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

Abstract

The invention relates to a recycled concrete anti-permeability performance evaluation method, device and equipment and a storage medium. The method comprises the following steps: carrying out image enhancement and de-noising preprocessing on obtained three-dimensional image data of recycled concrete to obtain a microstructure data set of the recycled concrete; reading 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; evaluating the impermeability of the recycled concrete according to the porosity of the recycled concrete; by adopting the method, the problem that the method for evaluating the impermeability performance of the recycled concrete is not accurate due to the unclear structure of the recycled concrete can be effectively solved.

Description

Technical Field

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

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

[0003] However, the density of recycled concrete is poor. It cannot cut off the continuity of pore channels like natural aggregates, and even provides new channels for the circulation of water, resulting in more cracks and poorer reliability compared to natural aggregate concrete. Therefore, evaluating the impermeability of recycled concrete is crucial for the application of recycled concrete. At the same time, the impermeability of recycled concrete is closely related to its porosity. CT scanning can not only reconstruct recycled concrete in three dimensions, but also calculate the porosity of recycled concrete and restore its porosity. However, the current three-dimensional reconstruction software has poor processing effect on non-homogeneous materials such as recycled concrete. Therefore, a recycled concrete CT scanning image processing technology is needed to effectively use the test results to calculate the porosity of recycled concrete, and establish a prediction model for the impermeability of recycled concrete based on this. Summary of the invention

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

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

[0006] 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;

[0007] The microstructure data set is read through OpenCV to obtain the image edge data and pore boundary data of recycled concrete;

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

[0009] The anti-permeability performance of recycled concrete was evaluated based on its porosity.

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

[0011] The contrast of the three-dimensional image data is enhanced, and the first median denoising process is performed; 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:

[0012]

[0013] ksize=3

[0014] 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 The highlight value after brightening the 3D image data; H S is the highlight value after the image is darkened 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 image enhancement and denoising preprocessing steps further include:

[0016] 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:

[0017] Ca ij =255-C ij

[0018]

[0019] 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 is darkened in the three-dimensional image data; h' is the second preset highlight value.

[0020] In one embodiment, the step of calculating the porosity of the recycled concrete according to the image edge data and the pore boundary data comprises:

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

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

[0023] 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.

[0024] In one embodiment, the step of evaluating the anti-permeability performance of the recycled concrete according to the porosity of the recycled concrete comprises:

[0025] The seepage height of recycled concrete with different porosity is predicted according to the following formula:

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

[0027] 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;

[0028] The relative permeability coefficient of recycled concrete is calculated according to the following formula as an evaluation index of anti-permeability performance;

[0029]

[0030] 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;

[0031] The anti-permeability performance of recycled concrete was evaluated based on the relative permeability coefficient.

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

[0033] 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;

[0034] The recycled concrete specimens were CT scanned to obtain three-dimensional image data of the recycled concrete.

[0035] The second aspect of the present application also provides a pore boundary extraction device for recycled concrete, the device comprising:

[0036] 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;

[0037] The data reading module is used to read the microstructure data set through OpenCV to obtain the image edge data and pore boundary data of the recycled concrete;

[0038] A calculation module, used for calculating the porosity of the recycled concrete according to the image edge data and the pore boundary data;

[0039] Evaluation module for evaluating the anti-permeability performance of recycled concrete based on its porosity.

[0040] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method provided in the first aspect of the present application when executing the computer program.

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

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

[0043] The above-mentioned method, device, equipment and storage medium for evaluating the impermeability of recycled concrete perform image enhancement and denoising preprocessing on the three-dimensional image data of the recycled concrete to obtain a microstructure data set that can fully reflect the pore structure of the recycled concrete without cropping the three-dimensional image data, and then read the image edge data and pore boundary data of the recycled concrete through OpenCV, and calculate the porosity of the recycled concrete through the image edge data and the pore boundary data respectively, so as to further obtain the impermeability of the recycled concrete with the porosity as the main parameter, effectively solving the problem of inaccurate evaluation method of the impermeability of the recycled concrete due to unclear structure of the recycled concrete. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A flowchart of the steps of a method for evaluating the impermeability of recycled concrete in one embodiment;

