An asphalt pavement road surface texture modeling method based on a three-dimensional discrete element model

Abstract

The application relates to a kind of asphalt pavement road surface texture modeling methods based on three-dimensional discrete element model, comprising: step S1: selecting representative coarse aggregate from the aggregate constituting asphalt pavement wearing course, obtaining the surface topography as the first real aggregate profile model after scanning each selected coarse aggregate;Step S2: based on the first real aggregate profile model, the three-dimensional discrete element model of asphalt mixture is constructed as the asphalt pavement model, the micro-mechanical properties between the aggregate particles in the asphalt pavement model are given, and the texture parameters and the inter-particle porosity of the asphalt pavement model are calculated;Step S3: the surface texture parameters and the inter-particle porosity of the real asphalt pavement with the same aggregate and gradation are calculated, and compared with the texture parameters and the inter-particle porosity of the asphalt pavement model, if the error is too large, then return to step S2 to adjust the micro-mechanical properties between the aggregate particles in the asphalt pavement model.Compared with the prior art, the application can accurately simulate pavement texture and establish the corresponding relationship between wearing course texture and skeleton.

Description

Technical Field

[0001] This invention relates to the field of asphalt pavement simulation modeling technology, and in particular to a method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model. Background Technology

[0002] As road service life increases, pavement skid resistance gradually declines. The application of wearing course technology significantly improves pavement skid resistance and is therefore widely adopted. The surface texture and structural features of the wearing course significantly affect pavement skid resistance; therefore, further research on the structural composition and texture characteristics of the wearing course is necessary. Currently, three-dimensional pavement textures are mainly obtained using high-resolution laser scanners and digital image processing technology. Although the texture of the wearing course has been extensively studied, the correlation between material composition and pavement texture from a mechanical perspective remains lacking. This gap hinders a comprehensive understanding of the mechanical relationship between the asphalt mixture skeleton and pavement texture. Furthermore, current simulation methods oversimplify the mechanical behavior of the skeleton by treating aggregates as spherical particles, failing to accurately explain their true contour characteristics. Summary of the Invention

[0003] The purpose of this invention is to provide a method for modeling the surface texture of asphalt pavement based on a three-dimensional discrete element model.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] A method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model includes:

[0006] Step S1: Select representative coarse aggregates from the aggregates that make up the asphalt pavement wearing course, and scan each selected coarse aggregate to obtain its surface morphology as the first real aggregate profile model.

[0007] Step S2: Construct a three-dimensional discrete element model of asphalt mixture based on the first real aggregate profile model as an asphalt pavement model, endow the asphalt pavement model with micro-mechanical properties between aggregate particles, and calculate the texture parameters and aggregate void ratio of the asphalt pavement model.

[0008] Step S3: Calculate the surface texture parameters and aggregate void ratio of a real asphalt pavement with the same aggregate and gradation, and compare them with the texture parameters and aggregate void ratio of the asphalt pavement model. If the error is too large, return to step S2 to adjust the micromechanical properties between aggregate particles in the asphalt pavement model.

[0009] The representative coarse aggregate is coarse aggregate with a particle size larger than the first preset particle size, and the angularity of all representative coarse aggregates is different from each other.

[0010] Step S2 includes:

[0011] Step S2-1: Based on the gradation data of the asphalt pavement wearing course, randomly generate different balls to obtain the asphalt pavement model, where one ball represents one aggregate particle;

[0012] Step S2-2: Replace each ball with a diameter larger than the second preset particle size with one of the first real aggregate profile models;

[0013] Step S2-3: Compact the obtained asphalt pavement model;

[0014] Step S2-4: Impart micromechanical properties between aggregate particles in the mixture;

[0015] Step S2-5: Extract the surface texture coordinates of the asphalt pavement model and calculate the texture parameters and aggregate void ratio of the asphalt pavement model.

[0016] In step S2-2, the replacement order is as follows: replacement is carried out step by step from high to low according to particle size classification.

[0017] The compaction process in steps S2-3 consists of 8 rolling compactions, with a compaction force of 120kN and a steel wheel radius of 550mm.

[0018] The surface texture parameters include average build depth (EMTD) and kurtosis (R). ku and root mean square wavelength λ q .

[0019] The mathematical expression for the average structural depth EMTD is:

[0020]

[0021] Where: l x l is the length of the sampling region. y z is the width of the sampling region. p (x,y) represents the peak value at point (x,y), and z(x,y) represents the texture contour value at point (x,y).

