Design method and system for asphalt pavement with ultra-large particle size and long service life

Abstract

The invention provides a design method and system for an ultra-large-particle-size long-service-life asphalt pavement, and belongs to the technical field of long-service-life asphalt pavement design. The method comprises the steps of obtaining basic parameter information of a pavement design project; according to the basic parameter information, the thickness and variation parameters of the pavement structure layers meeting the thickness requirement are initially planned, and the material Poisson's ratio, the temperature distribution of each structure layer and the material modulus are determined; randomly generating pavement structure layer thickness, temperature distribution, material modulus and traffic load parameters by adopting a Monte Carlo method, and carrying out pavement mechanical response calculation; checking the reliability and fatigue damage of the flexible base asphalt pavement according to a fatigue limit theory and a cumulative damage theory respectively; according to the flexible base asphalt pavement rut prediction model, pavement rut checking calculation is performed, the reliability of a pavement structure under the environment-load coupling effect is improved, and the long service life of the pavement structure is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of long-life asphalt pavement design, and particularly relates to a design method and system for an extra-large-size long-life asphalt pavement. Background Art

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] The semi-rigid base asphalt pavement has the advantages of high stiffness, strong bearing capacity, low cost, etc., and plays an extremely important role in the development and construction of expressways. However, the cracking problem of the semi-rigid base cannot be avoided, and it is difficult to meet the basic requirements of the long-life pavement for no structural damage. For a long time, the main reasons for the failure to comprehensively promote the full-depth asphalt pavement are: the project cost of the full-depth pavement is usually high; the concept of "strong base and thin surface" is deeply rooted, and there is a concern that the full-depth asphalt pavement is prone to rutting problems. The extra-large-size asphalt mixture (Large Stone Asphalt Mixture, LSAM-50) has excellent technical economy and is one of the target options for constructing a characteristic full-depth long-life asphalt pavement.

[0004] In terms of the design method of long-life asphalt pavement, although the current "Code for Design of Highway Asphalt Pavements" (JTG D50) stipulates the checking methods and requirements for asphalt layer fatigue cracking, semi-rigid base fatigue cracking, asphalt layer permanent deformation, vertical strain of the subgrade top surface, etc., it is mainly applicable to the design of asphalt pavements with a design service life of 15 years, while the design service life of long-life asphalt pavements is generally considered to reach more than 30 years. The patent with the publication number CN 101792992 A proposes a design method for long-life asphalt pavement with the fatigue cracking of the flexible base of asphalt mixture as the design index, considering the construction variability of pavement thickness and material modulus, the randomness of traffic axle load, and the time-varying law of pavement temperature. However, it only checks the reliability of the structure with the fatigue critical strain and has no permanent deformation (rutting) control index, and does not fully consider the spatio-temporal distribution characteristics of pavement temperature and the temperature dependence of asphalt mixture modulus. Asphalt mixture is a typical temperature-sensitive material, and its mechanical properties have significant temperature dependence. For long-life asphalt pavements, the temperature change inside the pavement structure has a significant impact on pavement performance and cannot be ignored. Summary of the Invention

[0005] To overcome the deficiencies of the above-mentioned existing technologies, the present invention provides a design method and system for an asphalt pavement with extra-large particle size and long service life. By obtaining the basic parameter information of the pavement design project, the Monte Carlo method is used to randomly generate the thickness of the pavement structural layer, temperature distribution, material modulus, and traffic load parameters, and the pavement mechanical response is calculated; and the reliability and fatigue damage of the flexible base asphalt pavement are checked; the pavement rutting is checked, etc., further improving the reliability of the pavement structure under the coupling action of the environment-load and achieving the long service life goal of the pavement structure.

[0006] To achieve the above object, one or more embodiments of the present invention provide the following technical solutions:

[0007] The first aspect of the present invention provides a design method for an asphalt pavement with extra-large particle size and long service life;

[0008] A design method for an asphalt pavement with extra-large particle size and long service life includes:

[0009] Obtain the basic information, traffic parameters, and environmental parameters of the pavement design project;

[0010] According to the traffic parameters, preliminarily determine the thickness of the pavement structural layer and the variation parameters that meet the requirements of the rutting critical depth and fatigue critical thickness, and determine the Poisson's ratio of each structural layer material; according to the environmental parameters, determine the temperature distribution of each structural layer; determine the temperature dependence model of the dynamic modulus of the asphalt mixture of each structural layer of the extra-large particle size asphalt pavement, and determine the material modulus according to the temperature dependence model;

[0011] Use the Monte Carlo method to randomly generate the thickness of the pavement structural layer, temperature distribution, material modulus, and traffic load parameters, and calculate the pavement mechanical response;

[0012] Respectively check the reliability and fatigue damage of the flexible base asphalt pavement according to the fatigue limit theory and the cumulative damage theory; check the pavement rutting according to the rutting prediction model of the flexible base asphalt pavement.

[0013] As a further technical solution, the basic information of the pavement design project includes the region where the project is located, highway grade, pavement design service life, the number of years from opening to the first rut repair, and the target reliability;

[0014] The traffic parameters include the traffic load level and the traffic axle load spectrum. The traffic load level is determined by obtaining the two-way annual average daily traffic volume of large buses and trucks on the section of the road, the annual average growth rate of traffic volume, the direction coefficient, and the lane coefficient, and according to the annual average daily traffic volume of large buses and trucks in the design lane; select the traffic axle load spectrum of the same grade in the same region according to the traffic load level;

[0015] The environmental parameters include the pavement temperature spectrum, and the pavement temperature spectrum statistically analyzes the distribution coefficient of the pavement depth temperature range based on the annual measured temperature data of each structural layer of the flexible base asphalt pavement.

