An asphalt mixture uniformity evaluation method based on CT scanning technology

Abstract

The application discloses a kind of based on CT scanning technology's asphalt mixture uniformity evaluation method, specific steps are as follows: S1, obtains asphalt mixture cylinder test piece, it is scanned using CT scanning equipment;S2, constructs the three-dimensional visualization model of test piece using slice image;S3, image processing is carried out to three-dimensional visualization model, and component identification is carried out;S4, with the geometric center of cylinder test piece as origin, height direction is Z axis and establishes XYZ coordinate system;S5, cylinder test piece is divided into N equal cubic grid, and minimum unit size is defined;S6, according to the equivalent diameter of aggregate, aggregate in asphalt mixture is divided into multiple aggregate;S7, calculates uniformity index K.The application is based on CT and three-dimensional image division, and the uniformity of entire recycled asphalt mixture test piece is evaluated, and the shortcomings that its integrity and large calculation error of mixed material are overcome.

Description

Technical Field

[0001] This invention belongs to the field of asphalt mixture technology, specifically relating to a method for evaluating the uniformity of asphalt mixtures based on CT scanning technology. Background Technology

[0002] Most current research on the uniformity of the internal structure of asphalt mixtures is based on CT scan slices. Internal structural images are obtained through CT scanning or cutting and photographing, and then a single slice is divided into fan-shaped, square, or other areas. The coefficient of variation of aggregate area between different areas is used as an indicator to quantitatively evaluate the uniformity of the asphalt mixture. This method has the following limitations: (1) There are numerous CT slices of the specimen, requiring the calculation of cross-sectional evaluation indicators for many slices to evaluate the uniformity, which is a complex process; (2) The calculation of the uniformity index for each cross-section has a certain error, ultimately resulting in a large cumulative error. In recycled asphalt mixtures, the clumping phenomenon caused by the uneven distribution of new and old asphalt binders is the main factor contributing to the uneven distribution of new and old materials. Therefore, it is necessary to improve the traditional method for evaluating the uniformity of asphalt mixtures. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method for evaluating the uniformity of asphalt mixtures based on CT scanning technology. This method evaluates the uniformity of the entire recycled asphalt mixture specimen based on CT and three-dimensional image segmentation, overcoming the drawbacks of fragmented mixture integrity and large calculation errors.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for evaluating the uniformity of asphalt mixtures based on CT scanning technology, the specific steps of which are as follows:

[0006] S1. Obtain cylindrical specimens of asphalt mixture and scan them using CT scanning equipment to obtain slice images of the specimens;

[0007] S2. Construct a three-dimensional visualization model of the specimen using the sliced ​​images obtained in step S1;

[0008] S3. Perform image processing on the 3D visualization model, identify components, remove asphalt mortar and voids, and retain aggregate particles;

[0009] S4. Establish an XYZ coordinate system with the geometric center of the cylindrical specimen as the origin and the height direction as the Z-axis;

[0010] S5. Divide the cylindrical specimen into N equal cubic grids. To ensure that each grid can accommodate at least one maximum aggregate and two minimum aggregates, define the minimum unit size and calculate it as shown in formula (1):

[0011] (1);

[0012] in is the minimum unit size; is the maximum aggregate particle size in the specimen; The smallest aggregate particle size in the specimen; , , R is the number of grid cells in each dimension, rounded down; H is the specimen radius; H is the specimen height; N is the total number of grid cells. For boundary grid cells (which are determined by the ratio of aggregate to grid cells), only the portion of the grid cell where the specimen volume coverage exceeds 50% is counted.

[0013] S6. Based on the equivalent diameter of the aggregates, the aggregates in the asphalt mixture are divided into multiple aggregate grades.

[0014] S7. For the i-th aggregate, calculate the volume percentage of that aggregate in each grid, count the volume percentage in all grids, calculate the uniformity coefficient, and finally weight and summarize the uniformity index K of each aggregate. The calculation formula is as shown in equation (2):

[0015] (2);

[0016] in, is the uniformity coefficient for each aggregate grade; N is the total number of grid cells; For the b-th grid, the first... The volume percentage of aggregates; For the first in all grids The volume percentage of aggregates; It is a very small constant (e.g., 1e-6, to avoid the denominator being zero); K represents the ratio of the volume of aggregate in grade i to the total volume of aggregate; the smaller the K value, the better the uniformity of the asphalt mixture; conversely, the larger the K value, the worse the uniformity of the asphalt mixture.

