Asphalt mixture sampling randomness evaluation method based on three-dimensional aggregate morphological characteristics

Abstract

The invention discloses an asphalt mixture sampling randomness evaluation method based on three-dimensional aggregate morphological characteristics, and relates to the field of road asphalt mixture construction evaluation. The invention aims to solve the problems that the sampling process of the asphalt mixture aggregate is complicated and the sampling randomness is difficult to characterize. The method comprises the following steps: extracting three-dimensional shape characteristic parameters of shape index, sphericity index and surface structure depth of particles in the asphalt mixture through standard test piece preparation and a three-dimensional reconstruction technology; carrying out normality test on the three types of morphological parameters of the same group of particles, and evaluating the sampling randomness grade of the group of samples according to the number of P values; and carrying out weighted averaging on the normality test P values of different groups, carrying out statistics, judging the overall sampling randomness level of the asphalt mixture through the number of indexes, and respectively representing poor sampling randomness, better sampling randomness, good sampling randomness and wonderful sampling randomness by grades 0-3. The sampling randomness characterization method provided by the invention is high in recognition sensitivity and wide in application range, and the accuracy and repeatability of test data are improved.

Description

Technical Field

[0001] The present invention relates to the field of road asphalt mixture construction evaluation, and in particular to an asphalt mixture sampling randomness evaluation method based on three-dimensional aggregate morphology characteristics. Background Art

[0002] In the production and quality control of asphalt mixtures, aggregates, as the backbone material, directly determine the mixture's mechanical properties and road performance. Because mineral aggregates come from a wide variety of sources, have complex particle size compositions, and vary in morphology, they are inevitably subject to numerous uncertainties during mining, crushing, screening, and transportation, ultimately exhibiting a highly random distribution. The "Specifications for Testing Aggregates for Highway Engineering" (JTG E42-2005) and the "Specifications for Testing Asphalt and Asphalt Mixtures for Highway Engineering" (JTG E20-2011) primarily rely on traditional manual methods and two-dimensional sieving particle size analysis for aggregate sampling and testing. While these methods offer some guidance for describing aggregate particle size distribution, they fail to fully capture the impact of three-dimensional aggregate morphological characteristics (such as angularity, flatness, and surface texture) on the structural stability of asphalt mixtures. Furthermore, these standards lack quantitative assessment methods for the randomness of the sampling process, making data biased by human variability, limiting the representativeness of the tests and the reliability of performance predictions.

[0003] The development of modern three-dimensional digital characterization technology has provided a new opportunity to solve this problem. Image processing technology based on X-ray CT, structured light scanning or three-dimensional reconstruction can comprehensively extract the three-dimensional morphological parameters of mineral aggregates and digitally quantify the differences and distribution of their geometric characteristics. This not only breaks through the limitation of traditional methods that can only reflect two-dimensional information, but also enables the volatility and discreteness of morphological characteristics during aggregate sampling to be systematically analyzed. Introducing three-dimensional morphological parameters into the evaluation of sampling randomness is expected to establish a mapping relationship between the spatial distribution of morphology and the representativeness of samples, thereby scientifically revealing the intrinsic relationship between different sampling schemes, sample quantities and aggregate morphology. This has important technical support significance for improving the rationality of asphalt mixture test design and the objectivity of data interpretation. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems of complex asphalt mixture aggregate sampling process and difficulty in characterizing sampling randomness, and to provide an asphalt mixture sampling randomness evaluation method based on three-dimensional aggregate morphology characteristics.

[0005] This method uses standardized specimen preparation and three-dimensional reconstruction techniques to extract three-dimensional morphological characteristic parameters of asphalt mixture particles, including shape index, sphericity index, and surface texture depth. Normality tests are performed on the three types of morphological parameters for the same group of particles, and the sampling randomness level of the group of samples is assessed based on the number of P values. The P values ​​of the normality tests for different groups are weighted and averaged, and the overall sampling randomness level of the asphalt mixture is determined by the number of indicators. Grades 0 to 3 represent poor, relatively good, good, and excellent sampling randomness, respectively. The proposed sampling randomness characterization method has high recognition sensitivity and a wide range of applicability. It can not only identify the degree of bias in the aggregate morphological distribution during the sampling process, but also reflect the changing trend of the overall representativeness of the sample, thereby improving the accuracy and repeatability of test data.

