Concrete abrasion resistance evaluation method based on surface reconstruction

Abstract

The invention discloses a surface reconstruction-based concrete abrasion resistance evaluation method. The method comprises the following steps: S1, acquiring image information of a to-be-evaluated concrete member by adopting a fixed-axis rotary photography method; s2, performing front-end and rear-end processing on the image information by adopting a visual SLAM technology, and outputting a concrete member point cloud; s3, carrying out secondary sampling on the point cloud of the concrete member to obtain a high-quality point cloud, and carrying out positioning correction treatment; then, carrying out classified interception on the corrected high-quality point cloud to obtain a point cloud model of the punching and grinding surface; s4, performing surface reconstruction on the punching and grinding surface point cloud model based on Delaunay2.5 D to obtain a punching and grinding surface model; then calculating the area of the punching and grinding surface and the punching and grinding loss volume according to the punching and grinding surface model; and S5, calculating the distance difference between the point cloud in the point cloud model of the impact grinding surface of the concrete member after impact grinding and the impact grinding surface of the impact grinding surface model of the concrete member before impact grinding, and then evaluating the impact grinding degrees of different positions of the concrete member according to the distance difference.

Description

Technical Field

[0001] The invention relates to the field of concrete anti-abrasion performance evaluation, and in particular to a concrete anti-abrasion performance evaluation method based on surface reconstruction. Background Art

[0002] The abrasion resistance of concrete refers to the ability of concrete to resist damage and wear when it is impacted by water or solid particles at high speed. It has a great impact on the life and aesthetics of concrete. The abrasion resistance of concrete is crucial to ensure the long-term stability and durability of engineering structures. It is an important quality assessment indicator in the construction of hydraulic structures, ports, and tunnel projects. The main testing methods for the abrasion resistance of concrete include the wear test method and the steel ball wear method.

[0003] Patent application CN118730790A discloses a concrete anti-abrasion test device and method, and specifically discloses that the device includes a test carrier, the test carrier has a sand box, a filter box and a water tank that are at least partially connected inside; the test block fixing table is located obliquely above the sand box, on which multiple test blocks and comparison test blocks are fixed by clamps, and the multiple test blocks and comparison test blocks are in water or sand water; the nozzle is movably connected to the top of the test block fixing table; the top of the nozzle is connected to a water pipe, and the side facing the sand box is connected to a sand suction pipe entering the sand box; the sand particles are sucked into the nozzle driven by high-speed water flow to form sand water; wherein, the top of the sand box has an overflow hole, the overflow hole is connected to the filter box, the water outlet of the filter box is connected to the water tank, and the water tank has a water pump connected to the water pipe. The application calculates the anti-abrasion index by comparing the wear volume of the ultra-high performance concrete test block and the comparison glass test block, thereby quantitatively evaluating the wear resistance of the material under simulated environment. However, the above patent application only evaluates the die resistance by calculating the volume loss. The evaluation index is too single, lacks a comprehensive evaluation of the change in concrete performance, and lacks an intuitive explanation of the model.

[0004] Therefore, in the face of the current problem of a single evaluation index for concrete abrasion resistance assessment, developing a method for evaluating concrete abrasion resistance performance that can improve the comprehensiveness of concrete abrasion resistance assessment is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to provide a method for evaluating the anti-abrasion performance of concrete based on surface reconstruction, so as to solve the problem of single evaluation index in the existing anti-abrasion performance evaluation of concrete.

[0006] In order to solve the above technical problems, the present invention provides a method for evaluating the anti-abrasion performance of concrete based on surface reconstruction, comprising the steps of:

[0007] S1: The image information of the concrete component to be evaluated is collected by fixed-axis rotation photography;

[0008] S2: Use visual SLAM technology to process image information at the front end and back end, and output the point cloud of concrete components;

[0009] S3: Perform secondary sampling on the point cloud of the concrete component to obtain high-quality point cloud and perform positioning correction processing; then classify and intercept the corrected high-quality point cloud to obtain the punching and grinding surface point cloud model;

[0010] S4: Surface reconstruction is performed on the punching and grinding surface point cloud model based on Delaunay2.5D to obtain the punching and grinding surface model; then the punching and grinding surface area and the punching and grinding loss volume are calculated according to the punching and grinding surface model;

[0011] S5: Calculate the distance difference between the point cloud in the point cloud model of the abrasion surface of the concrete component after abrasion and the abrasion surface of the abrasion surface model of the concrete component before abrasion, and then evaluate the abrasion degree of different positions of the concrete component according to the distance difference.

[0012] Furthermore, step S1 specifically includes:

[0013] Place the concrete component to be evaluated on the fixed-axis rotating platform so that the center of the concrete component is aligned with the axis of the fixed-axis rotating platform;

[0014] An image acquisition device is set up near the fixed-axis rotating platform, and the image information of the concrete component is collected by the image acquisition device as the concrete component rotates.

