Asphalt mixture strength prediction method and system based on discrete element method simulation

Abstract

The invention belongs to the technical field of asphalt mixture strength prediction, and discloses an asphalt mixture strength prediction method and system based on discrete element method simulation. The method comprises the following steps: calculating a bearing capacity index of a compacted and formed asphalt mixture coarse aggregate contact network; establishing a relationship between the interlocking friction characteristic of the coarse aggregate and the high-temperature deformation resistance of the asphalt mixture; establishing an asphalt mortar compactness index calculation model; establishing an asphalt mixture strength prediction model considering a coarse aggregate interlocking friction effect and an asphalt mortar compaction effect; and verifying the reliability of the strength prediction model by adopting an asphalt mixture shear slip test. According to the method, the three-dimensional dynamic virtual compaction model is established through the discrete element method, the strength forming process of the asphalt mixture is accurately simulated, the microcosmic strength characteristics of the asphalt mixture are explored by combining the interlocking friction effect of the coarse aggregate and the bonding and filling effect of the asphalt mortar, and the strength prediction level of the asphalt mixture can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of asphalt mixture strength prediction, and in particular relates to an asphalt mixture strength prediction method and system based on discrete element method simulation. Background Art

[0002] In recent years, the construction of highway transportation infrastructure has achieved leapfrog development and played an important supporting role in economic construction. Existing technologies have conducted a lot of research on the strength characteristics and decay characteristics of asphalt mixtures. Mounes et al. analyzed the relationship between the creep parameters and deformation characteristics of the mixture. Sun Lijun et al. and Xie Jun et al. proposed uniaxial penetration test and torsional shear test under normal stress to evaluate the shear resistance of asphalt mixtures. Zhu et al. used a simplified triaxial repeated load test to evaluate the deformation resistance of modified asphalt mixtures. Rueda et al. used a hollow cylinder test to analyze the influence of external environmental coupling on the mechanical strength of asphalt mixtures.

[0003] The mechanical strength of asphalt mixture mainly comes from the contact network formed by the mutual interlocking and friction of coarse aggregates and the bonding and filling effect of asphalt mortar. Asphalt mixture is a typical multiphase granular material with discrete, dissipative and discontinuous structures. However, it is difficult to reveal the strength formation mechanism of asphalt mixture using traditional mechanical test methods.

[0004] Through the above analysis, the problems and defects of the existing technology are as follows: the existing technology cannot accurately simulate the strength formation process of asphalt mixture, cannot observe the strength characteristics of asphalt mixture in detail, and has a relatively low accuracy in predicting the strength of asphalt mixture. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the disclosed embodiment of the present invention provides a method and system for predicting the strength of asphalt mixture based on discrete element method simulation.

[0006] The technical solution is as follows: A method for predicting asphalt mixture strength based on discrete element method simulation includes the following steps: S1, a three-dimensional dynamic virtual compaction model of asphalt mixture is established by using discrete element method, the contact parameters between coarse aggregates of asphalt mixture specimens under the designed compaction times are measured by discrete element software, and the bearing capacity index of the coarse aggregate contact network of asphalt mixture after compaction is calculated; S2, establish the relationship between the friction characteristics of coarse aggregate and the high temperature deformation resistance of asphalt mixture; S3, proposed a compaction index to evaluate the bonding and filling effect of asphalt mortar on the coarse aggregate contact network, and established a calculation model for the compaction index of asphalt mortar; S4. According to the influence of the friction characteristics of coarse aggregate on the high-temperature deformation resistance of asphalt mixture, a strength prediction model of asphalt mixture considering the friction effect of coarse aggregate and the compaction effect of asphalt mortar is established; S5, the reliability of the strength prediction model is verified by using asphalt mixture shear slip test.

[0007] In step S1, the contact parameters between coarse aggregates of the asphalt mixture specimens at the designed compaction times are measured by discrete element software, including: obtaining the geometric shape of the coarse aggregate by a blue light 3D scanner and assembling a coarse aggregate discrete element model, using spherical units with a radius of 0.7 mm to replace fine aggregates and asphalt mortar below 2.36 mm, and constructing a 3D dynamic rotational compaction model of the asphalt mixture by using wall servo and loading plate drive.

