Fatigue reliability evaluation method for permanent pavement structure based on bounded distribution of parameters

Abstract

The invention discloses a permanent pavement structure fatigue reliability evaluation method based on parameter bounded distribution, comprising: 1), obtaining variability statistical characteristics of pavement design input parameters; 2), generating random numbers that conform to a classical distribution model according to the obtained variability statistical characteristics of pavement design input parameters; 3), judging whether the generated random numbers conform to a specified interval range, screening random numbers that conform to the range, until a parameter truncation distribution model that conforms to the statistical characteristics specified in step 1) is obtained; 4), establishing a limit state equation Z of pavement structure fatigue index and cumulative equivalent axle times; 5), judging the state of a structural function function according to steps 3) and 4); 6), setting a number of cycles N, repeating steps 2)-5), counting the number of times p of Zi>0, and the number of times q of Zi<0; 7), based on step 6), drawing a reliability convergence curve of p / (p+q), and judging the reliability convergence; 8), obtaining the reliability of the pavement structure.

Description

Technical Field

[0001] The invention relates to the field of fatigue reliability evaluation of pavement structures, and in particular to a fatigue reliability evaluation method of permanent pavement structures based on bounded parameter distribution. Background Art

[0002] Since the mechanical response expression of the pavement structure contains an infinite Bessel integral function that is difficult to be expressed and to obtain partial derivatives, the calculation of the pavement structure reliability is usually based on numerical simulations such as the Monte Carlo method. Before the implementation of the "Highway Asphalt Pavement Design Specification" (JTG D50-2017) in September 2017, the pavement structure verification and reliability analysis were mainly carried out around the bending settlement and layer bottom tensile stress indicators. However, the "Highway Asphalt Pavement Design Specification" (JTG D50-2017) in September 2017 abolished the verification indicators such as bending settlement, and modified the pavement structure verification indicators, including asphalt mixture layer fatigue, inorganic binder layer fatigue and allowable compressive strain on the top surface of the roadbed. Since the implementation of the "Highway Asphalt Pavement Design Specification" (JTG D50-2017): Yang Congli designed the asphalt pavement structure reliability software framework based on the base fatigue index. The framework is still being improved and cannot fully calculate the reliability of the pavement structure. Fu Yu selected the two indicators of asphalt pavement base fatigue and permanent deformation to develop an asphalt pavement reliability calculation program. Existing studies have mainly analyzed the reliability of individual indicators of asphalt pavement, and mainly centered on reliability indicators such as deflection and bottom tensile stress. There are fewer studies on pavement fatigue reliability under current specifications. In existing reliability calculation models, it is often assumed that random variables such as pavement traffic, structure, and materials obey unbounded theoretical distributions such as normal distribution, without considering the actual boundedness of random variables in actual production and manufacturing, which is quite different from the error analysis of actual projects. Due to the fact that some random variables appear infinite or negative, their reliability calculations often do not match the actual project.

[0003] In addition, the mechanical behavior characteristics of permanent asphalt pavement structures are different from those of ordinary asphalt pavement structures. The maximum tensile strain position of the asphalt mixture layer is usually at the upper part of the surface layer. In existing studies, the reliability evaluation methods and procedures for the surface layer, base layer and subgrade of permanent asphalt pavement structures have not been reported.

[0004] Therefore, it is necessary to design a computational analysis method that can be used to accurately evaluate the influence of variability of engineering design indicators before the construction of permanent asphalt pavement structures and accurately evaluate fatigue reliability after completion. Summary of the invention

[0005] The purpose of the present invention is to propose a reliability evaluation method for fatigue of surface layer and base layer and compressive strain of roadbed based on the construction variability and range of permanent pavement structure design parameters, including generating random numbers with specified distribution and range, and sensitivity analysis of various factors affecting fatigue reliability, so as to provide support for the evaluation of the impact of variability of engineering design indicators before construction of permanent asphalt pavement structure and the fatigue reliability evaluation after completion.

[0006] The above-mentioned invention object is achieved through the technical solution described below.