[0015] Furthermore, in step S2, the front-end processing specifically includes: using visual SLAM to extract feature points from adjacent frame image information of the concrete component image information, performing feature matching on the extracted feature points, and obtaining a feature point mapping matching relationship.

[0016] Furthermore, in step S2, the back-end processing specifically includes: estimating the position of the image acquisition device, generating environmental primitives according to the matching information of the feature points, and exporting them in the form of point clouds to output the concrete component point clouds.

[0017] Furthermore, in step S3, the secondary sampling of the concrete component point cloud includes: obtaining global features of the concrete component point cloud, generating a dense point cloud according to the global features by iterating a multi-scale depth map, and obtaining a high-quality point cloud by random sampling.

[0018] Furthermore, in step S3, the positioning correction processing includes: according to the size of the standard concrete component, the scale is restored to the actual size by scaling the point cloud; according to the positioning point of the fixed-axis rotating platform, the horizontal plane is determined by picking three points, so that the normal direction of the punching and grinding surface of the high-quality point cloud is perpendicular to the fixed-axis rotating platform surface.

[0019] Furthermore, in step S3, classifying and intercepting the corrected high-quality point cloud includes: using a trained classifier to intercept the point cloud of the grinding surface area from the high-quality point cloud according to the classification label to obtain a grinding surface point cloud model.

[0020] Furthermore, step S4 specifically includes:

[0021] Project the punching and grinding surface point cloud model on a 2D plane and perform triangulation, then apply the triangulated network structure to the 3D point position structure to generate the punching and grinding surface model;

[0022] Selecting a grinding surface model, and calculating the area of ​​the grinding surface of the grinding surface model;

[0023] The horizontal plane where the highest point of the grinding surface model is located is selected as the first closed surface, the surface of the grinding surface model is selected as the second closed surface, and the volume of the closed space between the first closed surface and the second closed surface is calculated as the grinding loss volume.

[0024] Furthermore, after the distance difference is calculated, the distance difference calculation result is displayed in a progressive color scale value.

[0025] Furthermore, a Gaussian distribution model is used to perform statistical analysis on the distance difference, the wear depth of the concrete component is determined according to the mean of the Gaussian distribution, and the wear rate of the wear depth of the concrete component before and after impact and abrasion is determined according to the chi-square distance of the Gaussian distribution.

[0026] The beneficial effects of the present invention are as follows: by adopting the surface reconstruction method to evaluate the impact and wear resistance of concrete, the impact and wear loss of concrete can be visualized; by evaluating the wear depth of different parts of the concrete component before and after grinding with the help of the reconstructed model, the concave and convex conditions of the impact and wear points can be restored, which is helpful for the global evaluation of the impact and wear resistance of concrete. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to represent the same or similar parts. The exemplary embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0028] Figure 1 The flowchart of one embodiment of the present invention is shown in FIG.

[0029] Figure 2 Schematic diagram of feature point matching.

[0030] Figure 3 Generates a milled surface model for surface reconstruction.

[0031] Figure 4 It is the calculation result of the surface area of ​​the abrasive surface and the volume of abrasive loss.

[0032] Figure 5 It is the color scale numerical display of the abrasion loss on the concrete abrasion surface.

[0033] Figure 6 Numerical statistics for evaluating the abrasion resistance of concrete. DETAILED DESCRIPTION

[0034] like Figure 1 The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction includes the following steps:

[0035] S1: The image information of the concrete component to be evaluated is collected by fixed-axis rotation photography;

[0036] S2: Use visual SLAM technology to process image information at the front end and back end, and output the point cloud of concrete components;

[0037] S3: Perform secondary sampling on the point cloud of the concrete component to obtain high-quality point cloud and perform positioning correction processing; then classify and intercept the corrected high-quality point cloud to obtain the punching and grinding surface point cloud model;

[0038] S4: Surface reconstruction is performed on the punching and grinding surface point cloud model based on Delaunay2.5D to obtain the punching and grinding surface model; then the punching and grinding surface area and the punching and grinding loss volume are calculated according to the punching and grinding surface model;

[0039] S5: Calculate the distance difference between the point cloud in the point cloud model of the abrasion surface of the concrete component after abrasion and the abrasion surface of the abrasion surface model of the concrete component before abrasion, and then evaluate the abrasion degree of different positions of the concrete component according to the distance difference.

[0040] The present invention evaluates the anti-abrasion performance of concrete by adopting a surface reconstruction method, which can facilitate the visualization of concrete abrasion loss; by evaluating the wear depth of different parts of the concrete component before and after abrasion with the help of a reconstructed model, the concave and convex conditions of the abrasion points can be restored, which is helpful for the global evaluation of the anti-abrasion performance of concrete; by adopting a fixed-axis rotation photography method to collect image information of the concrete abrasion component to be evaluated and controlling the viewing angle difference range of the image information, the mapping relationship between adjacent frame image information can be ensured to be accurate, the feature point matching time can be reduced, and the point cloud model noise can be reduced; by performing surface reconstruction on the concrete abrasion surface point cloud model based on Delaunay2.5D, the original shape data of the concrete abrasion surface can be accurately represented, and the data visualization display can be realized.