[0008] In step S1, the bearing capacity index of the coarse aggregate contact network of the compacted asphalt mixture is calculated, and the expression is: ; In the formula, is the bearing capacity index, For aggregate particle size classification, i=4.75, 9.5, 13.2, 16, 19, 26.5, 31.5, 37.5, is the nominal maximum particle size, is the relative contact length of any two contacting coarse aggregates, is the relative contact volume of any two contacting coarse aggregates, is the relative contact area between any two contacting coarse aggregates, is the contact force, is the number of contact points, is the angle between the contact force and the loading direction, is the height of the virtual specimen, is the volume of the virtual specimen.

[0009] In step S2, the relationship between the coarse aggregate interlocking friction characteristics and the high temperature deformation resistance of the asphalt mixture is established, including the following steps: S201, using AC-13 upper limit, AC-13 middle value, AC-13 lower limit, AC-16 middle value, AC-20 middle value 5 gradations to mix and shape asphalt mixture; S202, the high temperature deformation resistance of asphalt mixtures was evaluated using an interface shear slip test device, and the maximum slip shear stress of five asphalt mixtures was measured; S203, using discrete element software to simulate the compaction process of the above five asphalt mixtures, and calculating the bearing capacity index of the coarse aggregate contact network after compaction; S204, obtaining the relationship between the coarse aggregate contact network bearing capacity index and the maximum sliding shear stress of five asphalt mixtures.

[0010] In step S3, a calculation model for the compactness index of asphalt mortar is established, and the expression is: ; In the formula, is the density index of asphalt mortar, is the amount of mineral powder used, is the total mass of mineral aggregate, For the The mesh size of the file, For the The mass fraction of the aggregate, For the The weighted surface area of ​​the aggregate, For the The density of the aggregate, For the The weighted volume of the aggregate.

[0011] In step S4, a strength prediction model for asphalt mixture is established considering the coarse aggregate interlocking friction effect and the asphalt mortar compaction effect. The expression is: ; In the formula, It is the strength index of asphalt mixture.

[0012] In step S5, the reliability of the strength prediction model is verified by using a shear slip test of an asphalt mixture, including the following steps: S501, using discrete element numerical simulation technology to calculate the strength index of five asphalt mixtures: AC-13 upper limit, AC-13 median, AC-13 lower limit, AC-16 median, and AC-20 median; S502, linearly fit the strength index of the five asphalt mixtures with the maximum sliding shear stress. If the correlation coefficient exceeds 0.8, there is a correlation.

[0013] Another object of the present invention is to provide an asphalt mixture strength prediction system based on discrete element method simulation, the system implements the asphalt mixture strength prediction method based on discrete element method simulation, the system comprises: The bearing capacity index calculation module is used to establish a three-dimensional dynamic virtual compaction model of asphalt mixture using the discrete element method, measure the contact parameters between coarse aggregates of the asphalt mixture specimen under the designed compaction times through the discrete element software, and calculate the bearing capacity index of the coarse aggregate contact network of the asphalt mixture after compaction; A module for establishing the relationship between high temperature deformation resistance, which is used to establish the relationship between the friction characteristics of coarse aggregate and the high temperature deformation resistance of asphalt mixture; Asphalt mortar density index calculation model establishment module, used to propose a density index to evaluate the bonding and filling effect of asphalt mortar on the coarse aggregate contact network, and to establish an asphalt mortar density index calculation model; Asphalt mixture strength prediction model establishment module, which is used to establish an asphalt mixture strength prediction model that takes into account the coarse aggregate embedding friction effect and the asphalt mortar compaction effect according to the influence of the coarse aggregate embedding friction characteristics on the high-temperature deformation resistance of the asphalt mixture; The reliability verification module is used to verify the reliability of the strength prediction model using asphalt mixture shear slip test.

[0014] Combining all the above technical solutions, the beneficial effects of the present invention are as follows: the present invention proposes a method for predicting the strength of asphalt mixtures based on discrete element method simulation, improves the asphalt mixture design and analysis system, and improves the durability of the pavement. The present invention establishes a three-dimensional dynamic virtual compaction model through the discrete element method, accurately simulates the strength formation process of the asphalt mixture, combines the interlocking friction effect of coarse aggregate and the bonding and filling effect of asphalt mortar, explores its microscopic strength characteristics, and helps to improve the strength prediction level of asphalt mixtures.