[0046] Figure 2 A flow chart of the steps for calculating porosity in one embodiment;

[0047] Figure 3 An embodiment is a comparison diagram of the predicted value and the experimental value of the water seepage height of recycled concrete under different porosities;

[0048] Figure 4 A flowchart of steps for obtaining three-dimensional image data of recycled concrete in one embodiment;

[0049] Figure 5 A pore structure diagram of recycled concrete after being processed by an existing image analysis method in one embodiment;

[0050] Figure 6 A flowchart of obtaining a microstructure data set of recycled concrete in one embodiment;

[0051] Figure 7 is a diagram of the untreated pore structure of recycled concrete in one embodiment;

[0052] Figure 8 A flowchart of grayscale adjustment in one embodiment;

[0053] Fig. 9 is a pore structure diagram of recycled concrete obtained by reading a microstructure data set in one embodiment;

[0054] Fig.10 It is a structural diagram of a device for evaluating the impermeability of recycled concrete in one embodiment. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with 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 aggregate is not as single and complete as natural aggregate and has a dense structure, some old mortar is attached to its surface, and there are cracks caused by the crushing process, which undoubtedly makes the pore structure of recycled concrete more complex than that of natural aggregate concrete. At the same time, the old mortar attached to the surface is not conducive to the bonding between recycled aggregate and new mortar, making the density of recycled concrete poor. It not only cannot cut off the continuity of pore channels like natural aggregate, but even provides new channels for the circulation of water. The old mortar attached to the surface of recycled aggregate will also make recycled concrete have higher water absorption. The new cement paste loses more water during the coagulation and hardening process, causing more pores and cracks in the interface transition zone of recycled concrete. Therefore, the relevant modification research of recycled concrete is related to improving the internal pore structure of recycled concrete.

[0057] In an exemplary embodiment, Figure 1As shown, a method for evaluating the impermeability 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 acquired three-dimensional image data of the recycled concrete to obtain a microstructure data set of the recycled concrete.

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

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

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

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

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

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

[0065] Step 108: evaluating the anti-permeability performance of the recycled concrete according to the porosity of the recycled concrete.

[0066] It is understandable that the pores in recycled concrete have a crucial influence on the impermeability of recycled concrete. Both porous recycled concrete and recycled concrete with larger pore cross-sectional area have a fatal impact on the impermeability. Therefore, the porosity measured by the cross-sectional area of ​​the pores and the evaluation of the impermeability of recycled concrete have become an important part of the research on related modification of recycled concrete. On the other hand, the impermeability of recycled concrete also determines its structural strength, so the evaluation of the impermeability of recycled concrete is very important.

[0067] The above-mentioned method for evaluating the impermeability of recycled concrete performs image enhancement and denoising preprocessing on the three-dimensional image data of the recycled concrete to obtain a microstructure data set that can fully reflect the pore structure of the recycled concrete without cropping the three-dimensional image data, and then reads the image edge data and pore boundary data of the recycled concrete through OpenCV, and calculates the porosity of the recycled concrete through the image edge data and the pore boundary data, respectively, to further obtain the impermeability of the recycled concrete with the porosity as the main parameter, effectively solving the problem of inaccurate evaluation method of the impermeability of the recycled concrete due to unclear recycled concrete structure.

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

[0069] The contrast of the three-dimensional image data is enhanced, and the first median denoising process is performed; 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:

[0070]

[0071] ksize=3

[0072] 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 The highlight value after brightening the 3D image data; H S is the highlight value after the image is darkened 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 as parameters that can reflect the best image effect based on the image characteristics of concrete in a large number of practices. For example, in the embodiment of the present application, the best shadow value can be 80, the best highlight value can be 50, and the best filter kernel size can be 3 to retain more image details.

[0074] Specifically, the three-dimensional image data is brightened to highlight the details and features in the image and to enhance the overall brightness of the image, attempting to make it fuller while repairing the dark details in the image, thereby improving the overall quality and visual effect of the image; then, combined with the shadow dimming processing of the three-dimensional image data, the contrast in the image can be enhanced, and the details and texture of the shadow will be clearer and more prominent. Compared with the prior art method of enlarging and repairing the image, the integrity of the image can be fully retained so that the data volume is not compressed, and the influence of pores on the anti-seepage performance can be more accurately reflected.