[0006] The present invention provides a randomness evaluation method for asphalt mixture sampling based on three-dimensional aggregate morphology characteristics, comprising the following steps:

[0007] Step 1: Extraction of 3D particle morphology features:

[0008] ① Sampling, screening and specimen forming of aggregate particles are carried out in accordance with the specifications to obtain asphalt mixture specimens;

[0009] ② Use digital technology to perform digital image processing on asphalt mixture specimens to obtain digital images of asphalt mixture;

[0010] ③. Perform 3D reconstruction and virtual screening on the digitized image of the asphalt mixture; measure the Euler angles of the digitized 3D aggregate and, using the Euler rotation matrix, obtain the longest chord L of the 3D digitized aggregate and its corresponding width W and thickness T. Simultaneously, the voxel-integration method can be used to obtain the volume V, surface area A, volume Vc, and surface area Ac of the minimum enclosing convex polygon of the digitized aggregate.

[0011] ④. Calculate the shape index F, sphericity index SI and surface texture depth MTD for all identifiable particles based on the parameters obtained in step ③;

[0012] Step 2: Randomness evaluation of single particle group sampling:

[0013] ① First, group the particles, and then calculate the shape index F, sphericity index SI and

[0014] The values ​​of surface texture depth MTD are plotted in the same coordinate system;

[0015] ②, normality tests were performed on the shape index F, sphericity index SI and surface texture depth MTD respectively;

[0016] ③Compare the obtained normality test P value with 0.05;

[0017] ④. Statistical analysis was performed on the parameters exceeding 0.05, where the number of parameters ranged from 0 to 3, representing poor, good, good, and excellent sampling randomness of the particles in that group, respectively.

[0018] Step 3: Randomness evaluation of overall sampling of asphalt mixture:

[0019] ① The P values ​​calculated for F, SI, and MTD in different groups were weighted averaged according to the volume ratio;

[0020] ② Normality test after weighted average The values ​​were compared with 0.05;

[0021] ③ Normality test after weighted average Parameters with values ​​exceeding 0.05 were counted, and the number of parameters ranged from 0 to 3, representing poor, relatively good, good, and excellent sampling randomness, respectively.

[0022] Beneficial effects of the present invention:

[0023] First, this paper combines digital image processing technology with a method for characterizing irregular particle morphology to propose a method for extracting three-dimensional digital aggregates and constructing morphological parameters. It also assesses the sampling randomness of single particle groupings through a normality test and integrates a volume-weighted average method to establish an evaluation rating system for the sampling process.

[0024] 2. The sampling randomness characterization method proposed in the present invention has high recognition sensitivity and a wide range of applications. It can not only identify the degree of bias in the aggregate morphology distribution during the sampling process, but also reflect the changing trend of the overall representativeness of the sample, thereby improving the accuracy and repeatability of the test data; this method helps to improve the current test procedures and promote the development of asphalt mixture quality control towards refinement and intelligence, and has significant technical necessity and engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Calculation results of aggregate shape index F for each aggregate grouping;

[0026] Figure 2 Calculation results of aggregate sphericity index SI for each aggregate grouping;

[0027] Figure 3 Calculation results of aggregate surface texture depth (MTD) for each aggregate grouping. DETAILED DESCRIPTION

[0028] Specific embodiment 1: This embodiment of the asphalt mixture sampling randomness evaluation method based on three-dimensional aggregate morphology characteristics includes the following steps:

[0029] Step 1: Extraction of 3D particle morphology features:

