image data set of the microscopic structure of the recycled concrete.

[0123] Furthermore, adopt the method of shadow darkening and highlight brightening to enhance the contrast between the pores and the concrete matrix boundary in the image, make the image clearer and easier to analyze, and apply the median denoising algorithm to eliminate possible image distortion and noise. The formula is as follows:

[0124]

[0125] ksize = 3

[0126] where S a is the shadow value after dimming the three - dimensional image data; S 0 is the initial shadow value of the three - dimensional image data; s is the first preset shadow value; H a is the highlight value after brightening the three - dimensional image data; H S is the highlight value after darkening the image in the three - dimensional image data; h is the first preset highlight value; ksize is the filter kernel size of the first median denoising process.

[0127] Step 3: Perform color inversion, second - time shadow darkening, second - time highlight brightening, and contrast enhancement on the dark dataset to obtain the revert dataset of the microstructure of recycled concrete.

[0128] Specifically, perform color inversion on the data (pre - enhanced image) in the dark dataset, and further adjust the shadow, highlight, and contrast to highlight the difference between pores and the concrete matrix, improving the information expression ability and analysis effect of the image. The formula is as follows:

[0129] Ca ij = 255 - C ij

[0130]

[0131] where Ca ij is the adjusted gray value of the pixel at the i - th row and j - th column in the three - dimensional image data; C ij is the initial gray value of the pixel at the i - th row and j - th column in the three - dimensional image data; S a ’ is the shadow value after darkening the image in the three - dimensional image data; S 0 ’ is the initial shadow value of the image in the three - dimensional image data; s’ is the second preset shadow value; H a ’ is the highlight value after brightening the image in the three - dimensional image data; H S ’ is the highlight value after darkening the image in the three - dimensional image data; h’ is the second preset highlight value.

[0132] Specifically, by performing color inversion on the dark dataset, second - time adjusting the shadow and second - time adjusting the highlight, and adjusting the contrast again, the boundary between pores and the recycled concrete matrix in the inverted image becomes clearer, obtaining the revert dataset of the cross - section microstructure of recycled concrete.

[0133] Specifically, the specific process of performing color inversion, second shadow darkening, second highlight brightening, and contrast enhancement on the dark dataset is as follows Figure 8 shown. The process includes:

[0134] After obtaining the images (grayscale images) in the dark dataset, it further includes: loading the grayscale image; obtaining the height and width of the grayscale image; setting i = 0 and j = 0; determining whether i is less than width, if so, determining whether j is less than height, if not, adjusting the shadow of the grayscale image; after determining whether j is less than height, if so, adjusting the gray value of the corresponding pixel and performing j++, if not, performing i++ and re-determining whether i is less than width, after adjusting the shadow of the grayscale image, adjusting the highlight of the grayscale image. Adjust the contrast of the grayscale image, save the grayscale image, and finally end.

[0135] Step 4: Perform median denoising on the revert dataset, remove the black dots in the image, eliminate the distorted data, and obtain the blur dataset of the more real microstructure of recycled concrete.

[0136] Specifically, apply the median denoising algorithm again to remove the possible black dots and noise in the image after color inversion, and obtain the blur dataset that clearly and truly reflects the microstructure. The formula is as follows:

[0137] ksize = 15

[0138] Step 5: As Figure 9 shown, extract the images in the blur dataset through the Canny edge detection algorithm in OpenCV to obtain and save the edge strips and pore boundaries of the recycled concrete cross-section, and obtain the image edge data and pore boundary data of the recycled concrete to reflect the real and accurate pore structure of the recycled concrete.

[0139] The method for processing recycled concrete CT images and extracting pore boundaries based on OpenCV of the present invention uses image processing and computer vision technologies to increase the contrast between pores and the recycled concrete matrix in CT scan images and extract the image edges and pore boundaries, making the most of CT scan data. Compared with existing analysis methods such as Avizo, it can obtain a more accurate pore structure of the recycled concrete cross-section. The pore boundaries of each obtained image are closer to the original image than the threshold segmentation in the Avizo modeling process, and can be used as an effective tool for studying and analyzing the pore structure of recycled concrete, providing an effective analysis tool for the research on the diffusion process and erosion mechanism of external media in recycled concrete.

[0140] It should be understood that although the steps in the flowcharts involved in the above embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0141] Based on the same inventive concept, an embodiment of the present application further provides a device for evaluating the impermeability performance of recycled concrete for implementing the method for evaluating the impermeability performance of recycled concrete involved above. The solution provided by this device for solving problems is similar to the solution recorded in the above method. Therefore, the specific limitations in one or more embodiments of the device for evaluating the impermeability performance of recycled concrete provided below can refer to the limitations on the method for evaluating the impermeability performance of recycled concrete in the above text, and will not be elaborated here.