[0016] As a further technical solution, the process of randomly generating the pavement structural layer thickness, temperature distribution, material modulus, and traffic load parameters by using the Monte Carlo method and calculating the pavement mechanical response is as follows:

[0017] Set the Monte Carlo cycle number, and randomly generate the thickness of each structural layer according to the preliminary pavement structural layer thickness and variation parameters;

[0018] According to the thickness combination of each structural layer and the pavement temperature spectrum, randomly generate the temperature of each structural layer;

[0019] Generate the dynamic modulus of each asphalt mixture layer according to the temperature of each structural layer and the temperature dependence model of the dynamic modulus of asphalt mixture;

[0020] Randomly generate the subgrade modulus according to the preliminary subgrade modulus and its variability parameters;

[0021] Randomly generate the axle type and axle weight according to the traffic axle load spectrum;

[0022] Adopt the elastic layered system theory to calculate the bottom tensile strain of the flexible base and the vertical compressive strain of the subgrade top surface respectively;

[0023] Repeatedly sample according to the above method to construct a Monte Carlo calculation table.

[0024] As a further technical solution, the process of performing reliability verification on the flexible base asphalt pavement according to the fatigue limit theory is as follows:

[0025] Based on the fatigue limit theory of long-life asphalt pavement design, when the bottom flexural tensile strain of the asphalt mixture layer is less than the fatigue limit, the pavement will not undergo fatigue cracking originating from the bottom of the asphalt layer;

[0026] Count the number of combinations in the Monte Carlo calculation table where the mechanical response is lower than the critical threshold, and calculate the actual reliability of the pavement according to the Monte Carlo cycle number. When it is greater than or equal to the target reliability P m At this time, the pavement design meets the reliability requirements; otherwise, adjust the pavement structure plan and re-check until the requirements are met. The formula is:

[0027]

[0028] In the formula, P s is the actual reliability; P m is the target reliability; N s is the Monte Carlo cycle number; n s is the number of combinations in the Monte Carlo cycle where the mechanical response is lower than the critical threshold.

[0029] As a further technical solution, the process of fatigue damage checking for flexible base asphalt pavement according to the cumulative damage theory is as follows:

[0030] Based on Miner's linear damage accumulation principle, calculate the single damage D when the mechanical response of each combination of input parameters in the Monte Carlo calculation table exceeds the damage critical threshold according to the fatigue equation i = 1 / N fi , and thus calculate the cumulative damage ∑D of one Monte Carlo cycle i , and then determine the average damage D of each group of parameters a = ∑D i / N s ;

[0031]

[0032] In the formula: N f is the fatigue cracking life of the LSAM-50 flexible base; ε is the bottom tensile strain of the flexible base determined by each Monte Carlo cycle calculation; T is the pavement temperature randomly generated by each Monte Carlo cycle; k c is the on-site comprehensive correction coefficient;

[0033] Combined with the average damage determined by the Monte Carlo cycle, calculate the number of axle load actions reaching the fatigue failure limit value, calculate the average daily axle load action number of the design lane in the initial year, and the expected service life of the pavement

[0034] As a further technical solution, the process of pavement rutting checking according to the rutting prediction model of flexible base asphalt pavement is as follows:

[0035] Calculate the cumulative equivalent axle load within the first rutting repair period after opening to traffic;

[0036] According to the temperature spectrum at each depth layer of the pavement, determine the distribution frequency of each temperature interval throughout the year, use the midpoint of the temperature interval as the representative value of the interval as the temperature of each layer, and calculate the cumulative equivalent design axle load action number within the first rutting repair period;

[0037] Calculate the pavement rutting based on the rutting prediction model. The rutting prediction model is as follows:

[0038]

[0039]

[0040] In the formula: R a is the cumulative rutting deformation of the asphalt mixture layer; R aij is the rutting deformation of each layer of the asphalt mixture layer in different temperature intervals; k c is the on-site comprehensive correction coefficient; αN and α T and α P are the action times exponent, temperature exponent, and load exponent respectively; N e2ij is the cumulative equivalent design axle load action times of the i-th layer in the m-th temperature range within the first rut repair period; T i is the temperature of each layer, taking the midpoint of each temperature range in the temperature spectrum; p i is the load of each layer, which is the vertical compressive stress on the top surface of each layer under the standard temperature.

[0041] As a further technical solution, the design method for a super-large particle size long-life asphalt pavement further includes:

[0042] For seasonal frozen soil areas, conduct pavement low-temperature cracking and anti-freezing thickness checks, and calculate the acceptance deflection value of the designed pavement structure.

[0043] The second aspect of the present invention provides a design system for a super-large particle size long-life asphalt pavement.

[0044] A design system for a super-large particle size long-life asphalt pavement includes:

[0045] A basic parameter information acquisition module, configured to: acquire the basic information, traffic parameters, and environmental parameters of the pavement design project;

[0046] A pavement parameter acquisition module, configured to: initially determine the pavement structure layer thickness and variation parameters that meet the requirements of rut critical depth and fatigue critical thickness according to traffic parameters, and determine the Poisson's ratio of each structural layer material; determine the temperature distribution of each structural layer according to environmental parameters; determine the temperature dependence model of the asphalt mixture dynamic modulus of each structural layer of the super-large particle size asphalt pavement, and determine the material modulus according to the temperature dependence model;

[0047] A Monte Carlo calculation module, configured to: randomly generate pavement structure layer thickness, temperature distribution, material modulus, and traffic load parameters by using the Monte Carlo method, and perform pavement mechanical response calculations;

[0048] A pavement reliability check module, configured to: perform reliability and fatigue damage checks on flexible base asphalt pavements respectively according to the fatigue limit theory and cumulative damage theory; perform pavement rut checks, pavement low-temperature cracking and anti-freezing thickness checks according to the rut prediction model of flexible base asphalt pavements.

[0049] The third aspect of the present invention provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, it implements the steps in the design method for a super-large particle size long-life asphalt pavement as described in the first aspect of the present invention.

[0050] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, the steps in a design method for an extra-large particle size long-life asphalt pavement as described in the first aspect of the present invention are implemented.

