A method for obtaining sample data in analyzing dynamic reliability of ballastless track

Abstract

The application discloses a method for obtaining sample data when analyzing dynamic reliability of a ballastless track, comprising the following steps: establishing a fault tree model of a ballastless track structure and converting the fault tree model into a ballastless track Bayesian network; establishing a three-dimensional explicit dynamic numerical model of the ballastless track structure and calculating failure times of each damaged component in the ballastless track Bayesian network model at different service periods; taking the compressive strength of a deteriorated concrete material at different service moments and the corresponding failure times as a sample database, and adopting a machine learning method to construct a regression model of the compressive strength of the material and the corresponding failure times; using a Monte Carlo method to randomly sample the compressive strength of the concrete material, obtaining the corresponding failure times by solving the regression model, and obtaining sufficient failure sample data of each component, wherein the application considers various track structure damage forms, so that a sufficient representative failure sample database is established, and the analysis result of the dynamic reliability of the ballastless track structure is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of high-speed railway technology, in particular to a method for obtaining sample data when analyzing dynamic reliability of ballastless track. BACKGROUND

[0002] Ballastless track has the characteristics of high smoothness, good uniformity of stiffness, long-lasting geometric shape and position maintenance, and less maintenance work. The railways with a speed of 200km / h and above in China all use various types of ballastless track. The track structure of the railways under construction in plateau, high ground stress and active fault zones also plans to use ballastless track.

[0003] The crustal plate movement and geological disasters in the above-mentioned areas can cause large deformation of the track foundation conditions such as uneven settlement of the roadbed, frost heaving arching, and active fault, in addition, extreme environments such as freezing and thawing can also cause degradation of the material properties of track components. Since the large deformation of the track foundation and the degradation of the material properties of the components accompany the entire life cycle of the ballastless track, the reliability of the ballastless track system in the above-mentioned areas during the normal service period is an urgent problem to be clarified.

[0004] At present, the existing research on the reliability of ballastless track structure only targets the track structure in non-plateau, non-high ground stress and active fault zones, and the damage sample data of track components required for reliability analysis is mainly obtained by subjective experience of industry experts. The failure mode of the reliability analysis method is simple (two-state system), and the failure state is simple. The reliability research results cannot reflect the dynamic change process of system reliability over time.

[0005] Dynamic Bayesian network is a high-efficiency tool for analyzing the dynamic reliability of a system, but when analyzing the time-varying reliability of the ballastless track structure in the above-mentioned areas by using dynamic Bayesian network, a large amount of failure sample data of each fault root node is required. Since the railways in the above-mentioned areas are mostly under construction, the statistical data of the failure sample data of each component of the ballastless track structure in this area is almost blank. SUMMARY

[0006] In view of the above-mentioned deficiencies in the prior art, the present application provides a method for obtaining sample data when analyzing the dynamic reliability of ballastless track, which solves the problem of difficulty in obtaining failure data of root node events in Bayesian network when analyzing the dynamic reliability of ballastless track in plateau, high ground stress and active fault zones.

[0007] In order to achieve the above-mentioned application purposes, the technical scheme adopted by the present application is as follows: a method for obtaining sample data when analyzing the dynamic reliability of ballastless track, comprising:

[0008] S1, establishing a fault tree model of the ballastless track structure and converting it into a Bayesian network of the ballastless track;

[0009] S2, a three-dimensional explicit dynamics numerical model of the ballastless track structure is established, and failure times of each damaged component in the ballastless track Bayesian network model at different service periods are calculated;

[0010] S3, the compressive strength of the deteriorated concrete material at different service times and the failure time corresponding thereto are taken as a sample database, and a regression model of the compressive strength of the material and the failure time corresponding thereto is constructed by using a machine learning method;

[0011] S4, the compressive strength of the concrete material is randomly sampled by using a Monte Carlo method, the corresponding failure time is obtained by solving the regression model, and sufficient failure sample data of each component are obtained.

[0012] Further, S1 comprises:

[0013] S11, according to the stress characteristics and damage form statistical data of the ballastless track, the failure mode of the ballastless track structure is determined;

[0014] S12, according to the failure mode of the ballastless track component, the ballastless track structure system is divided into different ballastless track structure subsystems;

[0015] S13, the possibility of each failure mode leading to the failure of the ballastless track structure subsystem is analyzed, and the failure path of the ballastless track structure fault tree is determined;

[0016] S14, according to the failure mode and failure path of the ballastless track structure, a fault tree model of the ballastless track structure is established;

[0017] S15, the established fault tree model of the ballastless track structure is converted into a ballastless track Bayesian network.