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

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

[0077] 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:

[0078] Ca ij =255-C ij

[0079]

[0080] 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 is darkened in the three-dimensional image data; h' is the second preset highlight value.

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

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

[0083] Furthermore, after the three-dimensional image is brightened and the shadow is dimmed again, the three-dimensional image is subjected to a second median denoising process. The formula is as follows:

[0084] ksize=15

[0085] Specifically, the optimal filter kernel size for the second median denoising may be 15, which removes more noise caused by image adjustment.

[0086] In an exemplary embodiment, Figure 2 As shown, the step of calculating the porosity of recycled concrete according to the image edge data and the pore boundary data includes the following steps 202 to 204. Among them:

[0087] Step 202: Obtain corresponding cross-sectional area data of 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 : acquiring cross-sectional area data of each corresponding pore in the recycled concrete according to the pore boundary data.

[0090] Step 206: 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.

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

[0092] In an exemplary embodiment, the step of evaluating the anti-permeability performance of the recycled concrete according to the porosity of the recycled concrete includes the following steps 302 to 306. Among them:

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

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

[0095] 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.

[0096] Among them, the seepage height method is used to recycle the anti-seepage capacity of concrete or other materials. It is used to determine the anti-seepage grade of the material by measuring the seepage height, so as to judge its permeability performance under different water pressures.

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

[0098] Step 304, calculating the relative permeability coefficient of the recycled concrete according to the following formula as an evaluation index of the anti-permeability performance;

[0099]

[0100] 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.

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

[0102] Step 306, evaluating the anti-permeability performance of the recycled concrete according to the relative permeability coefficient.

[0103] Specifically, a preset parameter can be set as a threshold value for the anti-permeability performance of the recycled concrete based on the relative permeability coefficient in a limited number of experiments or in a specific implementation process. When the calculated result of the relative permeability coefficient exceeds the threshold value, a prompt message indicating that the anti-permeability performance of the recycled concrete is poor is output; otherwise, a prompt message indicating that the anti-permeability performance is normal is output. There is no specific restriction on the setting of the preset parameters.

[0104] In one embodiment, the step of evaluating the anti-permeability performance of the recycled concrete according to the porosity of the recycled concrete may include the following scheme:

[0105] 1. Prepare recycled concrete with the label number C30 and the recycled aggregate replacement rate of 30%, 50%, 70% and 100%, respectively, which are numbered R 30 , R 50 , R 70 , R 100 , and NAC which used all natural aggregates as the control group. Each group included one cubic specimen with the size of 100mm×100mm×100mm, and six truncated cone specimens with the upper diameter of 175mm, the lower diameter of 185mm and the height of 150mm.

[0106] 2. After the specimen is demoulded, the cement slurry film on both ends of the truncated cone specimen is polished off with a grinder, and the specimen is immediately sent to a standard curing room for curing. When the curing age reaches 27 days, the truncated cone specimen is taken out and wiped clean, and then sealed with paraffin after drying. The specimen is pressurized at a constant pressure of 1.2MPa for 24 hours on a permeability tester, and the water seepage height of the specimen is tested according to the specification.

[0107] 3. The cube specimens with standard curing for 28 days were processed into 10mm×10mm×10mm cubes, and CT scans were performed. The porosity of each group of recycled concrete and its seepage height were calculated according to the method of this patent. Table 1 shows the porosity and seepage height prediction values ​​of each group of recycled concrete D mp And water seepage height test value D me .

[0108] Table 1 Porosity, predicted seepage height and experimental seepage height of recycled concrete

[0109]

[0110] like Figure 3 As shown in the figure, it is a comparison chart of the predicted value and experimental value of the water seepage height of recycled concrete under different porosities.

[0111] According to Table 1, the MSE of the prediction results is 0.0366 (less than 0.1), R 2 It is 0.9537 (greater than 0.95), which means that the predicted result is very close to the actual test value.