[0051] The above one or more technical solutions have the following beneficial effects:

[0052] Based on the basic principle of pavement reliability design, on the basis of the construction variability of pavement structure thickness and the randomness of traffic loads, the present invention fully considers the spatio-temporal variation characteristics of pavement temperature and the temperature dependence of material modulus. With the basic design requirements of no fatigue cracking in the base layer and no excessive permanent deformation in the subgrade during the life cycle, the surface layer meets the functional and durability requirements, ensures that the pavement rut meets the checking requirements within the target years, combines the elastic layer system theory and the Monte Carlo method, and proposes a design method for an extra-large particle size long-life asphalt pavement based on the double checking standard of "fatigue / permanent deformation critical threshold + cumulative damage", improves the reliability of the pavement structure under the coupling action of environment-load, and realizes the long-life goal of the pavement structure.

[0053] The advantages of the additional aspects of the present invention will be partly given in the following description, partly will become obvious from the following description, or will be understood through the practice of the present invention. Description of the Drawings

[0054] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0055] Figure 1 It is a flowchart of the method for the first embodiment.

[0056] Figure 2 It is a flowchart of randomly generating a Monte Carlo calculation table by using the Monte Carlo method in the first embodiment.

[0057] Figure 3 is the axle load distribution diagram in the first embodiment. Among them Figure 3a It is the single-axle single-tire axle load distribution diagram in the first embodiment; Figure 3b It is the single-axle dual-tire axle load distribution diagram in the first embodiment; Figure 3c It is the dual-axle axle load distribution diagram in the first embodiment; Figure 3d It is the triple-axle axle load distribution diagram in the first embodiment.

[0058] Figure 4 It is the schematic diagram of the pavement structure temperature spectrum in the first embodiment.

[0059] Figure 5 It is the system structure diagram of the second embodiment. Detailed implementation mode

[0060] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0061] It should be noted that the terms used herein are only for describing specific implementation modes and are not intended to limit the exemplary implementation modes according to the present invention.

[0062] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0063] Embodiment 1

[0064] This embodiment discloses a design method for an asphalt pavement with super-large particle size and long service life;

[0065] As Figure 1 shown, a design method for an asphalt pavement with super-large particle size and long service life includes:

[0066] Step S1, obtaining the basic information, traffic parameters and environmental parameters of the pavement design project;

[0067] According to relevant design information, clarify the basic information of the pavement design project, including the project location area, highway grade, pavement design service life t1 and the number of years t2 from opening to the first rut repair, and the target reliability P m .

[0068] Through the investigation and analysis of traffic data, traffic parameter data are obtained. The traffic parameter data include traffic load levels. Specifically, by obtaining the two-way annual average daily traffic volume AADTT of large buses and trucks on the section of the road, the annual average growth rate γ of traffic volume, the direction coefficient DDF and the lane coefficient LDF, and determining the traffic load level according to the annual average daily traffic volume of large buses and trucks on the design lane; selecting the traffic axle load spectrum of the same grade in the same area according to the determined traffic load level.

[0069] The environmental parameters mainly include the pavement temperature spectrum. According to the annual measured temperature data of each structural layer of the LSAM-50 flexible base asphalt pavement, statistically analyze the distribution coefficient TD of the i-level pavement depth in the j-level temperature range ij , and construct the pavement temperature spectrum.

[0070] Step S2: Based on traffic parameters, preliminarily determine the thickness of the pavement structural layer and variation parameters that meet the requirements of rutting critical depth and fatigue critical thickness, and determine the Poisson's ratio of each structural layer material; preliminarily determine the subgrade modulus and variation parameters; determine the temperature distribution of each structural layer according to environmental parameters; determine the temperature-dependent model of the asphalt mixture dynamic modulus of each asphalt mixture structural layer of the super-large-size asphalt pavement, and determine the asphalt mixture modulus according to the temperature-dependent model.

[0071] Step S21: According to the traffic load level, preliminarily determine the pavement structural thickness combination that meets the requirements of rutting critical depth and fatigue critical thickness according to design experience, and determine the Poisson's ratio of each structural layer material. Considering the construction variability of the pavement structural thickness, define variation parameters, including the coefficient of variation and the coefficient of variation. When performing Monte Carlo analysis, the pavement thickness variation follows a normal distribution.

[0072] Step S22: The subgrade is one of the structural layers of the pavement structure, and the subgrade modulus is the elastic modulus of the subgrade. Usually, the resilient modulus of the subgrade top surface is adopted. By preliminarily determining the subgrade modulus and considering the construction variability of the subgrade modulus, define the coefficient of variation and the coefficient of variation. When performing Monte Carlo analysis, the subgrade modulus variation follows a normal distribution.

[0073] Step S23: Determine the temperature-dependent model of the asphalt mixture dynamic modulus of each structural layer of the super-large-size asphalt pavement; in this embodiment, temperature-dependent models for surface course asphalt mixture, binder course asphalt mixture, and LSAM-50 asphalt mixture are provided, as follows:

[0074]

[0075] In the formula, E 1 、E 2 、E 3 are the dynamic modulus of the surface course asphalt mixture, the dynamic modulus of the binder course asphalt mixture, and the dynamic modulus of the LSAM-50 asphalt mixture, respectively; T is the temperature; f is the loading frequency.

[0076] Step S3: Combine the parameters preliminarily determined in Step S2, and use the Monte Carlo method to randomly generate pavement structural layer thickness, temperature distribution, material modulus, and traffic load parameters, and perform pavement mechanical response calculations; among them, the material modulus refers to the modulus of each structural layer. In this embodiment, the subgrade modulus is randomly generated by Monte Carlo; the traffic load parameters include the axle type and axle weight, and the axle types include single-axle single-tire, single-axle dual-tire, tandem axle, and triple axle.