[0007] The present invention discloses a method for evaluating the fatigue reliability of a permanent pavement structure based on bounded parameter distribution, which is characterized by comprising the following steps:

[0008] Step 1), obtaining the variability statistical characteristics of the pavement structure design input parameters, including: mean, variance, distribution type, upper limit and lower limit;

[0009] Step 2), based on the variability statistical characteristics of the pavement structure design input parameters obtained in step 1), combined with the classic random parameter distribution model in mathematical statistics, combined with the Monte Carlo sampling principle, generate random numbers that conform to the classic random parameter distribution model;

[0010] Step 3), according to the variability statistical characteristics of the upper and lower limits of the pavement structure design input parameters in step 1), a random number that meets the specified interval range is generated based on the probability equivalence principle and the inverse transformation method. If the random number does not meet the interval range, a random number is regenerated until a random number that meets the specified interval is generated, thereby obtaining a parameter truncation distribution model that meets the statistical characteristics of the distribution type, upper and lower limits specified in step 1);

[0011] Step 4), based on the pavement structure design parameters and traffic volume level in step 1), the limit state equations Zi = Ri-Si (i = 1, 2, 3) of the fatigue calculation index Ri (i = 1, 2, 3) of the pavement structure surface layer, base layer and subgrade and the cumulative equivalent axle number Si (i = 1, 2, 3) of the traffic volume are established respectively;

[0012] Step 5), judging the state of the structure function function Zi according to steps 3) and 4), if Zi ≥ 0, the structure is judged to be reliable, and if Zi < 0, the structure is judged to be unreliable;

[0013] Step 6), set the number of cycles N, repeat steps 2) to 5), count the number of times Zi ≥ 0 P, and the number of times Zi < 0 q;

[0014] Step 7), based on step 6), draw a reliability convergence curve of p / (p+q), where N=p+q, and judge the convergence of the reliability calculation process by the drawn reliability convergence curve. If the reliability convergence curve fluctuates greatly, it is necessary to adjust the number of cycles N until the reliability convergence curve is smooth and stable;

[0015] Step 8), based on step 6) and step 7), the last calculated value p / N of the number of cycles N is used as the reliability fi (i = 1, 2, 3) of each layer of the pavement structure, and the last calculated value q / N of the number of cycles N is used as the failure probability p of each layer of the pavement structure fi (i = 1, 2, 3), where the relationship between the reliability of the pavement structure surface layer, base layer and roadbed and the failure probability is fi = 1-p fi =1-q / N (i=1, 2, 3), then the reliability of the pavement structure

[0016] Furthermore, in step 1), for the permanent pavement structure after construction, pavement design input parameters of the entire line or a representative section of the pavement structure are collected; for the permanent pavement structure before construction, pavement design input parameters are formulated; the pavement design input parameters are obtained based on pavement structure design parameters and traffic parameters, wherein the pavement structure design parameters include pavement structure layer thickness and modulus, and the traffic parameters include traffic load level and traffic volume.

[0017] Furthermore, in steps 2) and 3), the pavement structure limit state function Zi(x) is an n-dimensional random variable x=x1,x2....,x n , its failure region is F = {x:Zi(x)≤0}, if x i (i=1,...,n) are independent and have a mean of μ i The standard deviation is σ i The truncated normal and lognormal distributions of are, a and b are the lower and upper truncated bounds of the random variable, respectively. The truncated distribution x based on the probability equivalence principle i The probability density function of and the cumulative distribution function As shown in the following formulas (1) and (2):

[0018]

[0019]

[0020] is the regularization constant, so that x i The cumulative distribution function of The cumulative probability in the interval [a, b] is 1; where f, F, They are random variables with mean μ i , standard deviation is σ i Probability density function, cumulative distribution function, mean, and variance of the parent distribution.

[0021] Furthermore, in step 3), the inverse transformation method determines the generation of random numbers of truncated distribution with normal distribution, and d is set as a random number of 0-1 that obeys uniform distribution (0≤d≤1), then:

[0022]

[0023]

[0024]

[0025] Until (a≤x≤b)

[0026] Furthermore, the pavement structure includes at least a surface layer, a base layer and a roadbed.