[0018] Further, in S11, the failure mode of the ballastless track structure comprises: cracking of the track slab at the middle of the span, cracking of the track slab at 1 / 4 of the end, dropping of the track slab, breaking of the self-compacting concrete, cracking of the base slab at the middle of the span, cracking of the base slab at 1 / 4 of the end, large-area dropping of the base slab, cracking and dropping of the limiting convex, and crushing of the limiting concave of the track slab.

[0019] Further, in S12, the subsystems of the ballastless track structure system comprise a vertical support system of the track slab, a vertical support system of the base slab, a horizontal constraint system of the track slab and the base slab, and a longitudinal constraint system of the track slab.

[0020] Further, in S13, the failure modes leading to the failure of the vertical support system of the track slab comprise: cracking of the track slab at the middle of the span, cracking of the track slab at 1 / 4 of the end, dropping of the track slab, and breaking of the self-compacting concrete.

[0021] The failure modes causing the base slab vertical support system to fail include: base slab cracking at the mid-span, base slab cracking at 1 / 4 of the end, and large area spalling of the base slab;

[0022] The failure modes causing the track slab and base slab transverse restraint system to fail include: cracking and spalling of the limiting boss, and crushing of the track slab limiting groove;

[0023] The failure mode causing the track slab longitudinal restraint system to fail is: crushing of the track slab limiting groove.

[0024] Further, S2 comprises:

[0025] S21, establishing a three-dimensional explicit dynamics numerical model of the ballastless track structure;

[0026] S22, considering the influence of the freeze-thaw environment on the compressive strength degradation of the concrete material, determining the degradation rate of the concrete material, and calculating the compressive strength of the degraded concrete material at different service times;

[0027] S23, inputting the compressive strength of the degraded concrete material at different service times as a calculation parameter into the three-dimensional explicit dynamics numerical model of the ballastless track structure, and simulating the degradation state of the track structure at different service times;

[0028] S24, determining the value range of the applied load, designing corresponding calculation conditions for the failure modes of the root node event in the Bayesian network according to the degradation state of the track structure at different service times, and calculating the number of applied loads at which the track structure components fail under different calculation conditions at different service times through the three-dimensional explicit dynamics calculation model of the ballastless track structure;

[0029] S25, determining the mapping relationship between the number of applied loads at which the track structure components fail in the numerical model of the ballastless track structure and the real time, and obtaining the real failure time of the failure modes of the root node event in the Bayesian network model under each calculation condition.

[0030] Further, S22 comprises:

[0031] S221, considering the influence of the freeze-thaw environment on the degradation of the concrete material, determining the relationship between the dynamic elastic modulus and Poisson's ratio of the material and the number of freeze-thaw cycles, and the expression is:

[0032]

[0033] E c = 1.25E d -19

[0034]

[0035] Wherein, E0 and v are the initial elastic modulus and Poisson's ratio respectively, E and v are the elastic modulus and Poisson's ratio after degradation respectively, E c is the static elastic modulus, E d is the dynamic elastic modulus, N is the number of freeze-thaw cycles experienced by the material;

[0036] S222, according to the relationship between the dynamic elastic modulus and Poisson's ratio of the concrete material and the cycle number, the compressive strength f cu of the concrete material with the cycle number is determined.

[0037] f cu = (((E0(1-0.00007417N)+19) / 1.25-14) / 7.8) 3 ;

[0038] S223, according to the compressive strength f cu of the concrete material with the cycle number, the compressive strength of the degraded concrete material at different service times is calculated.

[0039] Further: S24 includes:

[0040] S241, an explicit dynamic model of vehicle-track coupling is established, and the amplitude range of wheel-rail force is calculated and determined;

[0041] S242, taking the maximum wheel-rail force as a reference, based on the acceleration experiment theory, the load value that causes the track structure in the three-dimensional explicit dynamic model of the ballastless track to fail in advance is determined;

[0042] S243, the corresponding calculation condition is designed through the failure mode of the track structure component in the Bayesian network;

[0043] S244, taking the load value that causes the track structure in the three-dimensional explicit dynamic model of the ballastless track to fail in advance as the loading condition, the corresponding calculation condition is calculated by using the three-dimensional explicit numerical model of the ballastless track structure, and the load action frequency of the track structure component at its failure at different service periods is obtained.