[0015] The asphalt mixture strength prediction system based on discrete element method simulation proposed in the present invention is applied to road engineering. On the one hand, the strength of asphalt mixture can be predicted in the design stage, which can help design units optimize raw material selection and grading design; on the other hand, it can improve the mechanical strength and durability of the road surface, reduce the number of maintenance and repair times, and save maintenance costs. At present, the evaluation of asphalt mixture strength at home and abroad mostly relies on traditional mechanical experiments, which are difficult to fully reflect the internal mesostructure and strength formation mechanism. The present invention proposes a three-dimensional dynamic virtual compaction model based on the discrete element method, which reveals the contribution of the coarse aggregate embedding friction effect and the asphalt mortar compaction effect to the strength formation from a mesoscopic level.

[0016] The present invention effectively solves the problem of asphalt mixture strength prediction that has long plagued the industry; traditional methods are difficult to quantitatively characterize the role of coarse aggregate and asphalt mortar in strength formation, resulting in difficulty in effectively solving the problem of asphalt mixture strength prediction. The present invention realizes the comprehensive quantification of the coarse aggregate extrusion friction effect and the asphalt mortar compaction effect for the first time. This technology breaks through the limitations of the industry's reliance on experimental experience for many years, provides a high-precision and high-efficiency strength prediction tool for road projects, and can promote intelligent and scientific road construction. Traditional asphalt mixture strength analysis methods usually explain the influence of a single factor through the control variable method, such as grading, asphalt properties, asphalt dosage, etc., resulting in great challenges in the establishment of prediction models. Based on the discrete element method, the present invention comprehensively considers the extrusion friction effect of coarse aggregate and the compaction effect of asphalt mortar, and incorporates the interaction between the two into the strength prediction framework, overcoming the singleness and limitations of traditional methods from the micro-mechanism level, and providing new ideas for the innovative development of asphalt mixture strength prediction models. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure; Figure 1 It is a flow chart of a method for predicting asphalt mixture strength based on discrete element method simulation provided by an embodiment of the present invention; Figure 2 is a relationship diagram between the bearing capacity index and the maximum slip shear stress provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of an asphalt mixture strength prediction system based on discrete element method simulation provided by an embodiment of the present invention; In the figure: 1. Bearing capacity index calculation module; 2. High temperature deformation resistance relationship establishment module; 3. Asphalt mortar density index calculation model establishment module; 4. Asphalt mixture strength prediction model establishment module; 5. Reliability verification module. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0019] The innovation of the present invention is that: the present invention proposes a method and system for predicting the strength of asphalt mixture based on discrete element method. By establishing a three-dimensional dynamic virtual compaction model, the strength formation process of asphalt mixture is accurately simulated, and the synergistic mechanism of coarse aggregate and asphalt mortar is revealed from a microscopic level. First, the quantitative analysis of the coarse aggregate embedding friction effect and the asphalt mortar compaction effect is innovatively introduced, and the bearing capacity index and compaction index calculation models are established to comprehensively characterize the mechanical properties of asphalt mixture. The present invention overcomes the technical bottleneck that traditional test methods and mechanical parameters are difficult to reflect the microscopic mechanical mechanism of asphalt mixture, and provides a more scientific technical tool for predicting the performance of asphalt pavement. This method can be widely used in road material design, construction quality control and pavement operation and maintenance, and can improve the durability and performance of the pavement.

[0020] Embodiment 1, as Figure 1 As shown, an embodiment of the present invention provides an asphalt mixture strength prediction method based on discrete element method simulation, comprising the following steps: S1, a three-dimensional dynamic virtual compaction model of asphalt mixture is established by using discrete element method, the contact parameters between coarse aggregates of asphalt mixture specimens under the designed compaction times are measured by discrete element software, and the bearing capacity index of the coarse aggregate contact network of asphalt mixture after compaction is calculated; S2, establish the relationship between the friction characteristics of coarse aggregate and the high temperature deformation resistance of asphalt mixture; S3, proposed a compaction index to evaluate the bonding and filling effect of asphalt mortar on the coarse aggregate contact network, and established a calculation model for the compaction index of asphalt mortar; S4. According to the influence of the friction characteristics of coarse aggregate on the high-temperature deformation resistance of asphalt mixture, a strength prediction model of asphalt mixture considering the friction effect of coarse aggregate and the compaction effect of asphalt mortar is established; S5, the reliability of the strength prediction model is verified by using asphalt mixture shear slip test.

