A method for evaluating uniformity of asphalt mixture based on nearest neighbor analysis

Abstract

This application relates to a method for evaluating the uniformity of asphalt mixtures based on uniform distribution, comprising: S1: collecting asphalt mixture samples to be tested; S2: scanning and sampling the asphalt mixture prepared in step S1 using CT scanning technology; S3: acquiring CT slice images obtained in step S2; S4: processing the CT scan cross-sectional images of the samples in step S3 to obtain binary images of the distribution of coarse aggregate and asphalt mortar on the cross-section; S5: calculating the area and particle coordinates of each coarse aggregate in the two-dimensional cross-section based on the binary image of coarse aggregate obtained in step S4; S6: evaluating the asphalt mixture samples to be tested using the uniformity evaluation index of the two-dimensional / three-dimensional cross-section of the asphalt mixture specimen. The method of this application comprehensively considers the location, area, and quantity information of the aggregates, overcoming the shortcomings of existing methods that fragment the integrity of coarse aggregates; this application evaluates the uniformity of the entire specimen from a three-dimensional perspective, reducing the accumulation of errors.

Description

Technical Field

[0001] The invention belongs to the technical field of asphalt pavement uniformity evaluation, and in particular relates to an asphalt mixture uniformity evaluation method based on nearest neighbor analysis. Background Art

[0002] With the development of road construction and research in my country, according to relevant statistics of my country's highways and practical experience in construction in various regions, the main problem of asphalt pavement now is the gradual destruction of the pavement caused by local damage. This damage is often early damage within one, two or several years after the pavement is built, in the form of cracks, water damage, looseness, potholes and excessive vehicle withdrawal. Early damage to asphalt pavement has always been a major problem in the engineering community, and the unevenness of asphalt mixture is the main cause of early damage to the pavement. The uniformity of asphalt mixture is directly related to the mechanical properties and service life of asphalt pavement. Therefore, studying the uniformity of asphalt mixture has important theoretical significance and engineering application value.

[0003] Because of the unique advantages of industrial CT scanning, most of the current research on the uniformity of the internal structure of asphalt mixtures is based on CT scanning slices. A comprehensive analysis of the existing asphalt mixture uniformity evaluation methods shows the following limitations: 1) There are many CT slices of a specimen. In order to accurately evaluate the uniformity of the specimen, it is necessary to calculate the cross-sectional evaluation index of many slices, which is more complicated to handle; 2) There is a certain error in the calculation of the cross-sectional uniformity index of each image. This calculation method may lead to further accumulation of errors. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, one of the purposes of the present invention is to provide a new method for evaluating the uniformity of asphalt mixture, which can evaluate the uniformity of asphalt mixture from a three-dimensional perspective.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] On the one hand, the present application protects a method for evaluating the uniformity of asphalt mixture based on nearest neighbor analysis, comprising the following steps:

[0007] S1: Collection of asphalt mixture samples to be tested;

[0008] S2: Scanning and sampling the asphalt mixture prepared in step S1 using CT scanning technology;

[0009] S3: Acquisition of CT slice images scanned in step S2;

[0010] S4: Processing the CT scan cross-sectional image of the sample in step S3 to obtain binary images of the distribution of coarse aggregate and asphalt mortar on the cross-sectional area;

[0011] S5: Calculate the area and particle coordinates of each coarse aggregate in the two-dimensional section from the coarse aggregate binary image obtained in step S4;

[0012] S6: Evaluation index U of the uniformity of the two-dimensional cross section of the asphalt mixture specimen d Evaluation of asphalt mixture samples to be tested:

[0013] Among them, the cross-sectional coarse aggregate is quantified regularly, the centroid of the coarse aggregate is taken as an irregular random point set, and the cross-sectional uniformity evaluation model is used to evaluate the cross-sectional uniformity:

[0014]

[0015] U d =∑k j (F j +R j )

[0016] Where: k j —Area ratio of the jth aggregate in the cross section of the asphalt mixture;

[0017] G j —Total area of ​​aggregate particles in the jth level;

[0018] G—total area of ​​each aggregate level in the cross section of asphalt mixture;

[0019] U d —It is the evaluation index of the uniformity of the two-dimensional cross section of the asphalt mixture specimen;

[0020] Among them, U d The larger the value, the worse the cross-sectional uniformity. d The smaller it is, the better the cross-sectional uniformity.