[0022] The kurtosis R ku The mathematical expression is:

[0023]

[0024] Where: R q The root mean square roughness;

[0025] The root mean square wavelength λ q The mathematical expression is:

[0026]

[0027] Where: Δq is the root mean square slope, and π is pi;

[0028] The mineral aggregate interstitial ratio is:

[0029]

[0030] Where: VMA is the void fraction of the mineral aggregate, V agg V represents the total volume of the aggregate. tot This represents the total volume of the mixture.

[0031] A device for modeling asphalt pavement surface texture based on a three-dimensional discrete element model includes a memory, a processor, and a program stored in the memory. When the processor executes the program, it implements the method described above.

[0032] A storage medium having a program stored thereon, which, when executed, implements the method described above.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. It can restore the true shape and size of aggregates in asphalt mixtures, as well as the void structure, and characterize their distribution characteristics, accurately simulate road surface texture, and establish the correspondence between the texture of the wear layer and the skeleton.

[0035] 2. Provides a reference for the design of wear layers.

[0036] 3. Real road surface photos can be taken visually, and the anti-skid performance of the road surface can be obtained through computer simulation technology. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the coarse aggregate reconstruction results;

[0038] Figure 2 This is a schematic diagram of the main steps of the method of the present invention;

[0039] Figure 3 This is a texture diagram of the asphalt pavement model for SMA-13.

[0040] Figure 4 Models of asphalt pavements with different gradations are provided, where: (a) is AC-13, (b) is SMA-13, and (c) is OGFC-13;

[0041] Figure 5 The results are the average construction depth EMTD results in the examples;

[0042] Figure 6 For the kurtosis R in the example kuThe result;

[0043] Figure 7 In the example, the root mean square wavelength λ q The result. Detailed Implementation

[0044] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0045] A method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model, such as... Figure 2 As shown, it includes:

[0046] Step S1: Select representative coarse aggregates from the aggregates that make up the asphalt pavement wearing course, and scan each selected coarse aggregate to obtain its surface morphology as the first real aggregate profile model.

[0047] The representative coarse aggregate is the coarse aggregate with a particle size larger than the first preset particle size, and the angularity of all the representative coarse aggregates is different.

[0048] The first preset particle size can be selected according to the actual situation. In this embodiment, ten coarse aggregates with different angularity were specifically selected for scanning. Specifically, a portable 3D laser scanner was used to obtain their surface morphology as a reference for aggregate modeling. The 3D portable laser scanner used was FreeScan UE Pro, with a scanning accuracy of 0.08mm.

[0049] Step S2: Construct a three-dimensional discrete element model of the asphalt mixture based on the first real aggregate profile model as the asphalt pavement model. Assign microscopic mechanical properties to the aggregate particles in the asphalt pavement model, and calculate the texture parameters and aggregate void ratio of the asphalt pavement model. Specifically, this includes:

[0050] Step S2-1: Based on the gradation data of the asphalt pavement wear course, randomly generate different balls to obtain the asphalt pavement model, where one ball represents one aggregate particle;

[0051] In this embodiment, the asphalt pavement model is specifically built based on the discrete element software—LIGGGHTS software.

[0052] Step S2-2: Replace each ball with a diameter larger than the second preset particle size with one of the first real aggregate profile models;

[0053] The replacement order is as follows: replacement is carried out step by step from high to low particle size.

[0054] In this embodiment, the second preset particle size is 2.36 mm.

[0055] Step S2-3: Compact the obtained asphalt pavement model. The compaction process involves 8 rolling compactions with a compaction force of 120kN and a steel wheel radius of 550mm.

[0056] Step S2-4: Impart micromechanical properties between aggregate particles in the mixture;

[0057] Step S2-5: Extract the surface texture coordinates of the asphalt pavement model and calculate the texture parameters and aggregate void ratio of the asphalt pavement model.

[0058] In this embodiment, the texture parameters of the asphalt pavement model are calculated using Matlab software.

[0059] Step S3: Calculate the surface texture parameters and aggregate void ratio of a real asphalt pavement with the same aggregate and gradation, and compare them with the texture parameters and aggregate void ratio of the asphalt pavement model. If the error is too large, return to step S2 to adjust the micromechanical properties between aggregate particles in the asphalt pavement model.

[0060] Among them, the surface texture parameters include the average fabrication depth EMTD and the kurtosis R. ku and root mean square wavelength λ q .