[0021] Exemplarily, in step S1, the geometric shape of the coarse aggregate is obtained by a blue light 3D scanner and a discrete element model of the coarse aggregate is assembled, spherical units with a radius of 0.7 mm are used to replace fine aggregates and asphalt mortar below 2.36 mm, and a three-dimensional dynamic rotational compaction model of asphalt mixture is constructed by wall servo and loading plate drive.

[0022] Exemplarily, in step S1, the number of compaction times is designed to be 100 times.

[0023] For example, in step S1, the bearing capacity index of the contact network of the coarse aggregate of the asphalt mixture after compaction is calculated. for: ; In the formula, is the bearing capacity index, For aggregate particle size classification, i=4.75, 9.5, 13.2, 16, 19, 26.5, 31.5, 37.5, is the nominal maximum particle size, is the relative contact length of any two contacting coarse aggregates, is the relative contact volume of any two contacting coarse aggregates, is the relative contact area between any two contacting coarse aggregates, is the contact force, is the number of contact points, is the angle between the contact force and the loading direction, is the height of the virtual specimen, is the volume of the virtual specimen.

[0024] Exemplarily, in step S2, establishing the relationship between the coarse aggregate squeezing friction characteristics and the high temperature deformation resistance of the asphalt mixture includes: S201 uses the five gradations of AC-13 upper limit, AC-13 median, AC-13 lower limit, AC-16 median, and AC-20 median recommended in the Technical Specifications for Highway Asphalt Pavement Construction (JTGF40-2004) to mix and shape asphalt mixture.

[0025] S202, the interface shear slip test device was used to evaluate the high-temperature deformation resistance of asphalt mixtures and measure the maximum slip shear stress of five asphalt mixtures.

[0026] S203, using discrete element software to simulate the compaction process of the above five asphalt mixtures, and calculating the contact network bearing capacity index of the coarse aggregate after compaction.

[0027] S204, the relationship between the contact network bearing capacity index and the maximum sliding shear stress of the five asphalt mixtures is shown in Figure 2 shown.

[0028] according to Figure 2 It can be seen that the coarse aggregate contact network bearing capacity index of asphalt mixture is CFI The correlation with the maximum slip shear stress is poor. It is only 0.77, which shows that the interlocking friction between coarse aggregates cannot directly reflect the stability of the spatial structure of asphalt mixture. CFI It has a good linear correlation with the maximum slip shear stress, with a correlation coefficient of 0.93. The reason is that there are more large-size fine aggregates in the AC-13 lower limit, which reduces the integrity of the asphalt mortar and the bonding and filling effect on the coarse aggregate contact structure, resulting in the poor ability of the AC-13 lower limit asphalt mixture to resist deformation. Figure 2 The data point circled in the middle is the lower limit of AC-13. The correlation coefficient is calculated by grading the lower limit of AC-13 with the upper limit of AC-13, the median of AC-13, the median of AC-16, and the median of AC-20. is 0.77; if the lower limit of AC-13 is not included, there are only four gradations: the upper limit of AC-13, the median of AC-13, the median of AC-16, and the median of AC-20. It is 0.93.

[0029] Exemplarily, in step S3, the present invention innovatively proposes that the asphalt mortar density index is established MI The calculation model is: ; In the formula, is the density index of asphalt mortar, is the amount of mineral powder used, is the total mass of mineral aggregate, For the The mesh size of the file, For the The mass fraction of the aggregate, For the The weighted surface area of ​​the aggregate, For the The density of the aggregate, For the The weighted volume of the aggregate.

[0030] It is understandable that the density index of asphalt mortar is used The calculation model can predict the strength of asphalt mixture in the mix design stage, eliminating the need for a large number of mix design experiments and improving work efficiency.

[0031] Exemplarily, in step S4, the asphalt mixture strength prediction model considering the coarse aggregate squeezing friction effect and the asphalt mortar compaction effect is established as follows: ; In the formula, It is the strength index of asphalt mixture.

[0032] It can be understood that the present invention adopts the above-mentioned asphalt mixture strength prediction model to predict the strength of the asphalt mixture in the mix design stage, without the need for a large number of mix design experiments, thereby improving work efficiency.

[0033] Exemplarily, in step S5, the asphalt mixture shear slip test is used to verify the reliability of the strength prediction model, including: S501, combined with discrete element numerical simulation technology, calculates the strength index of five asphalt mixtures: AC-13 upper limit, AC-13 median, AC-13 lower limit, AC-16 median, and AC-20 median.