[0027] Furthermore, in step 4), the limit state equations of the structural fatigue index and the cumulative equivalent axle times of the surface layer, base layer and roadbed are established respectively, and the limit state equations of the surface layer, base layer and roadbed are: Z1, Z2, Z3;

[0028] When considering the structural fatigue of the surface layer, the limit state function is:

[0029]

[0030] In the formula, β is the target reliability index; K a Adjustment coefficient for seasonally frozen areas; K b Fatigue loading mode factor; E a Dynamic compression modulus of asphalt mixture at 20°C (MPa); asphalt saturation of VFA asphalt mixture (%); K T1 Temperature adjustment factor; N e The cumulative number of equivalent design axle load actions on the design lane within the design service life (times), ε a The tensile strain at the bottom of the asphalt mixture layer is calculated according to the elastic layered system theory, ε a=-53.687+225.743X4+161.835X8+0.154X11+0.0000001299X5*X5-386.763X6*X6-174.68 5X8*X8-0.000141X11*X11-0.0000001241X1*X5+0.0000001747X1*X9-1992.189X2*X4+11 26.921X2*X10+0.002268X3*X8-0.002344X3*X10-0.01053X4*X9-0.004188X5*X8-0.0000 0008339X5*X9-0.000002085X5*X11+280.713X6*X8+0.0000000170X7*X9-0.1939X8*X11;

[0031] When considering the structural fatigue of the base layer, the limit state function is:

[0032]

[0033] In the formula, β is the target reliability index; K a Adjustment coefficient for seasonally frozen areas; K T2 Temperature adjustment factor; R S Flexural strength of inorganic binder stabilized materials (MPa), a, b fatigue test regression parameters; K C On-site comprehensive correction factor; N e The cumulative number of times the equivalent design axle load acts on the design lane within the design service life (times); σ t Tensile stress at the bottom of the inorganic binder stabilized layer (MPa), σ t =0.221-0.2249X6-0.0002716X11-0.0000000002193X7*X7+0.0000003798X1 1*X11-0.00001831X1*X2-0.000030825X2*X3+0.00003902X2*X9-0.00001107 X3*X4+0.0000000002029X3*X9-0.7071X4*X10-0.000007991X5*X8+0.00000 00002992X5*X9+0.0000000001583X7*X9+0.00001247X7*X10-0.8351X8*X10;

[0034] When the vertical compressive stress on the top surface of the roadbed is considered, the limit state function is:

[0035] Z3=1.25×10 4-0.1β ×(KT3 ×N e4 ) -0.21 -ε Z (8)

[0036] Where β is the target reliability index; N e4 The cumulative number of equivalent design axle load actions on the design lane within the design service life; K T3 Temperature adjustment factor; ε Z Vertical compressive strain on the top surface of the roadbed; ε Z =361.178-812.836X4-0.014871X5-367.242X8-512.938X10-0.6751X11 -0.009337X1*X10+0.0000089732X1*X11-2205.2188X2*X6-0.00000005 057X3*X7+0.03457X4*X5+1361.3606X4*X6+1.1167X4*X11+0.018135X5 *X8+0.028678X5*X10-0.013954X6*X9+0.55373X8*X11+1.2821X10*X11;

[0037] In the above formula, X1 is the modulus of the upper layer (MPa), X2 is the thickness of the upper layer (m), X3 is the modulus of the middle layer (MPa), X4 is the thickness of the middle layer (m), X5 is the modulus of the lower layer (MPa), X6 is the thickness of the lower layer (m), X7 is the modulus of the upper base layer (MPa), X8 is the thickness of the upper base layer (m), X9 is the modulus of the subbase layer (MPa), X10 is the thickness of the subbase layer (m), and X11 is the roadbed modulus (MPa).

[0038] Furthermore, in step 6), the failure probability p fi It is expressed by the following formula:

[0039]

[0040] Among them, D f To make the functional function Zi(x)<0, that is, the failure area;

[0041]

[0042] Formula (10) I[Zi] is an indicative function used to determine whether the structure has failed. I[Zi] is an indicative function used to determine whether the structure has failed.