[0030] ① Sampling, screening and specimen forming of aggregate particles are carried out in accordance with the specifications to obtain asphalt mixture specimens;

[0031] ② Use digital technology to perform digital image processing on asphalt mixture specimens to obtain digital images of asphalt mixture;

[0032] ③. Perform 3D reconstruction and virtual screening on the digitized image of the asphalt mixture; measure the Euler angles of the digitized 3D aggregate and, using the Euler rotation matrix, obtain the longest chord L of the 3D digitized aggregate and its corresponding width W and thickness T. Simultaneously, the voxel-integration method can be used to obtain the volume V, surface area A, volume Vc, and surface area Ac of the minimum enclosing convex polygon of the digitized aggregate.

[0033] ④. Calculate the shape index F, sphericity index SI and surface texture depth MTD for all identifiable particles based on the parameters obtained in step ③;

[0034] Step 2: Randomness evaluation of single particle group sampling:

[0035] ① First, the particles are grouped, and then the shape index F, sphericity index SI and surface texture depth MTD values ​​of all particles in the same group are plotted in the same coordinate system;

[0036] ②, normality tests were performed on the shape index F, sphericity index SI and surface texture depth MTD respectively;

[0037] ③Compare the obtained normality test P value with 0.05;

[0038] ④. Statistical analysis was performed on the parameters exceeding 0.05, where the number of parameters ranged from 0 to 3, representing poor, good, good, and excellent sampling randomness of the particles in that group, respectively.

[0039] Step 3: Randomness evaluation of overall sampling of asphalt mixture:

[0040] ① The P values ​​calculated for F, SI, and MTD in different groups were weighted averaged according to the volume ratio;

[0041] ② Normality test after weighted average The values ​​were compared with 0.05;

[0042] ③ Normality test after weighted average Parameters with values ​​exceeding 0.05 were counted, and the number of parameters ranged from 0 to 3, representing poor, relatively good, good, and excellent sampling randomness, respectively.

[0043] Step 1 (3) of this embodiment is prior art, which is recorded in the paper: Ren, Z., Tan, Y., Huang, L., Yu, H., & Xiao, S. (2023). Irregular characteristic analysis of 3D particles— A novel virtual sieving technique. Powder Technology, 420(October 2022), 118383.

[0044] Specific embodiment 2: This embodiment differs from specific embodiment 1 in that the sampling and screening specifications in step 1① are based on the "Highway Engineering Aggregate Testing Specifications" (JTG E42-2005). The other steps are the same as those in specific embodiment 1.

[0045] Specific embodiment 3: This embodiment differs from either specific embodiment 1 or 2 in that the specimen molding described in step 1 (1) is based on the "Testing Procedure for Asphalt and Asphalt Mixtures in Highway Engineering" (JTG E20-2011); and the digitization technology described in step 1 (2) is computed tomography and 3D reconstruction image processing technology. The remaining steps are the same as those in specific embodiments 1 or 2.

[0046] Specific embodiment 4: This embodiment differs from specific embodiments 1 to 3 in that the Euler rotation matrix in step 1 ③ is as follows:

[0047] ;

[0048] In the formula —Three-dimensional coordinate points Corresponding coordinates after Euler rotation;

[0049] γ, β—Euler angles, which are the spatial angles between the longest chord of the three-dimensional digital aggregate and the X-axis and Y-axis of the three-dimensional rectangular coordinate system, respectively. The other steps are the same as those in the first to third embodiments.

[0050] Specific embodiment 5: This embodiment differs from specific embodiments 1 to 4 in that the calculation formula of the shape index F in step 1④ is as follows:

[0051] ;

[0052] Where T is the thickness corresponding to the longest chord of the three-dimensional digital aggregate, in mm;

[0053] W—the width corresponding to the longest chord of the three-dimensional digitized aggregate, in mm;

[0054] L—the longest chord of the three-dimensional digitized aggregate, in mm. The other steps are the same as those in the first to fourth embodiments.