[0061] The mathematical expression for the mean structural depth EMTD is:

[0062]

[0063] Where: l x l is the length of the sampling region. y z is the width of the sampling region. p (x,y) represents the peak value at point (x,y), and z(x,y) represents the texture contour value at point (x,y).

[0064] Kubivity R ku The mathematical expression is:

[0065]

[0066] Where: R q The root mean square roughness;

[0067] Root mean square wavelength λ q The mathematical expression is:

[0068]

[0069] Where: Δq is the root mean square slope, and π is pi;

[0070] The aggregate gap ratio is:

[0071]

[0072] Where: VMA is the void fraction of the mineral aggregate, V agg V represents the total volume of the aggregate. tot This represents the total volume of the mixture.

[0073] Specifically, in step S3, if the surface texture parameters of the asphalt pavement model differ significantly from those of the actual pavement, or if the VMA of the asphalt pavement model differs significantly from that of the actual pavement, then return to step S2, adjust the micromechanical properties between the aggregate particles in the mixture, and repeat steps S2 and S3 until the surface texture parameters of the asphalt pavement model and the surface texture parameters of the pavement are close, and the VMA of the asphalt pavement model and the VMA of the actual pavement are close. Then the model is considered valid.

[0074] In a real-world case study, using SMA-13 ​​as an example, a method for constructing three-dimensional pavement surface texture based on a discrete element model is presented. The reconstruction result of coarse aggregate is as follows: Figure 1 As shown.

[0075] The micromechanical properties imparted to the aggregate particles in the mixture are shown in Table 1.

[0076] Table 1

[0077]

[0078] Then, a portable 3D laser scanner was used to scan the actual surface texture of the SMA-13 ​​road surface to acquire point cloud data. Before scanning the road surface, a calibration board was used to calibrate the equipment and set reflection markers. During the scanning process, the scanner emitted 26 intersecting laser beams towards the selected area surrounded by the markers and captured the reflected laser beams, generating a 3D digital road surface in the selected area. The generated point cloud data was saved in .asc file format, with a scanning accuracy of 0.08mm.

[0079] The texture of the asphalt pavement model is measured using a texture extraction program, such as... Figure 3 As shown, relevant texture parameters were calculated in Matlab, and three parameters were selected for comparative analysis, including average construction depth (EMTD), kurtosis (R), and so on. ku and root mean square wavelength λ q ;

[0080] The calculated average texture depth (EMTD) of the asphalt pavement model is 1.31, while the average texture depth (EMTD) of the actual pavement is 1.38. Since the two are close, the kurtosis (R) of the asphalt pavement model is calculated. ku The kurtosis R of the actual road surface is 3.88.ku The value is 3.51, which is close to the value. The root mean square wavelength λ of the asphalt pavement model is calculated. q The root mean square wavelength λ of the actual road surface is 12.22. q The value is 12.40, which is close to the value.

[0081] By comparing the three texture parameters above, the texture of the established asphalt pavement model is similar to that of the real pavement, and the similarity of the texture parameters related to amplitude is generally high. Therefore, the asphalt pavement model is more suitable for simulating amplitude-related parameters.

[0082] To verify whether the aggregate void ratio (VMA) of the asphalt pavement model meets the requirements, the calculated VMA of the DEM model is 20.34%, while the VMA of the actual pavement is 18.8%, which are close.

[0083] Based on the above verification, it is believed that the texture of the SMA-13 ​​asphalt pavement model is close to that of the actual pavement. Therefore, this simulation model can be used to study the surface texture parameters of asphalt pavement.

[0084] Furthermore, in another case study, the analysis methods for three-dimensional surface texture of asphalt pavement based on discrete element models were compared using AC-13, SMA-13, and OGFC-13 as examples. The constructed models for the three asphalt pavements are as follows: Figure 4 As shown.

[0085] After verification, all three types of asphalt pavement models with wearing course were found to be effective.

[0086] Select the average structural depth EMTD and kurtosis R. ku and root mean square wavelength λ q The three indicators were compared and analyzed to evaluate the texture characteristics of the asphalt pavement. The calculation results are listed below. Figures 5 to 7 As shown.

[0087] The calculated average texture depth (EMTD) of the SMA asphalt pavement model was 1.31, that of the AC asphalt pavement model was 0.8, and that of the OGFC asphalt pavement model was 1.78. The average texture depth (EMTD) of SMA-13 ​​was 60% higher than that of AC-13, while OGFC had the highest average texture depth (EMTD). This is because a coarser aggregate skeleton is more likely to generate height differences in the amplitude direction, while fine aggregates can fill the differences between coarse aggregates. Therefore, the higher the coarse aggregate content, the larger the amplitude-related texture parameter; conversely, the higher the fine aggregate content, the smaller the amplitude-related texture parameter.