[0043] In step 8), the failure probability is calculated The unbiased estimate of is the mathematical expectation of I[Zi], The statistical formula is as follows:

[0044]

[0045] Calculate the reliability of the surface layer, base layer and roadbed respectively through N sampling Ultimately, the reliability of the pavement structure

[0046] Furthermore, the solutions in step 1) to step 8) can all be implemented by using the Monte Carlo method using Matlab programming.

[0047] Beneficial effects of the present invention: The present invention proposes a Monte Carlo random number generation method for generating a specified range, thereby improving the quality of random number generation.

[0048] The present invention combines the characteristics of permanent pavement such as long service life and large traffic volume, fully considers the mechanical behavior characteristics of permanent asphalt pavement structure, selects the verification indicators of the surface layer, base layer and roadbed of the asphalt pavement structure and the traffic axle load to construct the functional function, and combines the boundedness of the parameter distribution to develop a reliability calculation method for the fatigue index of the permanent asphalt pavement structure based on truncated normal and log-normal distribution random numbers. The functional function equation of the present invention fully considers the construction variability and range of the pavement structure design parameters, so that the statistical parameters are more in line with the actual variability level of the project to carry out fatigue reliability analysis of the permanent asphalt pavement structure. The present invention is suitable for the evaluation of the impact of engineering design indicator variability before the construction of permanent asphalt pavement structure and the fatigue reliability evaluation after completion.

[0049] In addition, the present invention combines the theory of multi-layer elastic layered system to establish the mechanical response index of pavement structure parameters in the tensile strain of the surface layer of permanent asphalt pavement structure, the tensile stress of the base layer, and the compressive strain of the roadbed under the action of standard load (BZZ-100). BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0051] Figure 1 is a flowchart of the steps of the evaluation method of the present invention;

[0052] Figure 2 The probability density distribution diagram of the truncated normal distribution (a=25, b=55) and the random number under the normal distribution condition of the present invention;

[0053] Figure 3 It is a reliability convergence curve drawn under a set number of cycles N in the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0056] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0057] The present invention proposes a permanent pavement structure fatigue reliability evaluation method based on parameter bounded distribution, which can generate a Monte Carlo random number generation method within a specified range and improve the quality of random number generation. The method includes the following steps:

[0058] Step 1) Obtain the variability statistical characteristics of the pavement structure design input parameters, including: mean, variance, distribution type, upper limit and lower limit. By investigating the test data of similar engineering projects in the area or consulting relevant specifications, obtain the structural design parameters such as the thickness and modulus of the pavement structure layer, the traffic load level and traffic volume, and obtain the variability statistical characteristics of the pavement structure design input parameters, such as: mean, variance, distribution type, upper limit and lower limit. Specifically, for the fatigue reliability assessment of the permanent pavement structure after completion, it is necessary to collect a large number of design parameters such as the thickness (hi) and modulus (Ei) of the pavement structure in the field construction of the entire line or some representative sections in combination with the actual project, and calculate the mean, variance, distribution type, upper limit and lower limit of each design parameter; for the evaluation of the impact of the variability of engineering design indicators before construction, the mean, variance, distribution type, upper limit and lower limit of the pavement design parameters can be proposed with reference to existing research results.

[0059] Step 2), based on the variability statistical characteristics of the pavement structure design input parameters obtained in step 1), combined with the classic random parameter distribution model in mathematical statistics, combined with the Monte Carlo sampling principle, generate random numbers that conform to the classic random parameter distribution model.

[0060] Step 3), according to the statistical characteristics of the pavement design parameters obtained in step 1), combined with the probability equivalence principle, step 3), according to the upper and lower limit statistical characteristics of the design parameters in step 1), combined with the probability equivalence principle, determine whether the random number generated in step 2) meets the specified interval range, if it does not meet the interval range, regenerate the random number until the random number that meets the specified interval is generated, thereby obtaining a parameter truncated distribution model that meets the statistical characteristics such as the specified distribution type, upper limit and lower limit in step 1). Specifically, the inverse transformation method determines the generation of the truncated distribution random number with a normal distribution, assuming that d is a random number from 0 to 1 that obeys a uniform distribution (0≤d≤1), then:

[0061]

[0062]

[0063]

[0064] Until (a≤x≤b)

[0065] Step 4) Establish the limit state equation Z of the pavement structure fatigue index and the cumulative equivalent axle number; the pavement structure includes at least the surface layer, the base layer and the roadbed. Specifically, based on the pavement structure design parameters and traffic volume level in step 1), establish the limit state equation Zi=Ri-Si(i=1,2,3) of the fatigue verification index Ri(i=1,2,3) of the pavement structure surface layer, base layer and roadbed and the cumulative equivalent axle number Sii=1,2,3) of the traffic volume.