[0055] Specific embodiment 6: The difference between this embodiment and specific embodiments 1 to 5 is that the calculation formula of the sphericity index SI in step 1④ is as follows:

[0056] ;

[0057] Where A is the surface area of ​​the digital aggregate in mm 2 ;

[0058] V—volume of digital aggregate, in mm 3 The other steps are the same as those in Specific Embodiments 1 to 5.

[0059] Specific embodiment 7: The difference between this embodiment and specific embodiments 1 to 6 is that the calculation formula of the surface texture depth MTD in step 1④ is as follows:

[0060] ;

[0061] Where V c —The volume of the smallest enclosing convex polygon of the digital aggregate, in mm 3 ;

[0062] A c —Surface area of ​​the minimum enclosing convex polygon of the digital aggregate, in mm 2 ;

[0063] V—volume of digital aggregate, in mm 3 The other steps are the same as those in Specific Embodiments 1 to 6.

[0064] Specific embodiment eight: The difference between this embodiment and specific embodiments one to seven is that in step two ①, the particles are grouped according to particle size, including: >32.5mm, 26~32.5mm, 19~26mm, 16~19mm, 13.2~16mm, 9.5~13.2mm, 4.75~9.5mm, 2.36~4.75m.

[0065] The other steps are the same as those in Specific Embodiments 1 to 7.

[0066] Specific embodiment 9: This embodiment differs from specific embodiments 1 to 8 in that the normality test method described in step 2② is the Shapiro-Wilk test, Kolmogorov–Smirnov test, or Anderson-Darling test. The other steps are the same as specific embodiments 1 to 8.

[0067] Specific embodiment 10: This embodiment differs from specific embodiments 1 to 9 in that the calculation process of the weighted average described in step 3① is as follows:

[0068] ;

[0069] In the formula — Normality test after weighted averaging value;

[0070] P1~P i —P value of normality test for each aggregate particle group;

[0071] V1~V i —Total volume of each aggregate particle group, in mm 3 The other steps are the same as those in Specific Embodiments 1 to 9.

[0072] The following examples are used to verify the beneficial effects of the present invention:

[0073] Example 1: A method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics, comprising the following steps:

[0074] Step 1: Extraction of 3D particle morphology features:

[0075] ① Sampling, screening and specimen forming of aggregate particles are carried out in accordance with the specifications to obtain asphalt mixture specimens;

[0076] The aggregate particles described in step 1 (1) have a particle size of 2.36-19 mm and are divided into 5 groups, each with a particle size of 16-19 mm, 13.2-16 mm, 9.5-13.2 mm, 4.75-9.5 mm, and 2.36-4.75 mm. The volume ratio of the aggregate particle groups is 5:11:14:22:14;

[0077] The asphalt mixture described in step 1① is AC-16;

[0078] The sampling and screening specifications in step 1① are in accordance with the "Testing Procedures for Highway Engineering Aggregates" (JTG E42-2005).

[0079] The test specimen molding specification described in step 1① is the "Test Procedure for Asphalt and Asphalt Mixtures in Highway Engineering" (JTGE20-2011);

[0080] ② Use digital technology to perform digital image processing on asphalt mixture specimens to obtain digital images of asphalt mixture;

[0081] The digital technology described in step 1② is industrial CT technology;

[0082] ③. Perform 3D reconstruction and virtual screening on the digitized image of the asphalt mixture; measure the Euler angles of the digitized 3D aggregate and, using the Euler rotation matrix, obtain the longest chord L of the 3D digitized aggregate and its corresponding width W and thickness T. Simultaneously, the voxel-integration method can be used to obtain the volume V, surface area A, volume Vc, and surface area Ac of the minimum enclosing convex polygon of the digitized aggregate.

[0083] Step 1 (3) is a prior art method, as described in the paper: Ren, Z., Tan, Y., Huang, L., Yu, H., & Xiao, S. (2023). Irregular characteristic analysis of 3D particles — A novel virtual sieving technique. Powder Technology, 420(October 2022), 118383.