[0044] Further: S25 includes:

[0045] S251, the time T1 of the first failure of the ballastless track structure after being put into use under the condition of large deformation of the foundation, the load number n1 applied when the numerical model fails when the parameter of the compressive strength of the concrete has not been degraded, and the load number N1 applied when the three-dimensional explicit dynamic numerical model of the ballastless track structure fails at different service times are determined.

[0046] S252, determine the mapping relationship between the number of applied loads and the failure time of the track structure components in the three-dimensional numerical calculation model of the ballastless track structure according to T1, n1 and N1, and the expression is:

[0047] t=N1xT1 / n1

[0048] Wherein, t is the component failure time obtained by the three-dimensional explicit dynamic numerical model calculation of the ballastless track structure;

[0049] S253, according to the mapping relationship between the number of applied loads and the failure time of the track structure components in the three-dimensional explicit dynamic numerical model of the ballastless track structure, the specific failure time of the track structure components under different service time is calculated by the three-dimensional explicit dynamic numerical model of the ballastless track structure.

[0050] Further: S4 includes:

[0051] S41, taking the initial compressive strength of the concrete material as the maximum value and the compressive strength after experiencing a 60-year service period as the minimum value, the sampling interval is determined;

[0052] S42, the Monte Carlo random sampling method is used to randomly sample the compressive strength of the concrete material in the determined sampling interval;

[0053] S43, according to the sampled compressive strength data of the concrete material, the corresponding failure time is obtained by solving the regression model, and sufficient failure sample data of each component is obtained.

[0054] The beneficial effects of the present application are:

[0055] 1. The present application adopts a numerical simulation method to establish a three-dimensional explicit dynamic numerical model, which can establish different three-dimensional explicit dynamic numerical models according to different working conditions, covers most of the track structure diseases, and the obtained data is more comprehensive, the calculation cost is low, and the efficiency is high;

[0056] 2. The present application uses machine learning method to establish the regression model of compressive strength and failure time, and uses Monte Carlo method for sampling to obtain sufficient failure sample data, which is simple in operation, low in calculation cost, controllable in failure sample data amount and high in accuracy;

[0057] 3. The method for obtaining sample data in the analysis of the dynamic reliability of the ballastless track structure can consider more track structure diseases, so as to establish a sufficient representative failure sample library, and the dynamic reliability analysis of the ballastless track structure is more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1A flow chart of a method for obtaining sample data when analyzing dynamic reliability of a ballastless track.

[0059] Figure 2 A schematic diagram of a fault tree model of a ballastless track structure.

[0060] Figure 3 A Bayesian network diagram of a ballastless track structure.

[0061] Figure 4 A schematic diagram of a three-dimensional explicit dynamic numerical model of a ballastless track structure.

[0062] Figure 5 A schematic diagram of a three-dimensional explicit dynamic numerical model of a vehicle-track coupling. DETAILED DESCRIPTION

[0063] The specific embodiments of the present application are described below to enable those skilled in the art to understand the present application, but it should be clear that the present application is not limited in scope to the specific embodiments described, and that various modifications within the spirit and scope of the present application, as defined by the claims appended hereto, will occur to those skilled in the art. Any and all such modifications are intended to be included within the scope of the claims.

[0064] As shown in the drawings, Figure 1 In one embodiment of the present application, a method for obtaining sample data when analyzing dynamic reliability of a ballastless track is provided, comprising:

[0065] S1, a fault tree model of a ballastless track structure is established and converted into a Bayesian network of a ballastless track;

[0066] S2, a three-dimensional explicit dynamic numerical model of a ballastless track structure is established, and the failure time of each damaged component in the Bayesian network model of the ballastless track at different service periods is calculated;

[0067] S3, the compressive strength of the deteriorated concrete material at different service times and the corresponding failure time are used as a sample database, and a regression model of the compressive strength of the material and the corresponding failure time is constructed by using a machine learning method;

[0068] S4, the compressive strength of the concrete material is randomly sampled by using a Monte Carlo method, the corresponding failure time is obtained by solving the regression model, and sufficient failure sample data of each component is obtained.

[0069] The present application considers various forms of damage to the track structure, thereby establishing a sufficient representative failure sample library, so that the analysis result of the dynamic reliability of the ballastless track structure is more accurate.