[0066] Specifically, in step 4), the pavement structure limit state function Zi(x) is assumed to be an n-dimensional random variable x=x1,x2....,x n , its failure region is F = {x:Zi(x)≤0}, if x i (i=1,...,n) are independent and have a mean of μ i The standard deviation is σ i The truncated normal and lognormal distributions of are: a and b are the lower and upper bounds of the random variable, respectively. The truncated distribution x based on the probability equivalence principle is i The probability density function of and the cumulative distribution function As shown in the following formulas (1) and (2):

[0067]

[0068]

[0069] is the regularization constant, so that x i The cumulative distribution function of The cumulative probability in the interval [a, b] is 1; where f, F, They are random variables with mean μ i , standard deviation is σ i Probability density function, cumulative distribution function, mean, and variance of the parent distribution.

[0070] In step 4), the limit state equations of the structural fatigue index and the cumulative equivalent axle times of the surface layer, base layer and roadbed are established respectively. The limit state equations of the surface layer, base layer and roadbed are: Z1, Z2, Z3;

[0071] When considering the structural fatigue of the surface layer, the limit state function is:

[0072]

[0073] In the formula, β is the target reliability index; K a Adjustment coefficient for seasonally frozen areas; K b Fatigue loading mode factor; E a Dynamic compression modulus of asphalt mixture at 20°C (MPa); asphalt saturation of VFA asphalt mixture (%); K T1 Temperature adjustment factor; N e The cumulative number of equivalent design axle load actions on the design lane within the design service life (times), ε a The tensile strain at the bottom of the asphalt mixture layer is calculated according to the elastic layered system theory, ε a =-53.687+225.743X4+161.835X8+0.154X11+0.0000001299X5*X5-386.763X6*X6-174.68 5X8*X8-0.000141X11*X11-0.0000001241X1*X5+0.0000001747X1*X9-1992.189X2*X4+11 26.921X2*X10+0.002268X3*X8-0.002344X3*X10-0.01053X4*X9-0.004188X5*X8-0.0000 0008339X5*X9-0.000002085X5*X11+280.713X6*X8+0.0000000170X7*X9-0.1939X8*X11;

[0074] When considering the structural fatigue of the base layer, the limit state function is:

[0075]

[0076] In the formula, β is the target reliability index; K a Adjustment coefficient for seasonally frozen areas; K T2 Temperature adjustment factor; R S Flexural strength of inorganic binder stabilized materials (MPa), a, b fatigue test regression parameters; K C On-site comprehensive correction factor; N e The cumulative number of times the equivalent design axle load acts on the design lane within the design service life (times); σ t Tensile stress at the bottom of the inorganic binder stabilized layer (MPa), σ t =0.221-0.2249X6-0.0002716X11-0.0000000002193X7*X7+0.0000003798X1 1*X11-0.00001831X1*X2-0.000030825X2*X3+0.00003902X2*X9-0.00001107 X3*X4+0.0000000002029X3*X9-0.7071X4*X10-0.000007991X5*X8+0.00000 00002992X5*X9+0.0000000001583X7*X9+0.00001247X7*X10-0.8351X8*X10;

[0077] When the vertical compressive stress on the top surface of the roadbed is considered, the limit state function is:

[0078] Z3=1.25×10 4-0.1β ×(K T3 ×N e4 ) -0.21 -ε Z (8)