[0142] In an exemplary embodiment, as Figure 10 shown, a device 1000 for evaluating the impermeability performance of recycled concrete is provided, including:

[0143] A preprocessing module 1001, configured to perform image enhancement and denoising preprocessing on the obtained three-dimensional image data of recycled concrete to obtain a microscopic structure data set of recycled concrete;

[0144] A data reading module 1002, configured to read the microscopic structure data set through OpenCV to obtain image edge data and pore boundary data of recycled concrete;

[0145] A calculation module 1003, configured to calculate the porosity of recycled concrete according to the image edge data and the pore boundary data;

[0146] An evaluation module 1004, configured to evaluate the impermeability performance of recycled concrete according to the porosity of recycled concrete.

[0147] In an exemplary embodiment, the steps of image enhancement and denoising preprocessing include:

[0148] Enhance the contrast of the three-dimensional image data and perform the first median denoising process; enhancing the contrast of the three-dimensional image data includes brightening the three-dimensional image data and dimming the shadow of the three-dimensional image data; the formula is as follows:

[0149]

[0150] ksize = 3

[0151] Where S a is the shadow value after dimming the three-dimensional image data; S 0 is the initial shadow value of the three-dimensional image data; s is the first preset shadow value; H a is the highlight value after brightening the three-dimensional image data; H S is the highlight value after dimming the image in the three-dimensional image data; h is the first preset highlight value; ksize is the filter kernel size for the first median denoising process.

[0152] In an exemplary embodiment, the steps of image enhancement and denoising preprocessing further include:

[0153] Performing color inversion processing and a second median denoising process on the three-dimensional image data after the first median denoising process; the color inversion processing includes adjusting the gray value of the three-dimensional image data, dimming the shadow of the three-dimensional image data again, and brightening the three-dimensional image data again; the formula is as follows:

[0154] Ca ij = 255 - C ij

[0155]

[0156] Where Ca ij is the adjusted gray value of the pixel at the i-th row and j-th column in the three-dimensional image data; C ij is the initial gray value of the pixel at the i-th row and j-th column in the three-dimensional image data; S a ’ is the shadow value after dimming the image in the three-dimensional image data; S 0 ’ is the initial shadow value of the image in the three-dimensional image data; s’ is the second preset shadow value; H a ’ is the highlight value after brightening the image in the three-dimensional image data; H S ’ is the highlight value after dimming the image in the three-dimensional image data; h’ is the second preset highlight value.

[0157] In an exemplary embodiment, the calculation module includes:

[0158] An acquisition unit, configured to acquire the cross-sectional area data of the corresponding recycled concrete according to the image edge data;

[0159] The acquisition unit is further configured to acquire the cross-sectional area data of each corresponding pore in the recycled concrete according to the pore boundary data;

[0160] A first calculation unit, configured to obtain the porosity of recycled concrete according to the cross-sectional area data of the recycled concrete and the cross-sectional area data of each pore in the recycled concrete.

[0161] In an exemplary embodiment, the evaluation module further includes:

[0162] A second calculation unit, configured to predict the seepage height of recycled concrete under the seepage height method according to the following formula:

[0163] D m = 4.88 - 0.37P + 0.04P 2

[0164] In the formula, D m is the predicted seepage height value of recycled concrete under the seepage height method, with the unit of mm; P is the porosity of recycled concrete;

[0165] The second calculation unit is further configured to calculate the relative permeability coefficient of the recycled concrete as an evaluation index of the impermeability performance according to the following formula;

[0166]

[0167] In the formula, K r is the relative permeability coefficient, with the unit of mm / h; a is the water absorption rate of the recycled concrete; t is the constant pressure time, with the unit of h; H is the water pressure, expressed as the height of the water column, with the unit of mm; t takes 24 h, and H takes 122400 mm;

[0168] An evaluation unit, configured to evaluate the impermeability performance of the recycled concrete according to the relative permeability coefficient.

[0169] In an exemplary embodiment, the impermeability performance evaluation device for recycled concrete further includes:

[0170] A cutting module, configured to cut the dried recycled concrete into cubes with side lengths of 10 mm to 25 mm or spheres with diameters of 10 mm to 25 mm to obtain recycled concrete specimens;

[0171] A scanning module, configured to perform CT scanning on the recycled concrete specimens to obtain three-dimensional image data of the recycled concrete.