[0070] Specifically, as shown in the drawings, Figure 2The figure is a schematic diagram of a fault tree model of a ballastless track structure, therefore, the method for establishing the fault tree model of the ballastless track structure in S1 comprises:

[0071] S11, determining the failure modes of the ballastless track structure according to the stress characteristics of the structure of the ballastless track and statistical data of damage forms;

[0072] The failure modes of the ballastless track structure include: cracking of the track slab at the middle part of the span, cracking of the track slab at the 1 / 4 part away from the end, dropping of the track slab, breaking of the self-compacting concrete, cracking of the base slab at the middle part of the span, cracking of the base slab at the 1 / 4 part away from the end, large-area dropping of the base slab, cracking and dropping of the limiting boss, and crushing of the limiting groove of the track slab;

[0073] S12, dividing the ballastless track structure system into different ballastless track structure subsystems according to the failure modes of the components of the ballastless track;

[0074] The subsystems of the ballastless track structure system include a vertical support system of the track slab, a vertical support system of the base slab, a horizontal constraint system of the track slab and the base slab, and a longitudinal constraint system of the track slab;

[0075] S13, analyzing the possibility of each failure mode leading to the failure of the ballastless track structure subsystems, and determining the failure paths of the fault tree of the ballastless track structure;

[0076] The failure modes leading to the failure of the vertical support system of the track slab include: cracking of the track slab at the middle part of the span, cracking of the track slab at the 1 / 4 part away from the end, dropping of the track slab, and breaking of the self-compacting concrete;

[0077] The failure modes leading to the failure of the vertical support system of the base slab include: cracking of the base slab at the middle part of the span, cracking of the base slab at the 1 / 4 part away from the end, and large-area dropping of the base slab;

[0078] The failure modes leading to the failure of the horizontal constraint system of the track slab and the base slab include: cracking and dropping of the limiting boss, and crushing of the limiting groove of the track slab;

[0079] The failure mode leading to the failure of the longitudinal constraint system of the track slab is: crushing of the limiting groove of the track slab

[0080] S14, establishing the fault tree model of the ballastless track structure according to the failure modes and the failure paths of the ballastless track structure.

[0081] S15, converting the established fault tree model of the ballastless track structure into a Bayesian network of the ballastless track, and the Bayesian network diagram of the ballastless track is as shown in Figure 3 .

[0082] Specifically, S15 comprises:

[0083] S151, mapping the basic events in the fault tree as the root nodes of the Bayesian network;

[0084] S152, mapping the intermediate events in the fault tree to intermediate nodes of the Bayesian network;

[0085] S153, mapping the top event in the fault tree to a leaf node of the Bayesian network;

[0086] S154. Determine the directed edges and conditional probability table connecting the nodes in the Bayesian network based on the logical relationship between the events in the fault tree.

[0087] Specifically, S2 includes:

[0088] S21. Establish a three-dimensional explicit dynamic numerical model of ballastless track structure;

[0089] S22. Consider the effect of freeze-thaw environment on the deterioration of the compressive strength of concrete materials, determine the deterioration rate of concrete materials, and calculate the compressive strength of deteriorated concrete materials at different service times;

[0090] S23. Using the degraded compressive strength of the concrete material at different service times as a calculation parameter, inputting it into a three-dimensional explicit dynamic numerical model of the ballastless track structure, and simulating the degraded state of the track structure at different service times;

[0091] S24. Determine a value range for the applied load, design corresponding calculation conditions based on the failure mode of the root node event in the Bayesian network according to the degradation state of the track structure at different service times, and calculate the number of applied loads when the track structure components fail under the calculation conditions at different service times using a three-dimensional explicit dynamics calculation model of the ballastless track structure;

[0092] S25. Determine the mapping relationship between the number of applied loads and the actual time when the track structure components fail in the ballastless track structure numerical model, and obtain the actual failure time of the failure mode corresponding to the root node event in the Bayesian network model under each calculation condition.

[0093] like Figure 4 As shown, in particular, in this embodiment, ANSYS / LS-DYNA software is used to establish a three-dimensional explicit dynamic numerical model of the ballastless track structure, specifically including:

[0094] S211, using the APDL language of ANSYS software to write a command stream for establishing a 3D numerical model of the ballastless track, and using ANSYS software to save the command stream as a K file that can be read by LS-DYNA software;

[0095] S212. By reading the K file into LS-DYNA software, a three-dimensional numerical model of ballastless track based on LS-DYNA software for explicit dynamic analysis was established;

[0096] S213. In the LS-DYNA software, the material type and properties of each track component in the ballastless track numerical model established in step S212 are modified to the materials in the LS-DYNA software material library, wherein the beam unit simulating the rail is modified to an isotropic elastic material, the spring unit simulating the fastener stiffness is modified to a linear elastic discrete beam material, the spring unit simulating the CA mortar stiffness is modified to a linear elastic discrete beam material, the solid unit simulating the track plate is modified to a continuous cap beam concrete material, and the beam unit simulating the steel bars in the track plate adopts an elastic-plastic material, so that the constitutive relationship of the reinforced concrete material in the numerical model has the function of simulating mechanical behaviors such as material damage accumulation and structural cracking.