[0079] Where β is the target reliability index; N e4 The cumulative number of equivalent design axle load actions on the design lane within the design service life; K T3 Temperature adjustment factor; ε Z Vertical compressive strain on the top surface of the roadbed; ε Z=361.178-812.836X4-0.014871X5-367.242X8-512.938X10-0.6751X11 -0.009337X1*X10+0.0000089732X1*X11-2205.2188X2*X6-0.00000005 057X3*X7+0.03457X4*X5+1361.3606X4*X6+1.1167X4*X11+0.018135X5 *X8+0.028678X5*X10-0.013954X6*X9+0.55373X8*X11+1.2821X10*X11;

[0080] In the above formula, X1 is the modulus of the upper layer (MPa), X2 is the thickness of the upper layer (m), X3 is the modulus of the middle layer (MPa), X4 is the thickness of the middle layer (m), X5 is the modulus of the lower layer (MPa), X6 is the thickness of the lower layer (m), X7 is the modulus of the upper base layer (MPa), X8 is the thickness of the upper base layer (m), X9 is the modulus of the subbase layer (MPa), X10 is the thickness of the subbase layer (m), and X11 is the roadbed modulus (MPa).

[0081] Step 5), judging the state of the structure function Zi according to steps 3) and 4), if Zi≥0, the structure is judged to be reliable, if Zi<0, the structure is judged to be unreliable.

[0082] Step 6), set the number of cycles N, repeat steps 2) to 5), count the number of times Zi ≥ 0 p, and the number of times Zi < 0 q; specifically, in step 6), the failure probability p f It is expressed by the following formula:

[0083]

[0084] Among them, D f To make the functional function Zi(x)<0, that is, the failure area;

[0085]

[0086] Formula (10) I[Zi] is an indicative function used to determine whether the structure has failed. I[Zi] is an indicative function used to determine whether the structure has failed.

[0087] Step 7), based on step 6), draw a reliability convergence curve of p / (p+q), where N=p+q, and judge the convergence of the reliability calculation process by the drawn reliability convergence curve. If the reliability convergence curve fluctuates greatly, it is necessary to adjust the number of cycles N until the reliability convergence curve is smooth and stable;

[0088] Step 8), based on step 6) and step 7), the last calculated value p / N of the number of cycles N is used as the reliability fi (i = 1, 2, 3) of each layer of the pavement structure, and the last calculated value q / N of the number of cycles N is used as the failure probability p of each layer of the pavement structure fi (i = 1, 2, 3), where the relationship between the reliability of the pavement structure surface layer, base layer and roadbed and the failure probability is fi = 1-p fi =1-q / N (i=1, 2, 3), the pavement structure functions and acts as a whole, then the reliability of the overall pavement structure

[0089] In this embodiment, the solutions in step 1) to step 8) can be implemented by Monte Carlo method using Matlab programming.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A fatigue reliability evaluation method for permanent pavement structure based on bounded parameter distribution, characterized in that: The following steps are involved: Step 1), obtaining the variability statistical characteristics of the pavement structure design input parameters, including: mean, variance, distribution type, upper limit and lower limit; Step 2), based on the variability statistical characteristics of the pavement structure design input parameters obtained in step 1), combined with the classic random parameter distribution model in mathematical statistics, combined with the Monte Carlo sampling principle, generate random numbers that conform to the classic random parameter distribution model; Step 3), according to the variability statistical characteristics of the upper and lower limits of the pavement structure design input parameters in step 1), combined with the classic random parameter distribution model in step 2), based on the probability equivalence principle and the inverse transformation method, a random number that meets the specified interval range is generated. If the random number does not meet the interval range, a random number is regenerated until a random number that meets the specified interval is generated, thereby obtaining a parameter truncation distribution model that meets the statistical characteristics of the distribution type, upper and lower limits specified in step 1); Step 4), based on the pavement structure design input parameters and traffic volume level in step 1), the limit state equations Zi = Ri-Si (i = 1, 2, 3) of the fatigue calculation index Ri (i = 1, 2, 3) of the pavement structure surface layer, base layer and subgrade and the cumulative equivalent axle number Si (i = 1, 2, 3) of the traffic volume are established respectively; Step 5), judging the state of the structure function function Zi according to steps 3) and 4), if Zi ≥ 0, the structure is judged to be reliable, if Zi < 0, the structure is judged to be unreliable; Step 6), set the number of cycles N, repeat steps 2) to 5), count the number of times Zi ≥ 0 P, and the number of times Zi < 0 q; Step 7), based on step 6), draw a reliability convergence curve of p / (p+q), where N=p+q, and judge the convergence of the reliability calculation process by the drawn reliability convergence curve. If the reliability convergence curve fluctuates greatly, it is necessary to adjust the number of cycles N until the reliability convergence curve is smooth and stable; Step 8), based on step 6) and step 7), the last calculated value p / N of the cycle number N is used as the reliability f of each layer of the pavement structure i (i = 1, 2, 3), the last calculated value q / N of the cycle number N is taken as the failure probability p of each layer of the pavement structure fi (i = 1, 2, 3), where the relationship between the reliability of the pavement structure surface layer, base layer and roadbed and the failure probability is f1 = 1-p fi =1-q / N (i=1, 2, 3), then the reliability of the pavement structure 2. The fatigue reliability evaluation method for permanent pavement structure based on parameter bounded distribution according to claim 1 is characterized in that: In step 1), for the permanent pavement structure after construction, the pavement design input parameters of the entire line or a representative section of the pavement engineering project are collected; for the permanent pavement structure before construction, the pavement design input parameters are formulated; The pavement design input parameters are obtained based on pavement structure design parameters and traffic parameters, wherein the pavement structure design parameters include the pavement structure layer thickness and modulus, and the traffic parameters include traffic load level and traffic volume prediction related parameters.