[0021] On the other hand, the present application also protects a method for evaluating the uniformity of an asphalt mixture based on nearest neighbor analysis, comprising the following steps:

[0022] S1: Collection of asphalt mixture samples to be tested;

[0023] S2: Scanning and sampling the asphalt mixture prepared in step S1 using CT scanning technology;

[0024] S3: Acquisition of CT slice images scanned in step S2;

[0025] S4: Processing the CT scan cross-sectional image of the sample in step S3 to obtain binary images of the distribution of coarse aggregate and asphalt mortar on the cross-sectional area;

[0026] S7: Perform three-dimensional modeling on the CT scan cross-sectional image of the specimen obtained in step S4 to obtain a three-dimensional solid model of the specimen;

[0027] S8: Based on the three-dimensional solid model of the specimen obtained in step S7, the volume of each coarse aggregate and the three-dimensional coordinates of its particle in the three-dimensional model of the specimen are calculated respectively;

[0028] S9: The three-dimensional evaluation index U of the overall uniformity of the asphalt mixture specimen z Evaluate the asphalt mixture samples to be tested;

[0029] Among them, after the three-dimensional reconstruction, the coarse aggregate inside the specimen is quantified regularly, the center of gravity of the coarse aggregate is taken as an irregular random point set, and the three-dimensional entity uniformity evaluation model is used to evaluate the uniformity of the asphalt mixture specimen:

[0030]

[0031] U z =∑k j (F j +R j )

[0032] Where: k j —Volume ratio of the jth aggregate in the asphalt mixture;

[0033] G j —Total volume of aggregate particles in the jth grade of the specimen;

[0034] G—total volume of each grade of aggregate in the asphalt mixture specimen;

[0035] U z —Three-dimensional evaluation index of overall uniformity of asphalt mixture specimens.

[0036] Furthermore, in step S9, the following steps are also included:

[0037] S91: Calculate the distance deviation S between the center of gravity of the j-th aggregate and the geometric center of the specimen j :

[0038]

[0039]

[0040]

[0041] R j =S j / r

[0042] Where: —Coordinates of the center of gravity of the j-th aggregate of the specimen;

[0043] —Coordinates of the geometric center of the cross section;

[0044] x ij ,y ij —The coordinate position of the particle center of gravity of the ith aggregate in the jth aggregate of the specimen;

[0045] n—the total number of particles in the j-th aggregate of the specimen;

[0046] S j —Deviation of the distance between the center of gravity of the j-th aggregate and the geometric center of the specimen;

[0047] R j —Deviation rate between the center of gravity of the j-th aggregate and the geometric center of the specimen;

[0048] r—the distance from the geometric center of the specimen to the edge of the specimen;

[0049] S92: Calculate the distance deviation F between the j-th aggregate particles in the specimen j

[0050]

[0051] T ij =d ij f ij

[0052]

[0053] Where: f ij —The volume weight of the i-th aggregate in the j-th aggregate in the specimen;

[0054] V ij —The volume of the ith aggregate in the jth batch of aggregates;

[0055] V j —Total volume of aggregate particles in the jth level;

[0056] T ij —The weighted value of the distance between the center of gravity of the ith aggregate and its nearest neighbor in the jth aggregate;

[0057] d ij —The distance between the center of gravity of the ith aggregate and its nearest neighbor in the jth aggregate;

[0058] F j —Deviation of the distance between the jth aggregate particles in the specimen;

[0059] —The mean of the weighted distances of the center of gravity of the j-th aggregate particles;

[0060] n—the total number of particles in the j-th grade aggregate of the specimen.

[0061] Furthermore, when scanning the specimens, it is necessary to ensure that the CT images of all specimens are obtained under the same CT equipment parameters.