[0077] Combine Figure 2 , the above process includes:

[0078] Step S31: Set the Monte Carlo cycle number N s, according to the preliminary pavement structural layer thickness and variation parameters, randomly generate the thickness of each structural layer;

[0079] Step S32, according to the thickness combination of each structural layer and the pavement temperature spectrum, randomly generate the temperature of each structural layer;

[0080] Step S33, according to the temperature of each structural layer and the temperature dependence model of the dynamic modulus of asphalt mixture, generate the dynamic modulus of each asphalt mixture layer;

[0081] Step S34, according to the preliminary subgrade modulus and its variability parameters, randomly generate the subgrade modulus;

[0082] Step S35, according to the traffic axle load spectrum, randomly generate the axle type and axle weight, and calculate the pavement mechanical response based on the elastic layer system theory;

[0083] Step S36, adopt the elastic layer system theory to calculate the bottom tensile strain of the flexible base and the vertical compressive strain of the subgrade top surface respectively, which is convenient for subsequent pavement structure reliability checking and fatigue damage checking; the elastic layer system theory abstracts the multi-layer pavement structure into a multi-layer semi-infinite elastic structure, equivalent the traffic load to a circular uniform / non-uniform load, establish a mechanical model of a multi-layer elastic half-space body, and obtain the pavement mechanical response through solving the mechanical boundary value problem.

[0084] Step S37, repeatedly sample N s times according to the above method, construct a Monte Carlo calculation table, and perform pavement reliability checking and fatigue damage checking based on the constructed Monte Carlo calculation table.

[0085] Step S4, perform reliability and fatigue damage checking of flexible base asphalt pavement according to fatigue limit theory and cumulative damage theory respectively; perform pavement rutting checking according to the rutting prediction model of flexible base asphalt pavement.

[0086] The process of performing reliability checking of LSAM-50 flexible base asphalt pavement according to fatigue limit theory in Step S41 is as follows:

[0087] According to the fatigue limit theory of long-life asphalt pavement design, when the bottom flexural tensile strain of the asphalt mixture layer is less than the fatigue limit, the pavement will not have fatigue cracking originating from the bottom of the asphalt layer. Set the critical threshold of the bottom tensile strain of the LSAM-50 flexible base to 70 με, and the critical threshold of the vertical compressive strain of the subgrade top surface to 200 με;

[0088] Statistically count the number of combinations n of mechanical responses lower than the critical threshold in the Monte Carlo calculation table s , according to the Monte Carlo cycle number N s , calculate the actual pavement reliability P s, when it is greater than or equal to the target reliability P m , the pavement design meets the reliability requirements. Otherwise, by adjusting the pavement structure plan, recheck until the requirements are met. The actual reliability of the pavement is shown in the following formula:

[0089]

[0090] In the formula: P s is the actual reliability (%); P m is the target reliability (%); N s is the number of Monte Carlo cycles (times); n s is the number of combinations where the mechanical response is lower than the critical threshold in the Monte Carlo cycle.

[0091] The LSAM-50 flexible base asphalt pavement is mainly for high-grade highways. For expressways, the target reliability P m is set to 95%; for first-class and second-class highways, the target reliability P m are both set to 90%.

[0092] Step S42, the process of checking the fatigue damage of the LSAM-50 flexible base according to the cumulative damage theory is as follows:

[0093] (1) Based on Miner's linear damage accumulation principle. Specifically, Miner's linear damage accumulation principle assumes that the fatigue damage of materials at different stress levels is linearly additive. Specifically, if a material will experience fatigue failure after a certain number of cycles at a certain stress level, then each cycle will cause a certain proportion of damage to the material. These damages can be linearly accumulated, and when the total damage reaches or exceeds the threshold, the material is considered about to fail. Calculate the single damage D i = 1 / N fi of the mechanical response of each input parameter combination in the Monte Carlo calculation table exceeding the damage critical threshold based on the LSAM-50 fatigue equation, and thus calculate the cumulative damage ∑D s of one Monte Carlo cycle (N s groups of random parameters), and then determine the average damage D i of each group of parameters a = ∑D i / N s . The formula for calculating the fatigue cracking life of the flexible base is as follows:

[0094]

[0095] In the formula: N f is the fatigue cracking life (times) of the LSAM-50 flexible base; ε is the bottom tensile strain of the LSAM-50 flexible base determined by each Monte Carlo cycle calculation (10 -6); T is the pavement temperature randomly generated for each Monte Carlo cycle (°C); k c is the on-site comprehensive correction factor.

[0096] (2) Combine the average damage D a (per axle load application) determined by the Monte Carlo cycle, and calculate the number of axle load applications N reaching the fatigue failure limit value e1 , where the fatigue failure limit value is 0.1, as shown in the following formula:

[0097]

[0098] (3) Calculate the initial annual average daily axle load applications N1 on the design lane;

[0099]

[0100] In the formula: N1 is the initial annual average daily axle load applications on the design lane (times); AADTT is the two-way annual average daily traffic volume of vehicles with more than 6 wheels on 2 axles (vehicles / day); DDF is the direction coefficient; LDF is the lane coefficient; VCDF m is the type distribution coefficient of m types of vehicles; NAPT mi is the average number of axles of the i-th axle type among m types of vehicles; i are single-axle single-tire, single-axle dual-tire, double-axle, and triple-axle respectively; m are vehicles of type 2 - 11.

[0101] (4) Calculate the expected service life t of the pavement. The expected service life of the LSAM-50 flexible base should be greater than or equal to the pavement design service life t1. Otherwise, the pavement structure plan should be adjusted and recalculated until the requirements are met.

[0102]

[0103] In the formula: t is the expected service life of the pavement (years); t1 is the pavement design service life (years); N1 is the initial annual average daily axle load applications on the design lane (times); N e1 is the number of axle load applications reaching the fatigue failure limit value (times); γ is the annual average growth rate of traffic volume within the design service life.