3. The fatigue reliability evaluation method for permanent pavement structure based on parameter bounded distribution according to claim 2 is characterized in that: In step 4), the pavement structure limit state function Zi(x) is an n-dimensional random variable x=x1, x2...., x n , its failure region is F = {x: Zi(x) ≤ 0}, if x i (i=1,...,n) are independent of each other and have a mean of μ i The standard deviation is σ i The truncated normal and lognormal distributions of are, a and b are the lower and upper truncated bounds of the random variable, respectively. The truncated distribution x based on the probability equivalence principle i The probability density function of and the cumulative distribution function As shown in the following formulas (1) and (2): is the regularization constant, so that x i The cumulative distribution function of The cumulative probability in the interval [a, b] is 1; where f, F, They are random variables with mean μ i , standard deviation is σ i Probability density function, cumulative distribution function, mean, and variance of the parent distribution.

4. The method for evaluating fatigue reliability of permanent pavement structure based on bounded parameter distribution according to claim 2 is characterized in that: In step 3), the inverse transformation method uses a normal distribution design to generate truncated distribution random numbers. Let d be a random number from 0 to 1 that obeys a uniform distribution (0≤d≤1), then: Until (a≤x≤b).

5. The fatigue reliability evaluation method for permanent pavement structure based on parameter bounded distribution according to claim 2 is characterized in that: In step 4), the limit state equations of fatigue index and cumulative equivalent axle times of the surface layer, base layer and roadbed structure are established respectively. The limit state equations of the surface layer, base layer and roadbed are: Z1, Z2, Z3; When considering the structural fatigue of the surface layer, the limit state function is: In the formula, β is the target reliability index; K a Adjustment coefficient for seasonally frozen areas; K b Fatigue loading mode factor; E a Dynamic compression modulus of asphalt mixture at 20°C (MPa); asphalt saturation of VFA asphalt mixture (%); K T1 Temperature adjustment factor; N e The cumulative number of equivalent design axle load actions on the design lane within the design service life (times), ε a The tensile strain at the bottom of the asphalt mixture layer is calculated according to the elastic layered system theory, ε a =-53.687+225.743X4+161.835X8+0.154X11+0.0000001299X5*X5- 386.763X6*X6-174.685X8*X8-0.000141X11*X11-0.0000001241X1 *X5+0.0000001747X1*X9-1992.189X2*X4+1126.921X2*X10+0.002 268X3*X8-0.002344X3*X10-0.01053X4*X9-0.004188X5*X8-0.000 00008339X5*X9-0.000002085X5*X11+280.713X6*X8+0.0000000170X7*X9-0.1939X8*X11, where: X1 is the modulus of the upper layer (MPa), X2 is the thickness of the upper layer (m), X3 is the modulus of the middle layer (MPa), X4 is the thickness of the middle layer (m), X5 is the modulus of the lower layer (MPa), X6 is the thickness of the lower layer (m), X7 is the modulus of the upper base layer (MPa), X8 is the thickness of the upper base layer (m), X9 is the modulus of the subbase layer (MPa), X10 is the thickness of the subbase layer (m), and X11 is the modulus of the roadbed (MPa); When considering the structural fatigue of the base layer, the limit state function is: In the formula, β is the target reliability index; K a Adjustment coefficient for seasonally frozen areas; K T2 