[0034] It can be understood that the compaction process of these five asphalt mixtures was simulated by discrete element method, the bearing capacity index was calculated, and then the asphalt mortar density index was calculated, and then the strength index was calculated.

[0035] S502, linearly fitting the strength index of the five asphalt mixtures with the maximum sliding shear stress, if the correlation coefficient exceeds 0.8, it indicates that there is a correlation, and further indicates that the established asphalt mixture strength prediction model is reliable.

[0036] It can be understood that in the prior art, after performing a correlation analysis between the composite angular index and the skeleton contact index, it was found that the correlation was good, so the composite angular index was used instead of the skeleton contact index for calculation. However, during the compaction process of asphalt mixture, coarse aggregates are mutually squeezed and rubbed, and the number of contact points, the magnitude and direction of the contact force, the contact state, and the contact position are all produced under the joint action of the asphalt coating and the particle characteristics of the aggregate (shape, texture, angularity, size, and quantity), while the composite angularity index only considers the angular characteristics and size distribution of the aggregate. Although the composite angularity index has a good correlation with the skeleton contact index, its scientificity needs to be further improved when used for strength index calculation. Therefore, this application linearly fits the strength index of these five asphalt mixtures with the maximum sliding shear stress, and the asphalt mixture strength prediction model has high reliability.

[0037] Embodiment 2, as Figure 3 As shown, an embodiment of the present invention provides an asphalt mixture strength prediction system based on discrete element method simulation, including: The bearing capacity index calculation module 1 is used to establish a three-dimensional dynamic virtual compaction model of asphalt mixture by using discrete element method, measure the contact parameters between coarse aggregates of asphalt mixture specimens under the designed compaction times by discrete element software, and calculate the bearing capacity index of the coarse aggregate contact network of the asphalt mixture after compaction; A module 2 for establishing a relationship between the high temperature deformation resistance and the coarse aggregate interlocking friction characteristics and the high temperature deformation resistance of the asphalt mixture; Asphalt mortar density index calculation model establishment module 3 is used to propose a density index to evaluate the bonding and filling effect of asphalt mortar on the coarse aggregate contact network and establish an asphalt mortar density index calculation model; Asphalt mixture strength prediction model establishment module 4 is used to establish an asphalt mixture strength prediction model that takes into account the coarse aggregate embedding friction effect and the asphalt mortar compaction effect according to the influence of the coarse aggregate embedding friction characteristics on the high temperature deformation resistance of the asphalt mixture; The reliability verification module 5 is used to verify the reliability of the strength prediction model by using the asphalt mixture shear slip test.

[0038] To further illustrate the effects of the embodiments of the present invention, the following experimental examples are conducted.

[0039] Test Example 1: A method for predicting asphalt mixture strength based on discrete element method simulation, comprising the following steps: Step 1: Use a blue light scanner to scan aggregates above 2.36 mm and assemble virtual aggregates. The discrete element method is used to set the contact stiffness and contact slip model between aggregates, and the Burgers model is set between aggregates and asphalt mortar and asphalt mortar to simulate the contact behavior between the components of the asphalt mixture, and then establish the AC-13 median asphalt mixture virtual specimen. The pass rate of the AC-13 median gradation is shown in Table 1, and the asphalt dosage is 4.8%. A vertical pressure of 600Kpa, an internal rotation angle of 1.15°, and a rotation rate of 30r / min are applied to the top wall of the virtual specimen, and the asphalt mixture specimen is compacted at the designed compaction times of 100. The lower vertical pressure is set to 600Kpa, the internal rotation angle is 1.15°, and the rotation rate is 30r / min.

[0040] Table 1 AC-13 median gradation pass rate

[0041] Step 2: Calculate the bearing capacity index of the coarse aggregate contact network of the asphalt mixture after compaction : ; Step 3: Calculate the density index of asphalt mortar Computational model: ; Step 4: Calculate the asphalt mixture strength index for the coarse aggregate squeeze friction effect and the asphalt mortar compaction effect: ; In the formula, It is the strength index of asphalt mixture.

[0042] After calculation, the strength index of AC-13 median asphalt mixture is 0.00765.

[0043] Test Example 2: The strength index of AC-16 median asphalt mixture was calculated using the asphalt mixture strength prediction method based on discrete element method simulation.