[0062] Furthermore, a binary map of the distribution of coarse aggregate on the cross section is obtained through image trimming, image graying, image enhancement, image segmentation and image post-processing.

[0063] Furthermore, the watershed segmentation method was used to extract the coarse aggregate information of the asphalt mixture, and the hole filling and morphological tools in AVIZO were used to post-process the image.

[0064] Furthermore, the internal structure of asphalt mixture is simplified into two parts: coarse aggregate and asphalt mortar.

[0065] Furthermore, the original slices are first cropped and grayed according to the required image size, and then the image is denoised using an edge-preserving filter to smooth the image while retaining the edges as much as possible; then the grayscale is homogenized and the brightness is balanced to eliminate the defect of low image brightness in the middle and high on both sides caused by the slice itself.

[0066] Compared with the prior art, the present invention has the advantages of:

[0067] 1. The evaluation of the uniformity of asphalt mixture starts from the relationship between adjacent coarse aggregates, comprehensively considering the location information, area information and quantity information of the aggregates, overcoming the shortcomings of previous regional evaluation methods that split the integrity of coarse aggregates.

[0068] 2. In the evaluation of the uniformity of the entire specimen, the previous method calculated the cross-sectional uniformity of all cross sections, and then calculated the standard deviation or mean between these cross-sectional evaluation indicators to complete the uniformity evaluation of a single specimen. However, this evaluation method has certain limitations. First of all, a specimen has many CT slices. In order to accurately evaluate the uniformity of the specimen, it is necessary to calculate the cross-sectional evaluation indicators of many slices, which is more complicated to handle. In addition, there is a certain error in the calculation of the cross-sectional uniformity index of each image, and this calculation method may lead to further accumulation of errors. This method uses a three-dimensional modeling method to reconstruct the specimen, and then evaluates the uniformity of the entire specimen from a three-dimensional perspective, which reduces the accumulation of errors and is more in line with reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of a CT scan of a specimen of an asphalt mixture uniformity evaluation method based on uniform distribution according to an embodiment of the present application;

[0070] Figure 2A CT scan of an asphalt mixture specimen according to an asphalt mixture uniformity evaluation method based on uniform distribution according to an embodiment of the present application;

[0071] Figure 3 A binary image of coarse aggregate distribution after image processing of an asphalt mixture uniformity evaluation method based on uniform distribution according to an embodiment of the present application;

[0072] Figure 4 This is a diagram of a three-dimensional reconstructed model of an asphalt mixture according to an asphalt mixture uniformity evaluation method based on uniform distribution in an embodiment of the present application. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the present invention to perform example operations and clearly and completely describe the technical solution of the present invention. It should be noted that the described examples are only part of the embodiments of the present invention, not all of the embodiments.

[0074] This application starts from the microstructure, takes the core sample of asphalt mixture as the research object, uses X-ray CT technology to non-destructively obtain the structural information inside the specimen and combines digital image technology to identify the internal structure of the asphalt mixture. With the coarse aggregate (>2.36mm) and asphalt mortar area of ​​asphalt mixture as the main characteristic parameters, a two-dimensional cross-section and three-dimensional solid uniformity evaluation method for the internal structure of asphalt mixture is established.

[0075] See also Figure 1-4 In combination with a specific embodiment of the present application, a method for evaluating the uniformity of an asphalt mixture based on uniform distribution is further introduced below, which includes the following steps:

[0076] Step 1: Sampling

[0077] According to the experimental requirements, collect the asphalt mixture sample to be tested.

[0078] Step 2: Image acquisition

[0079] In order to ensure uniform image quality, the same CT equipment and unified scanning parameters were used to scan the specimens to obtain CT scan slice images of the specimens.

[0080] Step 3: Image Processing

[0081] In the study of asphalt mixture uniformity, fine aggregate has basically no segregation problem due to its small particles and large number, while the distribution state of coarse aggregate has an important influence on the uniformity of asphalt mixture, because the distribution state of other components in the mixture is determined by it. Therefore, the internal structure of asphalt mixture can be simply simplified into two parts: coarse aggregate and asphalt mortar. The distribution binary map of coarse aggregate on its cross section is obtained through image trimming, image grayscale, image enhancement, image segmentation and image post-processing.