Temperature adjustment factor; R S Flexural strength of inorganic binder stabilized materials (MPa), a, b fatigue test regression parameters; K C On-site comprehensive correction factor; N e The cumulative number of times the equivalent design axle load acts on the design lane within the design service life (times); α t Tensile stress at the bottom of the inorganic binder stabilized layer (MPa), σ t =0.221-0.2249X6-0.0002716X11-0.0000000002193X7*X7+0.0000003798X11*X11-0.00001831X1*X2-0.000030825X2*X 3+0.00003902X2*X9-0.00001107X3*X4+0.0000000002029X3*X9-0.7071X4*X10-0.000007991X5*X8+0.0000000002992X5 *X9+0.0000000001583X7*X9+0.00001247X7*X10-0.8351X8*X10, where: X1 is the modulus of the upper layer (MPa), X2 is the thickness of the upper layer (m), X3 is the modulus of the middle layer (MPa), X4 is the thickness of the middle layer (m), X5 is the modulus of the lower layer (MPa), X6 is the thickness of the lower layer (m), X7 is the modulus of the upper base layer (MPa), X8 is the thickness of the upper base layer (m), X9 is the modulus of the subbase layer (MPa), X10 is the thickness of the subbase layer (m), and X11 is the modulus of the roadbed (MPa); When the vertical compressive stress on the top surface of the roadbed is considered, the limit state function is: Z3=1.25×10 4-0.1β ×(K T3 ×N e4 ) 0.2i -e Z (8) Where β is the target reliability index; N e4 The cumulative number of equivalent design axle load actions on the design lane within the design service life; K T3 Temperature adjustment factor; ε Z Vertical compressive strain on the top surface of the roadbed; ε Z =361.178-812.836X4-0.014871X5-367.242X8-512.938X10-0.6751X11-0.009337X1*X10+0.0000089732X1*X11-22 05.2188X2*X6-0.00000005057X3*X7+0.03457X4*X5+1361.3606X4*X6+1.1167X4*X11+0.018135X5*X8+0.028678X5 *X10-0.013954X6*X9+0.55373X8*X11+1.2821X10*X11, where: X1 is the modulus of the upper layer (MPa), X2 is the thickness of the upper layer (m), X3 is the modulus of the middle layer (MPa), X4 is the thickness of the middle layer (m), X5 is the modulus of the lower layer (MPa), X6 is the thickness of the lower layer (m), X7 is the modulus of the upper base layer (MPa), X8 is the thickness of the upper base layer (m), X9 is the modulus of the subbase layer (MPa), X10 is the thickness of the subbase layer (m), and X11 is the roadbed modulus (MPa).

6. The method for evaluating fatigue reliability of permanent pavement structure based on parameter bounded distribution according to claim 2 is characterized in that: In step 8), the failure probability p fi (i=1, 2, 3) is represented by the following formula: Among them, D f To make the functional function Zi(x)<0, that is, the failure area; Formula (10) I[Zi] is an indicative function used to determine whether the structure has failed.

7. The method for evaluating fatigue reliability of permanent pavement structure based on bounded parameter distribution according to claim 6, characterized in that: In step 8), calculate the failure probability The unbiased estimate of is the mathematical expectation of I[Zi], The statistical formula is as follows: Calculate the reliability of the surface layer, base layer and roadbed by N sampling Ultimately, the reliability of the pavement structure 8. The fatigue reliability evaluation method for permanent pavement structure based on parameter bounded distribution according to any one of claims 1 to 7, characterized in that: The solutions in steps 1) to 8) can all be implemented using the Monte Carlo method using Matlab programming.

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