[0088] The kurtosis R of the SMA asphalt pavement model was calculated. ku The kurtosis R of the AC asphalt pavement model is 3.88. kuThe kurtosis R of the OGFC asphalt pavement model is 3.78. ku The kurtosis R of these three types of asphalt pavement is 3.83. ku The differences are small, but their data dispersion differs significantly. This indicates that the higher the coarse aggregate content, the greater the dispersion of texture parameters related to magnitude. Therefore, the kurtosis R... ku It is less affected by the skeleton type and is likely mainly determined by the scrolling method or the number of times;

[0089] The root mean square wavelength λ of the SMA asphalt pavement model was calculated. q The root mean square wavelength λ of the AC asphalt pavement model is 12.22. q The root mean square wavelength λ of the OGFC asphalt pavement model is 13.43. q The root mean square wavelength λ of the AC asphalt pavement model is 11.52. q The asphalt pavement models larger than SMA and OGFC show that, because fine aggregates effectively fill the gaps in the wear course skeleton, the increased fine aggregate content makes the wear course surface smoother. Therefore, the higher the fine aggregate content, the higher the root mean square wavelength λ. q Also bigger;

[0090] By comparing and analyzing the texture parameters of different models, it can be concluded that the gradation with more coarse aggregate and less fine aggregate has better anti-skid performance.

[0091] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model, characterized in that, include: Step S1: Select representative coarse aggregates from the aggregates that make up the asphalt pavement wearing course, and scan each selected coarse aggregate to obtain its surface morphology as the first real aggregate profile model. Step S2: Construct a three-dimensional discrete element model of asphalt mixture based on the first real aggregate profile model as an asphalt pavement model, endow the asphalt pavement model with micro-mechanical properties between aggregate particles, and calculate the texture parameters and aggregate void ratio of the asphalt pavement model. Step S3: Calculate the surface texture parameters and aggregate void ratio of the real asphalt pavement with the same aggregate and gradation, and compare them with the texture parameters and aggregate void ratio of the asphalt pavement model. If the error is too large, return to step S2 to adjust the micromechanical properties between aggregate particles in the asphalt pavement model. Step S2 includes: Step S2-1: Based on the gradation data of the asphalt pavement wearing course, randomly generate different balls to obtain the asphalt pavement model, where one ball represents one aggregate particle; Step S2-2: Replace each ball with a diameter larger than the second preset particle size with one of the first real aggregate profile models; Step S2-3: Compact the obtained asphalt pavement model; Step S2-4: Impart micromechanical properties between aggregate particles in the mixture; Step S2-5: Extract the surface texture coordinates of the asphalt pavement model and calculate the texture parameters and aggregate void ratio of the asphalt pavement model; The mineral aggregate interstitial ratio is: , in: VMA The void ratio of the mineral aggregate. V agg The total volume of aggregates, V tot This represents the total volume of the mixture.

2. The method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model according to claim 1, characterized in that, The representative coarse aggregate is coarse aggregate with a particle size larger than the first preset particle size, and the angularity of all representative coarse aggregates is different from each other.

3. The method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model according to claim 1, characterized in that, In step S2-2, the replacement order is as follows: replacement is carried out step by step from high to low according to particle size classification.

4. The method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model according to claim 1, characterized in that, The compaction process in steps S2-3 consists of 8 rolling compactions, with a compaction force of 120 kN and a steel wheel radius of 550 mm.

5. The method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model according to claim 1, characterized in that, The surface texture parameters include average construction depth. EMTD kurtosis R ku and root mean square wavelength λ q .

6. The method for modeling asphalt pavement surface texture based on a three-dimensional discrete element model according to claim 5, characterized in that, The average construction depth EMTD The mathematical expression is: , in: l x The length of the sampling area, l y The width of the sampling area. z p ( x , y ) is a point ( x,y The peak value at ) z ( x , y ) is a point ( x,y Texture contour value at () The kurtosis R ku The mathematical expression is: , in: R q The root mean square roughness; The root mean square wavelength λ q The mathematical expression is: , Wherein: △ q The root mean square slope, π Pi is the mathematical constant of a circle.

7. A device for modeling asphalt pavement surface texture based on a three-dimensional discrete element model, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-6.

8. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in any one of claims 1-6.

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