[0112] Specifically, the structural parameter strength of recycled concrete can be used as a key parameter to measure the influence of the pores of recycled concrete on the anti-permeability of recycled concrete. Through the anti-permeability performance parameters of recycled concrete, the influence of the pore structure on the anti-permeability of recycled concrete can be effectively distinguished in combination with other parameters that affect the anti-permeability of recycled concrete, and it can avoid interfering with other parameters that affect the anti-permeability of recycled concrete.

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

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

[0115] Step 402: Cut 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.

[0116] Step 404: Perform CT scanning on the recycled concrete specimen to obtain three-dimensional image data of the recycled concrete.

[0117] In order to further explain the solution of the present application, a specific example is given below. Figure 5As shown in the figure, the image obtained by the existing image analysis method for processing the image cannot clearly reflect the pore and edge structure of the recycled concrete. Not only black noise will appear in the recycled concrete matrix, but also white noise will appear in the pores in the black area, which will lead to a large error in the porosity calculation and seriously affect the subsequent calculation and evaluation of connectivity. At the same time, the segmentation of the recycled concrete matrix is ​​relatively rough, and the air part is outside the recycled concrete matrix. 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 acquired three-dimensional image data of recycled concrete to obtain a microstructure data set of recycled concrete provided in the embodiment of the present application is as follows: Figure 6 As shown, the steps include:

[0119] Step 1: Cut the recycled concrete specimen and perform CT scanning to obtain a corresponding microstructure image dataset (microstructure dataset) of the recycled concrete cross section.

[0120] The images presented in the microstructure dataset of the recycled concrete after cutting and CT scanning are as follows: Figure 7 As shown in the figure, the image edges are not clear enough, the boundary between aggregate and concrete is not obvious enough, and there are too many noises that cause small pores that are difficult to distinguish.

[0121] Step 2: After the first shadow darkening and highlight brightening of the recycled concrete CT scan image (microstructure image dataset), denoising is performed to obtain the dark dataset of the recycled concrete microstructure.

[0122] Specifically, based on OpenCV, the cv2.imread command is used to read the three-dimensional reconstructed image dataset of the recycled concrete microstructure.

[0123] Furthermore, the method of darkening shadows and brightening highlights is adopted to enhance the contrast between the pores and the boundaries of the concrete matrix in the image, making the image clearer and easier to analyze, and the median denoising algorithm is applied to eliminate possible image distortion and noise. The formula is as follows:

[0124]

[0125] ksize=3

[0126] 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; Sis the highlight value after the image darkening processing in the three-dimensional image data; h is the first preset highlight value; ksize is the filter kernel size of the first median denoising processing.

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

[0128] Specifically, the data in the dark dataset (preliminary enhanced images) are inverted, and the shadows, highlights, and contrast are further adjusted to highlight the difference between the pores and the concrete matrix, thereby improving the image's information expression capability and analysis effect. The formula is as follows:

[0129] Ca ij =255-C ij

[0130]

[0131] 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 processing in the three-dimensional image data; h' is the second preset highlight value.

[0132] Specifically, by inverting the dark dataset, adjusting the shadows and highlights for the second time, and adjusting the contrast again, the pores in the inverted image and the boundary between the recycled concrete matrix are made clearer, and the revert dataset of the microstructure of the recycled concrete cross section is obtained.

[0133] Specifically, the dark dataset is subjected to the specific process of inverting the color, darkening the shadows for the second time, brightening the highlights for the second time, and enhancing the contrast, as shown in the following figure. Figure 8 As shown, the process includes:

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

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

[0136] Specifically, the median denoising algorithm is applied again to remove the black spots and noise that may appear in the image after the inversion processing, and a blur data set that clearly reflects the microstructure is obtained. The formula is as follows:

[0137] ksize=15

[0138] Step 5: Fig. 9 As shown, the Canny edge detection algorithm in OpenCV is used to extract the image in the blur data set to obtain and save the recycled concrete cross-section edge strips and pore boundary maps, and the image edge data and pore boundary data of the recycled concrete are obtained to reflect the real and accurate recycled concrete pore structure.