[0017] Preferably, in formula (2):

[0018]

[0019] Let be the volume of the i-th aggregate in the b-th grid; The volume of a single voxel is determined by the CT resolution; The number of voxels in the i-th aggregate in the b-th grid is given by exporting the position coordinates of all aggregates using AVIZO software and counting the number of voxels in each grid using MATLAB software.

[0020] Preferably, in step S1, the cylindrical specimen has a radius ≥ 50 mm and a height ≥ 50 mm; the CT tomographic scanning direction is the height direction of the specimen, and the spacing is ≤ 1.5 mm.

[0021] Preferably, in step S2 above, the three-dimensional visualization model of the specimen is constructed using AVIZO software.

[0022] Preferably, the specific steps for image processing of the 3D visualization model in step S3 are as follows:

[0023] (1) Using AVIZO software to read and convert CT scan images of asphalt mixtures;

[0024] (2) Image enhancement is achieved by using grayscale conversion to adjust the contrast and brightness of grayscale images and by using median filtering to remove particulate noise in digital images of asphalt mixtures.

[0025] (3) Use interactive threshold segmentation and slice editing to separate and check the aggregates in 5 to 10 consecutive tomographic images, and use them as materials to train the model for deep learning;

[0026] (4) Use the trained deep learning model to pre-identify the components of the remaining tomographic images to enhance the image contrast between components;

[0027] (5) Based on the bimodal method, determine the gray value boundaries of the components, and use the gray value threshold to separate the binary images of components such as aggregates, asphalt mortar and voids from the image;

[0028] (6) The adhering particles are broken by morphological opening operation.

[0029] Preferably, in step S6 above, the equivalent diameter of the aggregate is the diameter of a sphere with the same volume as the aggregate.

[0030] Preferably, in the aforementioned step S6, the aggregates in the asphalt mixture are divided into four grades based on their equivalent diameter: 2.36mm~4.75mm, 4.75~9.5mm, 9.5~13.2mm, and above 13.2mm.

[0031] The advantages of this invention are as follows: Based on CT and three-dimensional image segmentation, this invention systematically evaluates the uniformity of the entire recycled asphalt mixture specimen. Through three-dimensional global analysis, dynamic mesh generation, and graded weighted evaluation mechanism, it systematically solves the core problems of traditional two-dimensional evaluation methods, such as dimensional limitations, fragmentation integrity, and error accumulation. While ensuring the accuracy of calculation results, it improves calculation efficiency. Its technical advantages have been fully verified in the examples of virgin materials (SMA-13, AC-13) and recycled materials (conventional RAP, refined separated RAP). Attached Figure Description

[0032] Figure 1 This is the gradation curve of the asphalt mixture in Example 1;

[0033] Figure 2 These are the image processing steps for the 3D visualization model in Example 1;

[0034] Figure 3 This is a schematic diagram of the particle size distribution in Example 1;

[0035] Figure 4 These are the K values ​​of the three asphalt mixtures in Example 1;

[0036] Figure 5 This is the gradation curve of the asphalt mixture in Example 2;

[0037] Figure 6 It is the K value of the three asphalt mixtures in Example 2. Detailed Implementation

[0038] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0039] Example 1: A method for evaluating the uniformity of asphalt mixtures based on CT scanning technology, the specific steps of which are as follows:

[0040] (1) Three types of Marshall specimens were prepared using indoor molding: virgin SMA-13, conventionally crushed and screened RAP recycled SMA-13 ​​mixture, and finely separated RAP recycled mixture. The specimen dimensions were Φ101.6 mm × 63.5 mm. The gradation curves of the asphalt mixtures are shown below. Figure 1 As shown. A CT scanner was used to scan the specimen at intervals of 0.1 mm, obtaining a total of 635 slice images along the height direction.