[0097] In particular, the large deformation condition of the foundation in the numerical model is implemented using simply supported constraints.

[0098] S22. The numerical model considers the deterioration effect of concrete material strength over time under freeze-thaw conditions. This is achieved by determining the deterioration rate of concrete material and calculating the compressive strength of concrete material after deterioration at different service times.

[0099] Specifically, S22 includes:

[0100] S221. Considering the effect of freeze-thaw environment on the degradation of concrete materials, the relationship between the dynamic elastic modulus and Poisson's ratio of the material and the degradation of the number of freeze-thaw cycles is determined. The expression is:

[0101]

[0102] E c =1.25E d -19

[0103]

[0104] Among them, E0 and ν are the initial elastic modulus and Poisson's ratio, E and ν are the elastic modulus and Poisson's ratio after degradation, E c is the static elastic modulus, E d is the dynamic elastic modulus, N is the number of freeze-thaw cycles experienced by the material;

[0105] S222. According to the relationship between the dynamic elastic modulus and Poisson's ratio of concrete material and the degradation of the number of cycles, determine the compressive strength f of concrete material and the degradation of the number of cycles. cu , whose expression is:

[0106] f cu =(((E0(1-0.00007417N)+19) / 1.25-14) / 7.8) 3 ;

[0107] S223, the compressive strength of the concrete material deteriorating with the number of cycles is converted into the compressive strength of the concrete material deteriorating with time f cu , the compressive strength of the concrete material deteriorating at different service times is calculated.

[0108] Optionally, the compressive strength of the concrete material deteriorating with the number of cycles is converted into the compressive strength of the concrete material deteriorating with time f c ' u , the compressive strength of the concrete material deteriorating at different service times is calculated.

[0109] f c ' u = (((E0(1-0.00007417T×N′)+19 / 1.25-14) / 7.8) 3

[0110] wherein f c ' u is the compressive strength, T is the service time, and N' is the number of freeze-thaw cycles in the region where the ballastless track structure is located within a year;

[0111] S23, the compressive strength of the concrete material deteriorating at different service times is used as a calculation parameter of a three-dimensional numerical model of the ballastless track structure to simulate the deterioration state of the track structure at different service times;

[0112] S24, the value range of the applied load is determined, the corresponding calculation condition is designed through the failure mode of the root node event in the Bayesian network according to the deterioration state of the track structure at different service times, and the number of applied loads when the track structure component fails at different service times under the calculation condition is calculated through the established three-dimensional explicit dynamics calculation model of the ballastless track structure.

[0113] Specifically, S24 includes:

[0114] S241, a vehicle-track coupling explicit dynamics model is established to calculate and determine the amplitude range of the wheel-rail force;

[0115] The vehicle-track coupling explicit dynamics model established by the LS-DYNA software in the embodiment is shown in Figure 5 ;

[0116] S242, based on the acceleration experiment theory, the load value that causes the track structure in the three-dimensional explicit dynamics model of the ballastless track to fail in advance is determined with reference to the maximum wheel-rail force; the load value is greater than the reference wheel-rail force;

[0117] S243, the corresponding calculation condition is designed through the failure mode of the root node event in the Bayesian network model;

[0118] S244, taking the load value causing the track structure in the three-dimensional explicit dynamic model of the ballastless track to fail in advance as a loading condition, calculating the corresponding calculation conditions by using the three-dimensional explicit numerical model of the ballastless track structure to obtain the load action times of the track structure components in different service periods when the track structure components fail;

[0119] S25, determining the mapping relationship between the load action times of the track structure components in the numerical model of the ballastless track structure when the track structure components fail and the real time to obtain the real failure time of the failure mode corresponding to the root node event in the Bayesian network model under each calculation condition;

[0120] Specifically, S25 includes:

[0121] S251, determining the time T1 of the first failure of the ballastless track structure after being put into use under the condition of large deformation of the foundation, the load action times n1 of the numerical model when the parameter of the compressive strength of the concrete does not deteriorate when the track structure components fail, and the load action times N1 of the three-dimensional explicit dynamic numerical model of the ballastless track structure when the track structure components fail at different service times;

[0122] S252, determining the mapping relationship between the load action times of the track structure components in the three-dimensional numerical calculation model of the ballastless track structure when the track structure components fail and the failure time of the track structure components in reality according to T1, n1 and N1, and the expression is:

[0123] t = N1 x T1 / n1

[0124] Wherein, t is the component failure time obtained by the three-dimensional explicit dynamic numerical model of the ballastless track structure;

[0125] S253, according to the mapping relationship between the load action times of the track structure components in the three-dimensional explicit dynamic numerical model of the ballastless track structure when the track structure components fail and the failure time of the track structure components in reality, calculating the specific failure time of the track structure components under each calculation condition at different service times by the three-dimensional explicit dynamic numerical model of the ballastless track structure.