[0082] Specifically, the original slices are first cropped and grayed according to the required image size, and then the edge-preserving filter is used to reduce the noise of the image, smoothing the image while retaining the edges as much as possible. Then the grayscale is homogenized and the brightness is balanced to eliminate the defect of low brightness in the middle and high brightness on both sides caused by the slice itself. Finally, the watershed segmentation method is used to extract the coarse aggregate information of the asphalt mixture, and the hole filling and morphological tools in AVIZO are used to post-process the image.

[0083] Step 4: Evaluation of the uniformity of two-dimensional cross-section of asphalt mixture based on uniform distribution

[0084] After obtaining the two-dimensional coarse aggregate distribution binary map of the specimen, the coarse aggregate of the cross section is quantified regularly, and the centroid of the coarse aggregate is taken as an irregular random point set. The two-dimensional cross section uniformity evaluation model is used to evaluate the cross section uniformity.

[0085]

[0086] U d =∑k j (F j +R j )

[0087] Where: k j —Area ratio of the jth aggregate in the asphalt mixture cross section;

[0088] G j —Total area of ​​aggregate particles in the jth level;

[0089] G—total area of ​​each aggregate level in the cross section of asphalt mixture;

[0090] U d —It is the evaluation index of the uniformity of the single-layer cross section of the asphalt mixture specimen;

[0091] The cross-sectional uniformity index U is proposed. d As a measure of cross-sectional uniformity, U d The larger the value, the worse the cross-sectional uniformity. d The smaller it is, the better the cross-sectional uniformity.

[0092] Step 5: 3D reconstruction of asphalt mixture specimen

[0093] The CT image of the asphalt mixture specimen obtained in the third step is used to establish a three-dimensional model of the specimen using the volume rendering method. The generated model can retain the rich mesoscopic materials of the asphalt mixture specimen and can extract and analyze three-dimensional volume data based on the setting of unit size.

[0094] Furthermore, the calculation unit of this example model is set to micrometer, but the voxel is 100.0 micrometers*100.0 micrometers*100.0 micrometers.

[0095] Step 6: Asphalt mixture specimen uniformity evaluation

[0096] After 3D reconstruction, the coarse aggregate inside the specimen is quantified and the center of gravity of the coarse aggregate is taken as an irregular random point set. The 3D entity uniformity evaluation model is used to evaluate the uniformity of the asphalt mixture specimen.

[0097]

[0098] U z =∑k j (F j +R j )

[0099] Where: k j —Volume ratio of the jth aggregate in the asphalt mixture;

[0100] G j —Total volume of aggregate particles in the jth grade of the specimen;

[0101] G—total volume of each grade of aggregate in the asphalt mixture specimen;

[0102] U z —Indicators for evaluating the overall uniformity of asphalt mixture specimens;

[0103] The uniformity index U of the specimen is proposed Z As a standard for measuring the overall uniformity of asphalt mixture specimens, U Z The larger the value, the worse the uniformity of the specimen. Z The smaller it is, the better the uniformity of the specimen.

[0104] The following combination Figure 1-4 , introduces a specific embodiment of the present application:

[0105] A method for evaluating the uniformity of an asphalt mixture based on uniform distribution comprises the following steps:

[0106] Step 1: Sample preparation

[0107] This paper adopts the Marshall test design method to design the asphalt mixture ratio according to the gradation range recommended by the "Technical Specifications for Highway Asphalt Pavement Construction" (JTG F40-2004), and produces AC-13 core samples. The following table shows its gradation curve:

[0108] Table 1 Gradation curve of AC-13 asphalt mixture

[0109]

[0110] Step 2: Image acquisition

[0111] The parameters of the CT scanning equipment were unified, and the formed AC-13 specimens were scanned to obtain 400 images with an accuracy of 880*880 pixels.