[0041] (2) Using the slice images obtained in step A, construct a three-dimensional visualization model of the specimen using AVIZO software; perform image processing on the three-dimensional visualization model, identify components, remove asphalt mortar and voids, and retain aggregate particles; the operation steps for image processing of the three-dimensional visualization model are as follows. Figure 2 ;

[0042] (3) Use AVIZO software to read and convert the CT scan image of asphalt mixture; then use grayscale transformation to adjust the contrast and brightness of the grayscale image and use median filtering to remove particulate noise in the digital image of asphalt mixture to achieve image enhancement.

[0043] (4) Use interactive threshold segmentation and slice editing to separate and check the aggregates in 5 to 10 consecutive tomographic images, and use them as materials to train the model for deep learning; use the trained deep learning model to pre-identify the components of the remaining tomographic images to enhance the image contrast between components.

[0044] (5) Based on the bimodal method, the gray value boundaries of the components are determined, and the binary images of components such as aggregates, asphalt mortar and voids are separated from the image using gray value thresholds; then, the cohesive particles are broken up by morphological opening operations such as opening operation pre-segmentation and watershed segmentation.

[0045] (6) Establish an XYZ coordinate system with the geometric center of the cylindrical specimen as the origin and the height direction as the Z-axis; divide the cylindrical specimen into N equal cubic grids. To ensure that a single grid can accommodate at least one maximum aggregate and two minimum aggregates, define the minimum unit size and calculate it as shown in equation (1):

[0046] (1);

[0047] in Minimum unit size; The maximum aggregate particle size in the specimen; The smallest aggregate particle size in the specimen;

[0048] , , R is the number of grid cells in each dimension, rounded down; H is the specimen radius; H is the specimen height; N is the total number of grid cells. For boundary grid cells, only the portion of the grid cell where the specimen volume coverage exceeds 50% is counted.

[0049] (7) Based on the equivalent diameter of the aggregates, the aggregates in the asphalt mixture are divided into four grades: 2.36 mm~4.75 mm, 4.75~9.5 mm, 9.5~13.2 mm, and above 13.2 mm. The schematic diagram of each grade is shown below. Figure 3 As shown;

[0050] (8) For the i-th aggregate, calculate the volume percentage of that aggregate in each grid, count the volume percentage in all grids, calculate the uniformity coefficient, and finally weight and summarize the uniformity index K of each aggregate. The calculation formula is as shown in equation (2):

[0051] (2);

[0052] Where is the uniformity coefficient of each aggregate grade; N is the total number of grids; and is the uniformity coefficient of the b-th grid. The volume percentage of aggregates; Let be the volume percentage of the i-th aggregate in all grids; It is a very small constant (e.g., 1e-6, to avoid the denominator being zero); K represents the ratio of the volume of aggregate in grade i to the total volume of aggregate; the smaller the K value, the better the uniformity of the asphalt mixture; conversely, the larger the K value, the worse the uniformity of the asphalt mixture.

[0053] See Figure 4 The K-values ​​of different types of SMA-13 ​​asphalt mixtures ranged from 0.06 to 0.13. The K-values ​​of both refined separated recycled mixtures and conventionally crushed and screened recycled mixtures were higher than those of virgin asphalt, indicating that the incorporation of RAP reduced the homogeneity of the recycled mixtures. The K-value of refined separated recycled mixtures was 26.1% lower than that of conventionally crushed and screened recycled mixtures, indicating better homogeneity. This is because the refined separation technology reduces the agglomeration of RAP, facilitating the shedding of RAP surface particles during mixing, resulting in better dispersion and uniformity in the recycled mixture.

[0054] Example 2 follows the same steps as Example 1, except that the Marshall specimens are virgin AC-13, conventionally crushed and screened RAP recycled AC-13 mixture, and finely separated RAP recycled mixture. The asphalt mixture gradation curve is shown below. Figure 5 As shown, the K values ​​for the three asphalt mixtures are as follows: Figure 6 As shown.

[0055] See Figure 6 The virgin material had the lowest K value, indicating the best dispersion uniformity. The K value of the finely separated recycled mixture decreased by 30.4% compared to the conventionally crushed and screened recycled mixture, which is consistent with the trend shown in Example 1.

[0056] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any way, and all technical solutions obtained by equivalent substitution or equivalent transformation fall within the protection scope of the present invention.