[0044] Step 1: Use a blue light scanner to scan aggregates above 2.36 mm and assemble virtual aggregates. The discrete element method is used to set the contact stiffness and contact slip model between aggregates, and the Burgers model is set between aggregates and asphalt mortar and asphalt mortar to simulate the contact behavior between the components of the asphalt mixture, and then establish the AC-16 median asphalt mixture virtual specimen. The pass rate of the AC-16 median gradation is shown in Table 2, and the asphalt dosage is 4.5%. Apply a vertical pressure of 600Kpa, an internal rotation angle of 1.15°, and a rotation rate of 30r / min to the top wall of the virtual specimen, and compact the asphalt mixture specimen at the designed compaction times of 100. The lower vertical pressure is set to 600Kpa, the internal rotation angle is 1.15°, and the rotation rate is 30r / min.

[0045] Table 2 AC-16 median gradation pass rate

[0046] Step 2: Calculate the bearing capacity index of the coarse aggregate contact network of the asphalt mixture after compaction ： ; Step 3: Calculate the density index of asphalt mortar Computational model: ; Step 4: Calculate the asphalt mixture strength index of the coarse aggregate interlocking friction effect and the asphalt mortar compaction effect: ; In the formula, It is the strength index of asphalt mixture.

[0047] After calculation, the strength index of AC-16 median asphalt mixture is 0.0079554.

[0048] Test Example 3: The strength index of AC-20 median asphalt mixture was calculated using the asphalt mixture strength prediction method based on discrete element method simulation.

[0049] Step 1: Use a blue light scanner to scan aggregates above 2.36 mm and assemble virtual aggregates. The discrete element method is used to set the contact stiffness and contact slip model between aggregates, and the Burgers model is set between aggregates and asphalt mortar and asphalt mortar to simulate the contact behavior between the components of the asphalt mixture, and then establish the AC-20 median asphalt mixture virtual specimen. The pass rate of the AC-20 median gradation is shown in Table 3, and the asphalt dosage is 4.2%. A vertical pressure of 600Kpa, an internal rotation angle of 1.15°, and a rotation rate of 30r / min are applied to the top wall of the virtual specimen, and the asphalt mixture specimen is compacted at the designed compaction times of 100. The lower vertical pressure is set to 600Kpa, the internal rotation angle is 1.15°, and the rotation rate is 30r / min.

[0050] Table 3 AC-20 median gradation pass rate

[0051] Step 2: Calculate the bearing capacity index of the coarse aggregate contact network of the asphalt mixture after compaction ： ; Step 3: Calculate the density index of asphalt mortar MI The calculation model is: ; Step 4: Calculate the asphalt mixture strength index of the coarse aggregate interlocking friction effect and the asphalt mortar compaction effect: ; In the formula, It is the strength index of asphalt mixture.

[0052] After calculation, the strength index of AC-20 median asphalt mixture is 0.0087696.

[0053] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting asphalt mixture strength based on discrete element method simulation, characterized in that: The method comprises the following steps: S1, a three-dimensional dynamic virtual compaction model of asphalt mixture is established by using discrete element method, the contact parameters between coarse aggregates of asphalt mixture specimens under the designed compaction times are measured by discrete element software, and the bearing capacity index of the coarse aggregate contact network of asphalt mixture after compaction is calculated; S2, establish the relationship between the friction characteristics of coarse aggregate and the high temperature deformation resistance of asphalt mixture; S3, proposed a compaction index to evaluate the bonding and filling effect of asphalt mortar on the coarse aggregate contact network, and established a calculation model for the compaction index of asphalt mortar; S4. According to the influence of the friction characteristics of coarse aggregate on the high-temperature deformation resistance of asphalt mixture, a strength prediction model of asphalt mixture considering the friction effect of coarse aggregate and the compaction effect of asphalt mortar is established; S5, the reliability of the strength prediction model is verified by using asphalt mixture shear slip test.

2. The asphalt mixture strength prediction method based on discrete element method simulation according to claim 1 is characterized in that: In step S1, the contact parameters between coarse aggregates of the asphalt mixture specimens at the designed compaction times are measured by discrete element software, including: obtaining the geometric shape of the coarse aggregate by a blue light 3D scanner and assembling a coarse aggregate discrete element model, using spherical units with a radius of 0.7 mm to replace fine aggregates and asphalt mortar below 2.36 mm, and constructing a 3D dynamic rotational compaction model of the asphalt mixture by using wall servo and loading plate drive.