[0084] ④. Calculate the shape index F, sphericity index SI and surface texture depth MTD for all identifiable particles based on the parameters obtained in step ③. The calculation results are listed in Table 1.

[0085] Step 2: Randomness evaluation of single particle group sampling:

[0086] ① First, the particles are grouped, and then the shape index F, sphericity index SI and surface texture depth MTD values ​​of all particles in the same group are plotted in the same coordinate system;

[0087] ②, normality tests were performed on the shape index F, sphericity index SI and surface texture depth MTD respectively;

[0088] ③Compare the obtained normality test P value with 0.05;

[0089] ④. The parameters exceeding 0.05 were statistically analyzed. The statistical results are listed in Table 1. The number of parameters from 0 to 3 represents poor, good, good, and excellent sampling randomness of the particles in this group, respectively.

[0090] Step 3: Randomness evaluation of overall sampling of asphalt mixture:

[0091] ① The P values ​​calculated for F, SI, and MTD in different groups were weighted averaged according to the volume ratio;

[0092] ②, normality test after weighted average The values ​​were compared with 0.05;

[0093] ③ Normality test after weighted average Parameters with values ​​exceeding 0.05 were counted, and the statistical results are listed in Table 1 The number of parameters ranged from 0 to 3, representing poor, good, good, and excellent sampling randomness, respectively;

[0094] Specifically, the Euler rotation matrix described in step 1 (3) is as follows:

[0095] ;

[0096] In the formula —Three-dimensional coordinate points Corresponding coordinates after Euler rotation;

[0097] γ, β—Euler angles, which are the spatial angles between the longest chord of the 3D digitized aggregate and the X-axis and Y-axis of the 3D rectangular coordinate system, respectively;

[0098] Specifically, the calculation formula of the shape index F in step 1 (4) is as follows:

[0099] ;

[0100] Where T is the thickness corresponding to the longest chord of the three-dimensional digital aggregate, in mm;

[0101] W—the width corresponding to the longest chord of the three-dimensional digitized aggregate, in mm;

[0102] L—the longest chord of the three-dimensional digital aggregate, in mm;

[0103] Specifically, the calculation formula of the sphericity index SI described in step 1④ is as follows:

[0104] ;

[0105] Where A is the surface area of ​​the digital aggregate in mm 2 ;

[0106] V—volume of digital aggregate, in mm 3 .

[0107] Specifically, the calculation formula for the surface texture depth MTD described in step 1 (4) is as follows:

[0108] ;

[0109] Where V c —The volume of the smallest enclosing convex polygon of the digital aggregate, in mm 3 ;

[0110] A c —Surface area of ​​the minimum enclosing convex polygon of the digital aggregate, in mm 2 ;

[0111] V—volume of digital aggregate, in mm 3 ;

[0112] Specifically, the normality test method described in step 2② is: Anderson-Darling test;

[0113] Specifically, the calculation process of the weighted average described in step 3① is as follows:

[0114] ;

[0115] In the formula — Normality test after weighted averaging value;

[0116] P1~P i —P value of normality test for each aggregate particle group;

[0117] V1~V i —Total volume of each aggregate particle group, in mm 3 .

[0118] Table 1

[0119]