[0104] Step S43, conduct pavement rutting check according to the rutting prediction model of flexible base asphalt pavement. The cumulative rutting deformation of the asphalt mixture layer within the first rutting repair service life after opening to traffic should be lower than the allowable rutting deformation (10 mm). The check process is as follows:

[0105] (1) Calculate the cumulative equivalent axle load applications N e2 within the first rutting repair service life t2 after opening to traffic according to relevant design specifications;

[0106] (2) Determine the distribution frequency of each temperature range throughout the year based on the temperature spectrum at each depth layer of the road surface. Take the midpoint of the temperature range as the representative value of the range as the temperature of each layer, and calculate the product of the distribution frequency of each temperature range and the cumulative equivalent axle load repetitions within the first rut repair period. This is the cumulative number of equivalent design axle load applications N for the i-th layer and the j-th temperature range within the first rut repair period. e2ij ;

[0107] N e2ij = N e2 × TD ij

[0108] (3) Calculate the road surface rut based on the rut prediction model. The rut prediction model is as follows:

[0109]

[0110] In the formula: R a is the cumulative rut deformation of the mixture layer (mm); R aij is the rut deformation of each layer of the asphalt mixture layer in different temperature ranges (mm); k c is the on-site comprehensive correction coefficient; α N , α T , α P are the action number index, temperature index, and load index respectively; N e2ij is the cumulative number of equivalent design axle load applications for the i-th layer and the m-th temperature range within the first rut repair period; T i is the temperature of each layer (°C), taking the midpoint of each temperature range of the temperature spectrum; p i is the load of each layer (MPa), the vertical compressive stress on the top surface of each layer at the standard temperature.

[0111] Furthermore, in this embodiment, for seasonal frozen soil areas, the low-temperature cracking and anti-freezing thickness of the road surface are also checked according to the specifications, and the acceptance deflection value of the designed road surface structure is calculated for the inspection and acceptance of the road surface deflection value during the completion (or handover) of the road.

[0112] The method described in this embodiment is used for the design of a certain expressway. Specifically:

[0113] I. Basic information parameters:

[0114] 1) Project information. The design service life of this expressway is 50 years, the period from opening to the first rut repair is 15 years, and the target reliability is 95%.

[0115] 2) Traffic parameters. According to traffic survey and analysis, the traffic volume of large buses and trucks at the section is 12,000 vehicles per day, the annual average growth rate of traffic volume is 2%, the direction coefficient is 0.56, and the lane coefficient is 0.54. The annual average daily traffic volume of large buses and trucks on the design lane is 3,629. According to Table 1, the traffic grade belongs to extremely heavy traffic.

[0116] Table 1 Design traffic load grade

[0117]

[0118] Based on the above measured traffic data of the expressway, a typical axle load spectrum for extremely heavy traffic is constructed. The basic information is shown in Table 2.

[0119] Table 2 Basic information

[0120] Item Content Section Name The Third and Fourth Lanes in the Downward Direction of a Certain Highway Section in Shandong Province Number of Lanes Eight Lanes in Both Directions Traffic Load Level Extra-Heavy Traffic

[0121] Obtained according to the vehicle type distribution coefficient and the average number of axles of each type of vehicle. The axle load distribution of each axle type (axle load spectrum) is as Figures 3a - 3d .

[0122] When performing mechanical calculations, the elastic layer system theory is used to calculate the mechanical response. The tire loads of different axle types are equivalent to circular uniform loads. When performing Monte Carlo mechanical calculations, the axle type and axle weight are randomly generated according to the axle load spectrum, and the tire contact pressure is calculated as the mechanical calculation load parameter input.

[0123] Combined with Table 3-4, the conversion coefficients of equivalent design axle loads for various types of vehicles are determined according to Level 3 of the "Design Specification for Highway Asphalt Pavements" (JTG D50). The cumulative number of equivalent design axle load applications N e2 is 53,305,661.

[0124] Table 3 Proportion of non-full-load vehicles and full-load vehicles (%)

[0125] Vehicle Type Type 2 Type 3 Type 4 Type 5 Type 6 Type 7 Type 8 Type 9 Type 10 Type 11 Proportion of Unloaded Vehicles 85 90 65 75 55 70 45 60 55 65 Proportion of Loaded Vehicles 15 10 35 25 45 30 55 40 45 35

[0126] Table 4 Conversion coefficients of equivalent design axle loads for non-full-load vehicles and full-load vehicles

[0127] Vehicle Type Type 2 Type 3 Type 4 Type 5 Type 6 Type 7 Type 8 Type 9 Type 10 Type 11 Unloaded Vehicles 0.8 0.4 0.7 0.6 1.3 1.4 1.4 1.5 2.4 1.5 Loaded Vehicles 2.8 4.1 4.2 6.3 7.9 6 6.7 5.1 7 12.1

[0128] 3) Environmental parameters (temperature spectrum)

[0129] The typical structure of the ultra-large particle size asphalt pavement is as Figure 4 shown. Temperature sensors are arranged in the middle of each structural layer to collect the annual temperature data of the pavement structure and construct the pavement temperature spectrum.

[0130] II. Preliminary parameters

[0131] 1) Preliminary proposed pavement thickness combination

[0132] Considering the construction variability of the thickness of each structural layer, taking the design value of the thickness of each structural layer as the mean value of thickness variation, the preliminary proposed pavement thickness combination is shown in Table 5.

[0133] Table 5 Preliminary proposed pavement thickness combination

[0134]

[0135] 2) Preliminary proposed subgrade modulus

[0136] Considering the construction variability of the subgrade modulus, taking the design value of the subgrade modulus as the mean value of modulus variation, the preliminary proposed subgrade modulus is shown in Table 6.

[0137] Table 6 Preliminary proposed subgrade modulus

[0138]

[0139] 3) Temperature-dependent model of dynamic modulus of asphalt mixture

[0140] Based on the measured dynamic modulus of asphalt mixture in the laboratory, a temperature-dependent model of the dynamic modulus of each structural layer of the pavement is established.

[0141] Surface layer asphalt mixture:

[0142] Binder course asphalt mixture:

[0143] LSAM-50 asphalt mixture:

[0144] Where: E 1 、E 2 、E 3 are the dynamic modulus of surface layer asphalt mixture, the dynamic modulus of binder course asphalt mixture, and the dynamic modulus of LSAM-50 asphalt mixture respectively, T is the temperature (°C); f is the loading frequency (Hz).