[0041] According to an embodiment of the present application, step S1 specifically includes:

[0042] Place the concrete component to be evaluated on the fixed-axis rotating platform, adjust the fixed-axis rotating platform to keep it horizontal, and then fix the concrete component on the fixed-axis rotating platform so that the center of the concrete component is aligned with the axis of the fixed-axis rotating platform;

[0043] Use a tripod to set up the image acquisition device at a distance of 0.5m from the fixed-axis rotating platform, adjust the vertical angle of the lens of the image acquisition device to -45° and the horizontal angle to 0°. In order to ensure a clear and true image, three fill lights can be arranged around the fixed-axis rotating platform, and the angle of each light is 120°; then control the fixed-axis rotating platform to rotate 10° each time, and take a frame of image each time. After the fixed-axis rotating platform rotates one circle, increase the vertical angle of the lens by 5°, and repeat the above operation a total of 4 times.

[0044] According to one embodiment of the present application, in step S2, the front-end processing specifically includes: using visual SLAM to extract feature points from adjacent frame image information of the concrete component image information, performing feature matching on factors such as the position, orientation, pixel information, and vector space of the extracted feature points in the image information, using different distance measurement norms to evaluate the invariance, robustness, and distinguishability of the feature points, and obtaining a mapping matching relationship.

[0045] According to an embodiment of the present application, in step S2, the back-end processing specifically includes: estimating the position of the image acquisition device, generating environmental primitives according to the matching information of the feature points, and exporting them in the form of point clouds, outputting the concrete component point clouds, such as Figure 2 shown.

[0046] According to one embodiment of the present application, in step S3, secondary sampling of the concrete component point cloud includes: obtaining global features of the concrete component point cloud, generating a dense point cloud based on the global features through iteration of a multi-scale depth map, and using a random sampling method to eliminate noise and invalid data in the concrete component point cloud to obtain a high-quality point cloud.

[0047] According to one embodiment of the present application, in step S3, the positioning correction processing includes: according to the standard size of the punched and ground concrete component with a diameter of 30 cm, the scale is restored to the actual size through point cloud scaling; according to the position of the bubble level on the fixed-axis rotating platform, the horizontal plane is determined by picking up any three points on the table of the fixed-axis rotating platform, so that the normal direction of the punched and ground surface of the high-quality point cloud is perpendicular to the surface of the fixed-axis rotating platform.

[0048] According to an embodiment of the present application, in step S3, classifying and intercepting the corrected high-quality point cloud includes:

[0049] The point cloud of the abrasion surface area and the point cloud of the environment area are used as classification labels as the classification point cloud subset;

[0050] Import the point cloud subset as training data into the classifier, set the scale range of the multi-scale descriptor, set the upper limit of the point cloud classification to 20,000 points, use the original point cloud computing descriptor, and start training the classifier;

[0051] Apply the trained classifier to the point cloud to be classified, adjust the number of classifiers according to the first classification results, and correct the point cloud in the boundary area to make the subsequent classification boundary more accurate;

[0052] The above classifier is applied to high-quality point clouds, and the point cloud of the grinding surface area is intercepted according to the classification label to obtain a point cloud model of the concrete grinding surface.

[0053] According to an embodiment of the present application, step S4 specifically includes:

[0054] Project the punching and grinding surface point cloud model on the 2D plane and perform triangulation. Then apply the triangulated network structure to the 3D point position structure, specify the maximum triangle side length on the triangulated network as 0.001, and generate the punching and grinding surface model. Figure 3 As shown in the figure, by generating a high-quality triangular network with good angle distribution, the overall quality of the reconstructed surface is improved. It has a certain robustness against noise points and outliers, can fill in small area gaps caused by data loss, and is suitable for surface reconstruction with small surface concavity and convexity differences and short distances.

[0055] Selecting a grinding surface model, and calculating the grinding surface area of ​​the grinding surface model;

[0056] Select the horizontal plane where the highest point of the abrasion surface model is located as the first closed surface, and set the height to the 0 level surface; select the surface of the abrasion surface model as the second closed surface, set the side length of the calculation grid unit to 1mm, and calculate the volume of the closed space between the first closed surface and the second closed surface as the abrasion loss volume. The calculation results are as follows: Figure 4 shown.