3. The asphalt mixture strength prediction method based on discrete element method simulation according to claim 1 is characterized in that: In step S1, the bearing capacity index of the coarse aggregate contact network of the compacted asphalt mixture is calculated, and the expression is: ； In the formula, is the bearing capacity index, For aggregate size classification, is the nominal maximum particle size, is the relative contact length of any two contacting coarse aggregates, is the relative contact volume of any two contacting coarse aggregates, is the relative contact area between any two contacting coarse aggregates, is the contact force, is the number of contact points, is the angle between the contact force and the loading direction, is the height of the virtual specimen, is the volume of the virtual specimen.

4. The asphalt mixture strength prediction method based on discrete element method simulation according to claim 1 is characterized in that: In step S2, the relationship between the coarse aggregate interlocking friction characteristics and the high temperature deformation resistance of the asphalt mixture is established, including the following steps: S201, using AC-13 upper limit, AC-13 middle value, AC-13 lower limit, AC-16 middle value, AC-20 middle value 5 gradations to mix and shape asphalt mixture; S202, the high temperature deformation resistance of asphalt mixtures was evaluated using an interface shear slip test device, and the maximum slip shear stress of five asphalt mixtures was measured; S203, using discrete element software to simulate the compaction process of the above five asphalt mixtures, and calculating the bearing capacity index of the coarse aggregate contact network after compaction; S204, obtaining the relationship between the coarse aggregate contact network bearing capacity index and the maximum sliding shear stress of five asphalt mixtures.

5. The asphalt mixture strength prediction method based on discrete element method simulation according to claim 2 is characterized in that: In step S3, a calculation model for the compactness index of asphalt mortar is established, and the expression is: ； In the formula, is the density index of asphalt mortar, is the amount of mineral powder used, is the total mass of mineral aggregate, For the The mesh size of the file, For the The mass fraction of the aggregate, For the The weighted surface area of ​​the aggregate, For the The density of the aggregate, For the The weighted volume of the aggregate.

6. The asphalt mixture strength prediction method based on discrete element method simulation according to claim 5 is characterized in that: In step S4, a strength prediction model for asphalt mixture is established considering the coarse aggregate interlocking friction effect and the asphalt mortar compaction effect. The expression is: ； In the formula, It is the strength index of asphalt mixture.

7. The asphalt mixture strength prediction method based on discrete element method simulation according to claim 1 is characterized in that: In step S5, the reliability of the strength prediction model is verified by using a shear slip test of an asphalt mixture, including the following steps: S501, using discrete element numerical simulation technology to calculate the strength index of five asphalt mixtures: AC-13 upper limit, AC-13 median, AC-13 lower limit, AC-16 median, and AC-20 median; S502, linearly fit the strength index of the five asphalt mixtures with the maximum sliding shear stress. If the correlation coefficient exceeds 0.8, there is a correlation.

8. An asphalt mixture strength prediction system based on discrete element method simulation, characterized in that: The system implements the asphalt mixture strength prediction method based on discrete element method simulation as claimed in any one of claims 1 to 7, and the system comprises: A bearing capacity index calculation module (1) is used to establish a three-dimensional dynamic virtual compaction model of asphalt mixture using a discrete element method, measure the contact parameters between coarse aggregates of the asphalt mixture specimen at the designed compaction times through discrete element software, and calculate the bearing capacity index of the coarse aggregate contact network of the asphalt mixture after compaction; A module for establishing a relationship between high temperature deformation resistance (2), used to establish a relationship between the coarse aggregate interlocking friction characteristics and the high temperature deformation resistance of the asphalt mixture; Asphalt mortar density index calculation model establishment module (3), used to propose a density index to evaluate the bonding and filling effect of asphalt mortar on the coarse aggregate contact network, and to establish an asphalt mortar density index calculation model; An asphalt mixture strength prediction model establishment module (4) is used to establish an asphalt mixture strength prediction model that takes into account the coarse aggregate interlocking friction effect and the asphalt mortar compaction effect according to the influence of the coarse aggregate interlocking friction characteristics on the high temperature deformation resistance of the asphalt mixture; The reliability verification module (5) is used to verify the reliability of the strength prediction model by using a shear slip test of the asphalt mixture.

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