[0112] Step 3: Image Processing

[0113] Through image cropping, image graying, image enhancement, image segmentation and image post-processing, a binary image of the cross section is obtained. The internal structure of the asphalt mixture can be simplified into: coarse aggregate and asphalt mortar.

[0114] Step 4: Evaluation of the uniformity of two-dimensional cross-section of asphalt mixture based on uniform distribution

[0115] Take the circular plane cross-section after treatment, take the centroid of the coarse aggregate as a random point, and quantify the distribution of the coarse aggregate according to the uniform distribution theory. Taking the cross-section given above as an example, after calculation, the calculation results of the cross-section uniformity are as follows:

[0116]

[0117] Step 5: 3D reconstruction of asphalt mixture specimen

[0118] The asphalt mixture treatment map obtained in the third step is reconstructed in three dimensions using the volume rendering method. The model calculation unit is set to micrometers, and the single pixel is 100.0 micrometers * 100.0 micrometers * 100.0 micrometers. The volume of coarse aggregate is calculated.

[0119] Step 6: Asphalt mixture specimen uniformity evaluation

[0120] Taking the center of gravity of the coarse aggregate as a random point, the distribution of the coarse aggregate is quantified according to the three-dimensional solid uniform distribution theory of the specimen. The calculation results are as follows:

[0121]

[0122] The above embodiments are merely examples for clearly illustrating the present invention, rather than limiting the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description.

Claims

1. A method for evaluating the uniformity of asphalt mixture based on nearest neighbor analysis, characterized in that: The steps include: S1: Collection of asphalt mixture samples to be tested; S2: Scanning and sampling the asphalt mixture prepared in step S1 using CT scanning technology; S3: Acquisition of CT slice images scanned in step S2; S4: Processing the CT scan cross-sectional image of the sample in step S3 to obtain binary images of the distribution of coarse aggregate and asphalt mortar on the cross-sectional area; S5: Calculate the area and particle coordinates of each coarse aggregate in the two-dimensional section from the coarse aggregate binary image obtained in step S4; S6: Evaluation index U of the uniformity of the two-dimensional cross section of the asphalt mixture specimen d Evaluation of asphalt mixture samples to be tested: Among them, the cross-sectional coarse aggregate is quantified regularly, the centroid of the coarse aggregate is taken as an irregular random point set, and the cross-sectional uniformity evaluation model is used to evaluate the cross-sectional uniformity: U d =∑k j (F j +R j ) Where: k j —Area ratio of the jth aggregate in the cross section of the asphalt mixture; G j —Total area of ​​aggregate particles in the jth level; G—total area of ​​each aggregate level in the cross section of asphalt mixture; U d —It is the evaluation index of the uniformity of the two-dimensional cross section of the asphalt mixture specimen; Among them, U d The larger the value, the worse the cross-sectional uniformity. d The smaller it is, the better the cross-sectional uniformity; S7: Perform three-dimensional modeling on the CT scan cross-sectional image of the specimen obtained in step S4 to obtain a three-dimensional solid model of the specimen; S8: Based on the three-dimensional solid model of the specimen obtained in step S7, the volume of each coarse aggregate and the three-dimensional coordinates of its particle in the three-dimensional model of the specimen are calculated respectively; S9: The three-dimensional evaluation index U of the overall uniformity of the asphalt mixture specimen z Evaluate the asphalt mixture samples to be tested; Among them, after the three-dimensional reconstruction, the coarse aggregate inside the specimen is quantified regularly, the center of gravity of the coarse aggregate is taken as an irregular random point set, and the three-dimensional entity uniformity evaluation model is used to evaluate the uniformity of the asphalt mixture specimen: IS z =∑kA j (BYE j +RA j ) In the formula:kA j —Volume ratio of the jth aggregate in the asphalt mixture; VA j —Total volume of aggregate particles in the jth grade of the specimen; VA—total volume of each grade of aggregate in the asphalt mixture specimen; UA z —Three-dimensional evaluation index of overall uniformity of asphalt mixture specimens.