[0057] The abrasion surface area, abrasion loss volume and distance difference of the above-mentioned abrasion surface model can be calculated using the software CloudCompare; in the process of calculating the distance difference between the point cloud in the abrasion surface point cloud model of the concrete component after abrasion and the abrasion surface of the abrasion surface model of the concrete component before abrasion, the octree calculation of the abrasion surface of the concrete component after abrasion is set to the highest level, and the upper limit of the estimated distance is selected for calculation to adjust the calculation intensity and accuracy, thereby improving the calculation accuracy.

[0058] According to an embodiment of the present application, after the distance difference is calculated, the distance difference calculation result is displayed in a progressive color scale value, such as Figure 5 By using progressive color scale values ​​to display the calculation results, the abrasion loss on the concrete abrasion surface can be intuitively displayed from a three-dimensional perspective.

[0059] According to an embodiment of the present application, a Gaussian distribution model is used to perform statistical analysis on the distance difference, and a representative value of the wear depth of the concrete component is determined based on the mean value of the Gaussian distribution. The wear rate ratio of the wear depth of the concrete component before and after impact and abrasion is determined based on the chi-square distance of the Gaussian distribution, that is, the wear rate. The lower the wear rate and the smaller the wear mean, the stronger the impact and abrasion resistance. Figure 6 shown.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the abrasion resistance of concrete based on surface reconstruction, characterized in that: Includes steps: S1: The image information of the concrete component to be evaluated is collected by fixed-axis rotation photography; S2: Using visual SLAM technology to perform front-end and back-end processing on the image information and output a point cloud of the concrete component; S3: performing secondary sampling on the concrete component point cloud to obtain a high-quality point cloud and perform positioning correction processing; then classifying and intercepting the corrected high-quality point cloud to obtain a punching and grinding surface point cloud model; S4: reconstructing the surface of the grinding surface point cloud model based on Delaunay2.5D to obtain a grinding surface model; and then calculating the grinding surface area and the grinding loss volume according to the grinding surface model; S5: calculating the distance difference between the point cloud in the point cloud model of the abraded surface of the concrete component after abrasion and the abraded surface of the abraded surface model of the concrete component before abrasion, and then evaluating the abrasion degree of different positions of the concrete component according to the distance difference.

2. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 1 is characterized in that: The step S1 specifically includes: Placing the concrete component to be evaluated on a fixed-axis rotating platform so that the center of the concrete component is aligned with the axis of the fixed-axis rotating platform; An image acquisition device is set up near the fixed-axis rotating platform, and the image acquisition device is used to acquire image information of the concrete component as the concrete component rotates.

3. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 1 is characterized in that: In the step S2, the front-end processing specifically includes: extracting feature points from adjacent frame image information of the concrete component image information using visual SLAM, performing feature matching on the extracted feature points, and obtaining a feature point mapping matching relationship.

4. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 3 is characterized in that: In the step S2, the backend processing specifically includes: estimating the position of the image acquisition device, generating environmental primitives according to the matching information of the feature points, and exporting them in the form of point clouds to output the concrete component point clouds.

5. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 1, characterized in that: In the step S3, the secondary sampling of the concrete component point cloud includes: obtaining global features of the concrete component point cloud, generating a dense point cloud by iterating a multi-scale depth map according to the global features, and obtaining a high-quality point cloud by random sampling.

6. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 5 is characterized in that: In step S3, the positioning correction processing includes: according to the size of the standard concrete component, the scale is restored to the actual size by scaling the point cloud; according to the positioning point of the fixed-axis rotating platform, the horizontal plane is determined by picking three points, so that the normal direction of the punching and grinding surface of the high-quality point cloud is perpendicular to the fixed-axis rotating platform surface.

7. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 6, characterized in that: In the step S3, classifying and intercepting the corrected high-quality point cloud includes: using a trained classifier to intercept the point cloud of the grinding surface area from the high-quality point cloud according to the classification label to obtain a grinding surface point cloud model.

8. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 1, characterized in that: The step S4 specifically includes: Projecting the punching and grinding surface point cloud model on a 2D plane and performing triangulation, and then applying the triangulated network structure to the 3D point position structure to generate a punching and grinding surface model; Selecting a grinding surface model, and calculating the area of ​​the grinding surface of the grinding surface model; The horizontal plane where the highest point of the grinding surface model is located is selected as the first closed surface, the surface of the grinding surface model is selected as the second closed surface, and the volume of the closed space between the first closed surface and the second closed surface is calculated as the grinding loss volume.

9. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 1, characterized in that: After the distance difference is calculated, the distance difference calculation result is displayed in a progressive color scale value.

10. The method for evaluating the anti-abrasion performance of concrete based on surface reconstruction according to claim 1, characterized in that: A Gaussian distribution model is used to perform statistical analysis on the distance difference, the wear depth of the concrete component is determined according to the mean of the Gaussian distribution, and the wear rate of the wear depth of the concrete component before and after impact and abrasion is determined according to the chi-square distance of the Gaussian distribution.

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