Claims

1. Asphalt mixture sampling randomness evaluation method based on three-dimensional aggregate morphology characteristics, characterized by: The following steps are involved: Step 1: Extraction of 3D particle morphology features: ① Sampling, screening and specimen forming of aggregate particles are carried out in accordance with the specifications to obtain asphalt mixture specimens; ② Use digital technology to perform digital image processing on asphalt mixture specimens to obtain digital images of asphalt mixture; ③. Perform 3D reconstruction and virtual screening on the digitized image of the asphalt mixture; measure the Euler angles of the digitized 3D aggregate and, using the Euler rotation matrix, obtain the longest chord L of the 3D digitized aggregate and its corresponding width W and thickness T. Simultaneously, the voxel-integration method can be used to obtain the volume V, surface area A, volume Vc, and surface area Ac of the minimum enclosing convex polygon of the digitized aggregate. ④. Calculate the shape index F, sphericity index SI and surface texture depth MTD for all identifiable particles based on the parameters obtained in step ③; Step 2: Randomness evaluation of single particle group sampling: ① First, group the particles, and then calculate the shape index F, sphericity index SI and surface The values ​​of the surface structural depth MTD are plotted in the same coordinate system; ②, normality tests were performed on the shape index F, sphericity index SI and surface texture depth MTD respectively; ③Compare the obtained normality test P value with 0.05; ④. Statistical analysis was performed on the parameters exceeding 0.05, where the number of parameters ranged from 0 to 3, representing poor, good, good, and excellent sampling randomness of the particles in that group, respectively. Step 3: Randomness evaluation of overall sampling of asphalt mixture: ① The P values ​​calculated for F, SI, and MTD in different groups were weighted averaged according to the volume ratio; ② Normality test after weighted average The values ​​were compared with 0.05; ③ Normality test after weighted average Parameters with values ​​exceeding 0.05 were counted, and the number of parameters ranged from 0 to 3, representing poor, relatively good, good, and excellent sampling randomness, respectively.

2. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1 is characterized in that: The sampling and screening specifications described in step 1① are based on the "Test Procedure for Aggregates in Highway Engineering" (JTG E42-2005).

3. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1 is characterized in that: The specification for the specimen molding described in step 1① is the "Test Procedure for Asphalt and Asphalt Mixtures in Highway Engineering" (JTG E20-2011); the digitalization technology described in step 1② is computed tomography technology and three-dimensional reconstruction image processing technology.

4. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1, characterized in that: The Euler rotation matrix described in step 1 (3) is as follows: ； In the formula —Three-dimensional coordinate points Corresponding coordinates after Euler rotation; γ, β—Euler angles, which are the spatial angles between the longest chord of the three-dimensional digitized aggregate and the X-axis and Y-axis of the three-dimensional rectangular coordinate system, respectively.

5. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1, characterized in that: The calculation formula of the shape index F described in step 1④ is as follows: ； Where T is the thickness corresponding to the longest chord of the three-dimensional digital aggregate, in mm; W—the width corresponding to the longest chord of the three-dimensional digitized aggregate, in mm; L—The longest chord of the three-dimensional digitized aggregate, in mm.

6. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1, characterized in that: The calculation formula of the sphericity index SI described in step 1④ is as follows: ； Where A is the surface area of ​​the digital aggregate in mm 2 ; V—volume of digital aggregate, in mm 3 .

7. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1, characterized in that: The calculation formula of the surface texture depth MTD described in step 1④ is as follows: ； Where V c —The volume of the smallest enclosing convex polygon of the digital aggregate, in mm 3 ; A c —Surface area of ​​the minimum enclosing convex polygon of the digital aggregate, in mm 2 ; V—volume of digital aggregate, in mm 3 .

8. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1, characterized in that: In step 2①, the particles are grouped according to particle size, including: >32.5mm, 26~32.5mm, 19~26mm, 16~19mm, 13.2~16mm, 9.5~13.2mm, 4.75~9.5mm, and 2.36~4.75mm.

9. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1, characterized in that: The normality test method described in step 2② is: Shapiro-Wilk test, Kolmogorov–Smirnov test or Anderson-Darling test.

10. The method for evaluating randomness of asphalt mixture sampling based on three-dimensional aggregate morphology characteristics according to claim 1, characterized in that: The calculation process of the weighted average described in step 3① is as follows: ； In the formula — Normality test after weighted averaging value; P1~P i —P value of normality test for each aggregate particle group; V1~V i —Total volume of each aggregate particle group, in mm 3 .

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