[0145] III. Monte Carlo calculation

[0146] Set 5000 Monte Carlo cycles. According to the pavement structure thickness variation parameters, temperature spectrum, temperature-dependent model of dynamic modulus of asphalt mixture, subgrade modulus variation parameters, and axle load spectrum, randomly generate pavement structure combined thickness, temperature, modulus, and axle load combinations, and calculate the horizontal tensile strain at the bottom of the upper base of LSAM-50, the horizontal tensile strain at the bottom of the lower base, and the vertical compressive strain at the top of the subgrade. Some results are shown in Table 7:

[0147] Table 7 Monte Carlo mechanical calculation results

[0148]

[0149] IV. Checking Calculation

[0150] 1) Reliability Checking Calculation

[0151] According to the reliability design requirements of the large-size aggregate asphalt pavement, to control the fatigue cracking at the bottom of the LSAM-50 base course, the bottom tensile strain should not exceed 70 με (tensile strain is positive); to control the vertical permanent deformation at the top of the subgrade, the vertical compressive strain at the top should not exceed -200 με (compressive strain is negative). The Monte Carlo method is used for the reliability checking calculation of the pavement structure. When the probability that the tensile strain at the bottom of the LSAM-50 base course is lower than the critical threshold exceeds 95%, the fatigue cracking checking calculation is qualified; when the probability that the vertical compressive strain at the top of the subgrade is lower than the critical threshold exceeds 95%, the checking calculation of the vertical permanent deformation at the top of the subgrade is qualified, otherwise the checking calculation is unqualified.

[0152] According to the Monte Carlo calculation table, the reliability checking calculation of the pavement structure is carried out, and the results are shown in Table 8.

[0153] Table 8 Reliability Checking Calculation of Pavement Structure

[0154] Layer Position Verification Index Threshold Value / με Target Reliability / % Actual Reliability / % Verification Result 3 Bottom Layer Tensile Strain 70 95 100 √ 4 Bottom Layer Tensile Strain 70 95 99.98 √ 5 Top Layer Compressive Strain -200 95 99.98 √

[0155] According to the checking calculation results, the proposed pavement structure and materials meet the requirements of the reliability checking calculation.

[0156] 2) Fatigue Damage Checking Calculation

[0157] According to the anti-fatigue damage design requirements of the large-size aggregate asphalt pavement, the cumulative fatigue damage threshold at the bottom of the LSAM-50 base course is 0.1. When the cumulative fatigue damage is less than this limit value, the structural layer will not have fatigue failure.

[0158] The project belongs to an expressway, and the target reliability is 95%. The on-site comprehensive correction coefficient k c is 6.5×10 2 . According to the Monte Carlo calculation results, based on the LSAM-50 fatigue equation (95% reliability), the Miner damage rule is used for the pavement cumulative damage analysis, and the results are shown in Table 9.

[0159] Table 9 Fatigue Damage Checking Calculation of Pavement Structure

[0160] Layer Position Verification Index Damage Threshold Value Average Damage / Axle Load Theoretical Service Life / Year Verification Result 3 Cumulative Fatigue Damage at the Bottom Layer 0.1 <![CDATA[3.9141×10 -11 > 139 √ 4 Cumulative Fatigue Damage at the Bottom Layer 0.1 <![CDATA[3.0591×10 -10 > 53 √

[0161] According to the checking calculation results, the proposed pavement structure and materials meet the requirements of the LSAM-50 fatigue damage checking calculation.

[0162] 3) Pavement Rutting Checking Calculation

[0163] According to the "Code for Design of Highway Asphalt Pavements" (JTG D50), calculate the cumulative equivalent design axle load applications N for rutting verification of the corresponding pavement during the period from the opening to traffic to the first rut repair e3 is 53,305,661. The on-site comprehensive correction factor k c is 1.1×10 -6 .

[0164] The asphalt mixture layers of the super-large particle size asphalt pavement are stratified as follows:

[0165] The surface layer is stratified at 20 mm; the second asphalt mixture layer is stratified at 20 mm; the third asphalt mixture layer is stratified at 100 mm; the fourth asphalt mixture layer is taken as one layer.

[0166] Based on the temperature spectrum at each depth of the pavement layers, determine the distribution frequency of each temperature range throughout the year, and calculate the product of the distribution frequency of each temperature range and the cumulative equivalent axle load during the first rut repair period to obtain the cumulative equivalent design axle load applications N for the i-th layer and the j-th temperature range during the first rut repair period e2ij .

[0167] According to the elastic layer theory, calculate the vertical compressive stress at the top surface of each layer of the asphalt mixture layer under the action of the standard axle load (BZZ-100), as shown in Table 10.

[0168] Table 10 Vertical Compressive Stress at the Top Surface of Each Layer of the Asphalt Mixture Layer

[0169]

[0170] According to the rut prediction model of the super-large particle size asphalt pavement, calculate and accumulate the permanent deformation of each layer of the asphalt mixture layer in different temperature ranges, and obtain that the pavement rut is 8.2 mm. The requirement for the permanent deformation of the asphalt mixture layer is 10 mm, and the proposed pavement structure meets the requirement for the permanent deformation of the asphalt mixture layer.

[0171] 4) Low-temperature cracking verification

[0172] According to the climatic conditions, the low-temperature design temperature T in the area is -10°C. The subgrade type parameter b = 5 (sand), the stiffness modulus St of the surface layer asphalt in the bending beam rheological test under the test temperature condition of adding 10°C to the pavement low-temperature design temperature is 200 MPa, and the thickness h a of the asphalt binder material layer is 500 mm. Calculate the low-temperature cracking index CI = -3.892. The requirement for the low-temperature cracking index is 3, and the proposed pavement structure and materials meet the requirements for low-temperature cracking.