[0139] The OpenCV-based recycled concrete CT image processing and pore boundary extraction method of the present invention adopts image processing and computer vision technology to increase the contrast between the pores and the recycled concrete matrix in the CT scan image and extract the image edge and pore boundary, making the most of the CT scan data. Compared with the existing analysis methods such as Avizo, a more accurate cross-sectional pore structure of recycled concrete can be obtained. The pore boundary of each image obtained is closer to the original image than the threshold segmentation in the Avizo modeling process. It can be used as an effective tool for studying and analyzing the pore structure of recycled concrete, and provides an effective analysis tool for 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 displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, 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, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0141] Based on the same inventive concept, the embodiment of the present application also provides a device for evaluating the impermeability of recycled concrete for implementing the above-mentioned method for evaluating the impermeability of recycled concrete. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more devices for evaluating the impermeability of recycled concrete provided below can refer to the limitations of the method for evaluating the impermeability of recycled concrete above, and will not be repeated here.

[0142] In an exemplary embodiment, Fig.10 As shown, a device 1000 for evaluating the impermeability of recycled concrete is provided, comprising:

[0143] A preprocessing module 1001 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;

[0144] The data reading module 1002 is used to read the microstructure data set through OpenCV to obtain the image edge data and pore boundary data of the recycled concrete;

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

[0146] The evaluation module 1004 is used to evaluate the anti-permeability performance of the recycled concrete according to the porosity of the recycled concrete.

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

[0148] The contrast of the three-dimensional image data is enhanced, and the first median denoising process is performed; 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:

[0149]

[0150] ksize=3

[0151] 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 The highlight value after brightening the 3D image data; H S is the highlight value after the image is darkened 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.

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

[0153] 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:

[0154] Ca ij =255-C ij

[0155]

[0156] 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 is darkened 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, used for acquiring corresponding cross-sectional area data of recycled concrete according to the image edge data;

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

[0160] The first calculation unit is used to 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.

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

[0162] The second calculation unit is used to predict the seepage height of recycled concrete with different porosities according to the following formula:

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

[0164] 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;

[0165] The second calculation unit is also used to calculate the relative permeability coefficient of the recycled concrete as an evaluation index of the anti-permeability performance according to the following formula;

[0166]

[0167] 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;

[0168] An evaluation unit is used to evaluate the anti-permeability performance of the recycled concrete according to the relative permeability coefficient.

[0169] In an exemplary embodiment, the device for evaluating the anti-permeability performance of recycled concrete further comprises:

[0170] A cutting module, used for 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;

[0171] The scanning module is used to perform CT scanning on the recycled concrete specimen to obtain three-dimensional image data of the recycled concrete.

[0172] Each module in the above-mentioned recycled concrete anti-permeability performance evaluation device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0173] In an exemplary embodiment, the present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for evaluating the impermeability performance of recycled concrete provided above in the present application are implemented.

[0174] In an exemplary embodiment, the present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method for evaluating the impermeability performance of recycled concrete as provided above in the present application are implemented.

[0175] In an exemplary embodiment, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for evaluating the impermeability performance of recycled concrete provided above.

[0176] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0177] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0178] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating the impermeability of recycled concrete. It is characterized in that The method comprises: 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; 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.

2. The method according to claim 1, It is characterized in that The steps of image enhancement and denoising preprocessing include: The contrast of the three-dimensional image data is enhanced, and a first median denoising process is performed; the 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 S is the shadow value after the three-dimensional image data is dimmed; 0 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 processing in the three-dimensional image data; h is the first preset highlight value; ksize is the filter kernel size of the first median denoising processing.

3. The method according to claim 2, It is characterized in that The steps of image enhancement and denoising preprocessing also include: 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; 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 is brightened in the three-dimensional image data; H S ' is the highlight value after the image darkening processing in the three-dimensional image data; h' is the second preset highlight value.

4. The method according to claim 1, It is 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.

5. The method according to claim 4, It is characterized in that The step of evaluating the anti-permeability performance of the recycled concrete according to the porosity of the recycled concrete comprises: The seepage height of recycled concrete with different porosity is predicted 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.

6. The method according to claim 1, It is 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.

7. A pore boundary extraction device for recycled concrete, It is characterized in that 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.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program. It is characterized in that When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, It is 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 6 are implemented.

10. A computer program product comprising a computer program, It is 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 6 are implemented.

Images