[0172] Each module in the above impermeability performance evaluation device for recycled concrete can be implemented in whole or in part through software, hardware, and their combinations. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0173] In an exemplary embodiment, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the anti-seepage performance evaluation method of recycled concrete provided above in the present application are implemented.

[0174] In an exemplary embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the anti-seepage performance evaluation method of recycled concrete provided above in the present application are implemented.

[0175] In an exemplary embodiment, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the anti-seepage performance evaluation method of recycled concrete provided above are implemented.

[0176] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0177] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0178] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for evaluating the impermeability of recycled concrete, characterized in that: The method comprises: The acquired three-dimensional image data of recycled concrete is subjected to image enhancement and denoising preprocessing to obtain a microstructure data set of recycled concrete; the steps of image enhancement and denoising preprocessing include: enhancing the contrast of the three-dimensional image data and performing a first median denoising process; enhancing the contrast of the three-dimensional image data includes brightening the three-dimensional image data and darkening the shadow of the three-dimensional image data; the formula is as follows: ksize=3 In the formula, S a is the shadow value after dimming the three-dimensional image data; S0 is the initial shadow value of the three-dimensional image data; s is the first preset shadow value; H a H is the highlight value after brightening the three-dimensional image data; S is the highlight value after the image darkening process in the three-dimensional image data; h is the first preset highlight value; ksize is the filter kernel size of the first median denoising process; The three-dimensional image data after the first median denoising process is subjected to color inversion processing and second median denoising processing; the color inversion processing includes adjusting the grayscale value of the three-dimensional image data, darkening the shadow of the three-dimensional image data again, and brightening the three-dimensional image data again; the formula is as follows: That ij =255-C ij Where Ca ij is the gray value of the pixel in row i and column j after adjustment in the three-dimensional image data; C ij is the initial grayscale value of the pixel in row i and column j in the three-dimensional image data; S a ' is the shadow value after the image is darkened in the three-dimensional image data; S0' is the initial shadow value of the image in the three-dimensional image data; s' is the second preset shadow value; H a ' is the highlight value after the image is brightened in the three-dimensional image data; H S ' is the highlight value after the image darkening process in the three-dimensional image data; h' is the second preset highlight value; Read the microstructure data set through OpenCV to obtain image edge data and pore boundary data of the recycled concrete; Calculating the porosity of the recycled concrete according to the image edge data and the pore boundary data; The anti-permeability performance of the recycled concrete is evaluated according to the porosity of the recycled concrete; the step of evaluating the anti-permeability performance of the recycled concrete according to the porosity of the recycled concrete comprises: predicting the seepage height under the seepage height method of recycled concrete with different porosities according to the following formula: D m =4.88-0.37P+0.04P 2 Where D m is the predicted value of the seepage height of recycled concrete under the seepage height method, in mm; P is the porosity of recycled concrete; The relative permeability coefficient of recycled concrete is calculated according to the following formula as an evaluation index of anti-permeability performance; In the formula, K r is the relative permeability coefficient, in mm / h; a is the water absorption rate of recycled concrete; t is the constant pressure time, in h; H is the water pressure, expressed as the height of the water column, in mm; t is 24h, and H is 122400mm; The anti-permeability performance of the recycled concrete is evaluated according to the relative permeability coefficient.

2. The method according to claim 1, characterized in that The step of calculating the porosity of the recycled concrete according to the image edge data and the pore boundary data comprises: Acquiring corresponding cross-sectional area data of the recycled concrete according to the image edge data; Acquire cross-sectional area data of each corresponding pore in the recycled concrete according to the pore boundary data; The porosity of the recycled concrete is obtained according to the cross-sectional area data of the recycled concrete and the cross-sectional area data of each pore in the recycled concrete.

3. The method according to claim 1, characterized in that The method further comprises: Cutting the dried recycled concrete into cubes with a side length of 10 mm to 25 mm or spheres with a diameter of 10 mm to 25 mm to obtain recycled concrete specimens; The recycled concrete specimen is subjected to CT scanning to obtain three-dimensional image data of the recycled concrete.

4. A pore boundary extraction device for recycled concrete, characterized in that: Based on the method according to any one of claims 1 to 3, the device comprises: A preprocessing module is used to perform image enhancement and denoising preprocessing on the acquired three-dimensional image data of recycled concrete to obtain a microstructure data set of recycled concrete; A data reading module, used to read the microstructure data set through OpenCV to obtain image edge data and pore boundary data of the recycled concrete; A calculation module, used for calculating the porosity of the recycled concrete according to the image edge data and the pore boundary data; An evaluation module is used to evaluate the anti-permeability performance of the recycled concrete according to the porosity of the recycled concrete.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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