Claims

1. A method for evaluating the uniformity of asphalt mixtures based on CT scanning technology, characterized in that, The specific steps are as follows: S1. Obtain cylindrical specimens of asphalt mixture and scan them using CT scanning equipment to obtain slice images of the specimens; S2. Construct a three-dimensional visualization model of the specimen using the sliced ​​images obtained in step S1; S3. Perform image processing on the 3D visualization model, identify components, remove asphalt mortar and voids, and retain aggregate particles; S4. Establish an XYZ coordinate system with the geometric center of the cylindrical specimen as the origin and the height direction as the Z-axis; S5. Divide the cylindrical specimen into N equal cubic grids. To ensure that each grid can accommodate at least one maximum aggregate and two minimum aggregates, define the minimum unit size and calculate it as shown in formula (1): ； ； ； （1）； in Minimum unit size; The maximum aggregate particle size in the specimen; The smallest aggregate particle size in the specimen; , , R is the number of grid cells in each dimension, rounded down; H is the specimen radius; H is the specimen height; N is the total number of grid cells. For boundary grid cells, only the portion of the grid cell where the specimen volume coverage exceeds 50% is counted. S6. Based on the equivalent diameter of the aggregates, the aggregates in the asphalt mixture are divided into multiple aggregate grades. S7. For the i-th aggregate, calculate the volume percentage of that aggregate in each grid, count the volume percentage in all grids, calculate the uniformity coefficient, and finally weight and summarize the uniformity index K of each aggregate. The calculation formula is as shown in equation (2): ； （2）； in, P is the uniformity coefficient of each aggregate grade; N is the total number of grids; P is the uniformity coefficient of each aggregate grade. ib For the b-th grid, the first... The volume percentage of aggregates; Let i be the volume percentage of the i-th aggregate in all grids; W is a minimal constant. i K represents the ratio of the volume of aggregate in grade i to the total volume of aggregate; the smaller the K value, the better the uniformity of the asphalt mixture; conversely, the larger the K value, the worse the uniformity of the asphalt mixture.

2. The method for evaluating the uniformity of asphalt mixtures based on CT scanning technology according to claim 1, characterized in that, In step S7, ； ; Among them, V ib Let be the volume of the i-th aggregate in the b-th grid; The volume of a single voxel is determined by the CT resolution; The number of voxels in the i-th aggregate in the b-th grid is given by exporting the position coordinates of all aggregates using AVIZO software and counting the number of voxels in each grid using MATLAB software.

3. The method for evaluating the uniformity of asphalt mixtures based on CT scanning technology according to claim 1, characterized in that, In step S1, the cylindrical specimen has a radius ≥ 50 mm and a height ≥ 50 mm; the CT tomographic scanning direction is the height direction of the specimen, and the spacing is ≤ 1.5 mm.

4. The method for evaluating the uniformity of asphalt mixtures based on CT scanning technology according to claim 1, characterized in that, In step S2, the three-dimensional visualization model of the specimen is constructed using AVIZO software.

5. The method for evaluating the uniformity of asphalt mixtures based on CT scanning technology according to claim 1, characterized in that, In step S3, the specific steps for image processing of the 3D visualization model are as follows: (1) Using AVIZO software to read and convert CT scan images of asphalt mixtures; (2) Image enhancement is achieved by using grayscale conversion to adjust the contrast and brightness of grayscale images and by using median filtering to remove particulate noise in digital images of asphalt mixtures. (3) Use interactive threshold segmentation and slice editing to separate and check the aggregates in 5 to 10 consecutive tomographic images, and use them as materials to train the model for deep learning; (4) Use the trained deep learning model to pre-identify the components of the remaining tomographic images to enhance the image contrast between components; (5) Based on the bimodal method, determine the gray value boundaries of the components, and use the gray value threshold to separate the binary images of components such as aggregates, asphalt mortar and voids from the image; (6) The adhering particles are broken by morphological opening operation.

6. The method for evaluating the uniformity of asphalt mixtures based on CT scanning technology according to claim 1, characterized in that, In step S6, the equivalent diameter of the aggregate is the diameter of a sphere with the same volume as the aggregate.

7. The method for evaluating the uniformity of asphalt mixtures based on CT scanning technology according to claim 1, characterized in that, In step S6, the aggregates in the asphalt mixture are divided into four grades based on their equivalent diameter: 2.36mm~4.75mm, 4.75~9.5mm, 9.5~13.2mm, and above 13.2mm.

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