2. The method for evaluating uniformity of asphalt mixture according to claim 1, characterized in that: In step S6, the following steps are also included: S61: Calculate the deviation rate R between the j-th aggregate centroid and the cross-sectional geometric center j : R j =S j / r Where: —Coordinates of the centroid of the j-th aggregate; —Coordinates of the geometric center of the cross section; x ij ,y ij —The particle coordinate position of the ith aggregate in the jth aggregate; n—the total number of particles in the j-th aggregate; S j —Deviation of the distance between the mass center of the j-th aggregate and the geometric center of the cross section; R j —Deviation rate between the centroid of the j-th aggregate and the geometric center of the cross section; r—the distance from the geometric center of the section to the edge of the section; S62: Calculate the distance deviation F between the j-th aggregate particles j : M ij =d ij f ij Where: f ij —The area weight of the ith aggregate in the jth aggregate as a percentage of the entire jth aggregate; G ij —The area of ​​the ith aggregate in the jth aggregate; G j —Total area of ​​aggregate particles in the jth level; M ij —The weighted value of the distance between the center of mass of the ith aggregate and its nearest neighbor in the jth aggregate; d ij —The distance between the centroid of the ith aggregate and its nearest neighbor in the jth aggregate bin; F j —Deviation of distance between aggregate particles in the jth level; —The mean of the weighted distances of the centroids of the j-th aggregate particles; n—the total number of particles in the j-th aggregate.

3. The method for evaluating uniformity of asphalt mixture according to claim 1, characterized in that: In step S9, the following steps are also included: S91: Calculate the distance deviation S between the center of gravity of the j-th aggregate and the geometric center of the specimen j : R j =S j / r Where: —Coordinates of the center of gravity of the j-th aggregate of the specimen; —Coordinates of the geometric center of the cross section; x ij ,y ij —The coordinate position of the particle center of gravity of the ith aggregate in the jth aggregate of the specimen; n—the total number of particles in the j-th aggregate of the specimen; S j —Deviation of the distance between the center of gravity of the j-th aggregate and the geometric center of the specimen; R j —Deviation rate between the center of gravity of the j-th aggregate and the geometric center of the specimen; r—the distance from the geometric center of the specimen to the edge of the specimen; S92: Calculate the distance deviation F between the j-th aggregate particles in the specimen j T ij =d ij f ij Where: f ij —The volume weight of the i-th aggregate in the j-th aggregate in the specimen; V ij —The volume of the ith aggregate in the jth batch of aggregates; V j —Total volume of aggregate particles in the jth level; T ij —The weighted value of the distance between the center of gravity of the ith aggregate and its nearest neighbor in the jth aggregate; d ij —The distance between the center of gravity of the ith aggregate and its nearest neighbor in the jth aggregate; F j —Deviation of the distance between the jth aggregate particles in the specimen; —The mean of the weighted distances of the center of gravity of the j-th aggregate particles; n—the total number of particles in the j-th grade aggregate of the specimen.

4. The method for evaluating uniformity of asphalt mixture according to any one of claims 1 to 3, characterized in that: When scanning the specimens, it is necessary to ensure that the CT images of all specimens are obtained under the same CT equipment parameters.

5. The method for evaluating uniformity of asphalt mixture according to any one of claims 1 to 3, characterized in that: The binary distribution map of coarse aggregate on the cross section is obtained through image trimming, image grayscale, image enhancement, image segmentation and image post-processing.

6. The method for evaluating uniformity of asphalt mixture according to claim 5, characterized in that: The watershed segmentation method was used to extract the coarse aggregate information of asphalt mixture, and the hole filling and morphological tools in AVIZO were used to post-process the image.

7. The method for evaluating uniformity of asphalt mixture according to claim 5, characterized in that: The internal structure of asphalt mixture is simplified into two parts: coarse aggregate and asphalt mortar.

8. The method for evaluating uniformity of asphalt mixture according to claim 5, characterized in that: First, the original slice is cropped and grayscaled according to the required image size, and then the image is denoised using an edge-preserving filter to smooth the image while retaining the edges as much as possible; then the grayscale is homogenized and the brightness is balanced to eliminate the defect of low brightness in the middle and high brightness on both sides caused by the slice itself.

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