[0173] 5) Frost protection thickness verification

[0174] According to the survey data, the subgrade in the seasonal frozen soil area of the region is in a medium-moist or moist state, and the maximum annual frozen depth Z of the ground d = 1000 mm. The thermal physical property coefficients of each structural layer material are shown in Table 11:

[0175] Table 11 Thermal physical property coefficients of each structural layer material

[0176] Layer Position Material Thickness / mm Thermophysical Coefficient of Material 1 SMA - 13 40 1.35 2 AC - 20 60 1.35 3 LSAM - 50 200 1.35 4 LSAM - 50 200 1.35 5 Subgrade (Silty Sand) 500 (within the range of the maximum ground frost depth) 1.20

[0177] By calculating the weighted average according to the thickness, the thermal physical property coefficient a = 1.275 of each layer material of the subgrade and pavement within the frozen depth of the ground, the subgrade moisture coefficient b = 0.95, and the subgrade cross-section form coefficient c = 0.9. Calculate the maximum annual frozen depth Z of the highway max = 1090 mm. According to the maximum annual frozen depth of the highway and the dry-wet type of the subgrade, determine the minimum anti-freezing thickness of the asphalt pavement structure as 400 mm from Table B.6.2 of Code JTG D50. The total thickness of the pavement structure is 500 mm, and the proposed pavement structure meets the anti-freezing thickness requirement.

[0178] 6) Summary of verification results

[0179] The summary of each verification result is shown in Table 12:

[0180] Table 12 Summary of pavement structure verification results

[0181] Verification Content Calculated Value Required Value Whether It Meets the Requirements Structural Reliability (Tensile Strain at the Bottom of the LSAM - 50 Base Course) 99.98％ 95％ Yes Structural Reliability (Vertical Compressive Strain at the Top of the Subgrade) 99.98％ 95％ Yes Fatigue Life of the LSAM - 50 Base Course 53 years 50 years Yes Pavement Rutting 8.2 mm 10 mm Yes Low - Temperature Cracking Index -3.892 3 Yes Anti - Frost Thickness 500 mm (total thickness of the pavement structure) 400 mm Yes

[0182] In summary, the proposed pavement structure and materials can meet the requirements of pavement structure verification.

[0183] Example 2

[0184] This example discloses a design system for super-large particle size long-life asphalt pavement;

[0185] As Figure 5 shown, a design system for super-large particle size long-life asphalt pavement includes:

[0186] A design system for super-large particle size long-life asphalt pavement includes:

[0187] Basic parameter information acquisition module, configured to: acquire the basic information, traffic parameters and environmental parameters of the pavement design project;

[0188] Pavement parameter acquisition module, configured to: preliminarily determine the thickness of the pavement structural layer and variation parameters that meet the requirements of rutting critical depth and fatigue critical thickness according to traffic parameters, and determine the Poisson's ratio of each structural layer material; determine the temperature distribution of each structural layer according to environmental parameters; determine the dynamic modulus temperature dependence model of the asphalt mixture of each structural layer of the super-large particle size asphalt pavement, and determine the material modulus according to the temperature dependence model;

[0189] A Monte Carlo calculation module, configured to: randomly generate pavement structural layer thickness, temperature distribution, material modulus, and traffic load parameters by using the Monte Carlo method, and perform pavement mechanical response calculations;

[0190] A pavement reliability verification module, configured to: perform reliability and fatigue damage verification of flexible base asphalt pavements respectively according to the fatigue limit theory and the cumulative damage theory; perform pavement rutting verification according to the flexible base asphalt pavement rutting prediction model.

[0191] Example Three

[0192] The purpose of this example is to provide a computer-readable storage medium.

[0193] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps in a super-large particle size long-life asphalt pavement design method as described in Example 1.

[0194] Example Four

[0195] The purpose of this example is to provide an electronic device.

[0196] An electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in a super-large particle size long-life asphalt pavement design method as described in Example 1.

[0197] The steps involved in the devices in the above Examples Two, Three, and Four correspond to those in Method Example One. For specific implementation manners, reference may be made to the relevant description part of Example One. The term "computer-readable storage medium" should be understood to include a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0198] Those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computer device. Optionally, they can be implemented by program codes executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0199] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, they are not limitations on the protection scope of the present invention. Those skilled in the art should understand that various modifications or deformations that can be made without creative efforts on the basis of the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A method for designing an ultra-large particle size and long-life asphalt pavement, characterized in that: include: Obtain basic information, traffic parameters and environmental parameters of pavement design projects; Preliminarily estimate the pavement structure layer thickness and variation parameters that meet the requirements of critical rutting depth and critical fatigue thickness based on traffic parameters, and determine the Poisson's ratio of each structural layer material; preliminarily estimate the roadbed modulus and variation parameters; determine the temperature distribution of each structural layer based on environmental parameters; determine the temperature dependence model of the asphalt mixture dynamic modulus of each asphalt mixture structural layer of the super-large particle size asphalt pavement, and determine the asphalt mixture modulus based on the temperature dependence model; The Monte Carlo method is used to randomly generate the pavement structure layer thickness, temperature distribution, material modulus and traffic load parameters, and the pavement mechanical response is calculated; The reliability and fatigue damage of flexible base asphalt pavement are verified according to fatigue limit theory and cumulative damage theory respectively; the pavement rutting is verified according to the rutting prediction model of flexible base asphalt pavement.

2. A method for designing an ultra-large particle size and long-life asphalt pavement as claimed in claim 1, characterized in that: The basic information of the pavement design project includes the project location, highway grade, pavement design service life and the period from opening to the first rutting repair, and target reliability; The traffic parameters include traffic load level and traffic axle load spectrum. The traffic load level is determined by obtaining the annual average daily traffic volume of large buses and trucks in both directions of the road section, the annual average growth rate of traffic volume, the direction coefficient and the lane coefficient, and according to the annual average daily traffic volume of large buses and trucks in the design lane; and the traffic axle load spectrum of the same level in the same area is selected according to the traffic load level; The environmental parameters include a road surface temperature spectrum, which is based on the actual temperature data of each structural layer of the flexible base asphalt pavement throughout the year, and statistically analyzes the distribution coefficient of the road surface depth temperature range.

3. The method for designing an ultra-large particle size long-life asphalt pavement according to claim 1, wherein the process of randomly generating the pavement structure layer thickness, temperature distribution, material modulus and traffic load parameters using the Monte Carlo method and calculating the pavement mechanical response is as follows: Set the number of Monte Carlo cycles, and randomly generate the thickness of each structural layer according to the thickness of the initial pavement structure layer and the variation parameters; According to the thickness combination of each structural layer and the pavement temperature spectrum, the temperature of each structural layer is randomly generated; Generate the dynamic modulus of each asphalt mixture layer according to the temperature of each structural layer and the temperature dependence model of the dynamic modulus of the asphalt mixture; According to the preliminary proposed subgrade modulus and its variability parameters, the subgrade modulus is randomly generated; Randomly generate axle type and axle weight according to traffic axle load spectrum; The elastic layered system theory is used to calculate the tensile strain at the bottom of the flexible base and the vertical compressive strain at the top of the roadbed. Repeat the sampling times according to the above method to construct the Monte Carlo calculation table.

4. The method for designing an ultra-large particle size long-life asphalt pavement according to claim 1, wherein the process of performing reliability calculation of a flexible base asphalt pavement according to fatigue limit theory is as follows: According to the fatigue limit theory of long-life asphalt pavement design, when the bending and tensile strain at the bottom of the asphalt mixture layer is less than the fatigue limit, the pavement will not experience fatigue cracking originating from the bottom of the asphalt layer. The number of combinations whose mechanical responses are below the critical threshold in the Monte Carlo calculation table is counted, and the actual reliability of the pavement is calculated based on the number of Monte Carlo cycles. When it is greater than or equal to the target reliability P m When , the pavement design meets the reliability requirements; otherwise, adjust the pavement structure plan and recalculate until it meets the requirements. The actual reliability of the pavement is as follows: Where: P s is the actual reliability; P m is the target reliability; N s is the number of Monte Carlo cycles; n s is the number of combinations in the Monte Carlo cycle where the mechanical response is below the critical threshold.

5. According to the design method of the ultra-large particle size long-life asphalt pavement as claimed in claim 1, the process of calculating the fatigue damage of the flexible base asphalt pavement according to the cumulative damage theory is as follows: According to Miner's linear damage accumulation principle, the single damage D of each input parameter combination exceeding the critical damage threshold is calculated based on the fatigue equation. i =1 / N fi , and the cumulative damage ∑D of a Monte Carlo cycle is calculated i , and then determine the average damage D of each group of parameters a =∑D i / N s ;in, The calculation formula for the fatigue cracking life of the flexible base layer is: Where: N f The fatigue cracking life of LSAM-50 flexible base layer; ε is the tensile strain at the bottom of the flexible base layer determined by each Monte Carlo cycle; T is the pavement temperature randomly generated by each Monte Carlo cycle; k c is the comprehensive correction factor on site; Combined with the average damage determined by the Monte Carlo cycle, the number of axle load actions that reaches the fatigue damage limit value, the average daily axle load action times of the design lanes in the initial year, and the expected service life of the pavement are calculated.

6. The method for designing an ultra-large particle size long-life asphalt pavement according to claim 1, wherein the process of calculating the pavement rutting based on the flexible base asphalt pavement rutting prediction model is as follows: Calculate the cumulative equivalent axle trips within the first rutting maintenance period after the opening of the road; The distribution frequency of each temperature interval throughout the year is determined based on the temperature spectrum at each layer depth of the pavement. The midpoint of the temperature interval is taken as the representative value of the interval as the temperature of each layer, and the cumulative number of equivalent design axle load actions within the first rutting maintenance period is calculated. The road rutting is calculated based on the rutting prediction model. The rutting prediction model is as follows: Where: R a is the accumulated rutting deformation of the asphalt mixture layer; R aij is the rutting deformation of each layer of the asphalt mixture layer in different temperature ranges; k c is the on-site comprehensive correction coefficient; α N , α T , α P are the action number index, temperature index and load index respectively; N e2ij T is the cumulative number of equivalent design axle load actions in the mth temperature interval of the ith layer within the first rutting maintenance period; i is the temperature of each layer, taking the midpoint of each temperature interval of the temperature spectrum; p i is the load of each layer and the vertical compressive stress on the top surface of each layer at standard temperature.

7. The method for designing an ultra-large particle size and long-life asphalt pavement according to claim 1, further comprising: For seasonally frozen soil areas, the low-temperature cracking and anti-freeze thickness of the pavement are verified, and the acceptance deflection value of the designed pavement structure is calculated, and the deflection value of the road surface is tested and accepted when the pavement is completed.

8. A super-large particle size and long-life asphalt pavement design system, characterized by: include: The basic parameter information acquisition module is configured to: acquire basic information, traffic parameters and environmental parameters of the pavement design project; The pavement parameter acquisition module is configured to: preliminarily estimate the pavement structure layer thickness and variation parameters that meet the requirements of rutting critical depth and fatigue critical thickness according to traffic parameters, and determine the Poisson's ratio of each structural layer material; preliminarily estimate the roadbed modulus and variation parameters; determine the temperature distribution of each structural layer according to environmental parameters; determine the temperature dependence model of the asphalt mixture dynamic modulus of each asphalt mixture structural layer of the super-large particle size asphalt pavement, and determine the asphalt mixture modulus according to the temperature dependence model; The Monte Carlo calculation module is configured to: randomly generate the pavement structure layer thickness, temperature distribution, material modulus and traffic load parameters using the Monte Carlo method, and perform pavement mechanical response calculation; The pavement reliability calculation module is configured to: perform reliability and fatigue damage calculations on flexible base asphalt pavement according to fatigue limit theory and cumulative damage theory respectively; and perform pavement rutting calculations according to a rutting prediction model for flexible base asphalt pavement.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for designing an ultra-large particle size and long-life asphalt pavement as described in any one of claims 1-7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for designing an ultra-large particle size and long-life asphalt pavement as described in any one of claims 1-7 are implemented.

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