[0126] In particular, in the embodiment, S3 includes:

[0127] S31, dividing 4 / 5 of the samples in the sample database into a training set, and taking the remaining 1 / 5 of the samples in the sample database as a validation set;

[0128] S32, based on the data in the training set, using the random forest algorithm in the machine learning method to establish a regression model taking the compressive strength of the concrete material as the independent variable and the component failure time as the dependent variable;

[0129] S33, based on the data in the validation set, using the random forest algorithm in the machine learning method to verify the accuracy of the regression model.

[0130] S34, the failure time of each damaged component of the ballastless track in different service periods can be calculated through the established regression model.

[0131] Further, S4 comprises:

[0132] S41, the initial compressive strength of the concrete material is taken as the maximum value, and the compressive strength after experiencing a 60-year service period is taken as the minimum value, to determine a sampling interval;

[0133] S42, the Monte Carlo random sampling method is used to randomly sample the compressive strength of the concrete material in the determined sampling interval;

[0134] S43, according to the sampled compressive strength data of the concrete material, the corresponding failure time is solved through the regression model, and sufficient sample data of the failure of each component is obtained.

[0135] The above examples are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for obtaining sample data in analyzing dynamic reliability of a ballastless track, characterized in that, include: S1. Establish a ballastless track structure fault tree model and convert it into a ballastless track Bayesian network; S2. Establish a three-dimensional explicit dynamic numerical model of the ballastless track structure and calculate the failure time of each damaged component in the ballastless track Bayesian network model at different service periods; S3. Using the compressive strength of deteriorated concrete materials at different service times and their corresponding failure times as a sample database, a regression model of the compressive strength and the corresponding failure time of the materials is constructed using a machine learning method. S4. Use the Monte Carlo method to randomly sample the compressive strength of concrete materials, obtain the corresponding failure time through the regression model, and obtain sufficient failure sample data for each component.

2. The method for obtaining sample data in analyzing dynamic reliability of ballastless track according to claim 1, characterized in that, S1 includes: S11. Determine the failure mode of the ballastless track structure based on the structural stress characteristics and damage statistics of the ballastless track; S12. Divide the ballastless track structure system into different ballastless track structure subsystems according to the failure modes of the ballastless track components; S13. Analyze the possibility of each failure mode causing failure of the ballastless track structure subsystem and determine the failure path of the ballastless track structure fault tree; S14. Establish a fault tree model of the ballastless track structure based on the failure mode and failure path of the ballastless track structure; S15. Convert the established ballastless track structure fault tree model into a ballastless track Bayesian network.

3. The method for obtaining sample data in analyzing dynamic reliability of ballastless track according to claim 2, characterized in that, In S11, the failure modes of the ballastless track structure include: cracking of the track slab in the middle of the span, cracking of the track slab at 1 / 4 of the distance from the end, track slab block falling, crushing of self-compacting concrete, cracking of the base plate in the middle of the span, cracking of the base plate at 1 / 4 of the distance from the end, large-scale block falling of the base plate, cracking and block falling of the limit boss, and crushing of the track slab limit groove.

4. The method for obtaining sample data when analyzing dynamic reliability of ballastless track according to claim 3, characterized in that, In S12, the subsystems of the ballastless track structure system include the track plate vertical support system, the base plate vertical support system, the track plate and base plate lateral restraint system, and the track plate longitudinal restraint system.

5. The method for obtaining sample data in analyzing dynamic reliability of ballastless track according to claim 4, characterized in that, In S13, the failure modes that lead to failure of the track slab vertical support system include: cracking of the track slab mid-span, cracking of the track slab 1 / 4 of the way to the end, track slab falling, and crushing of self-compacting concrete; Failure modes that lead to failure of the base plate vertical support system include: cracking of the base plate mid-span, cracking of the base plate 1 / 4 of the way to the end, and large-scale falling of the base plate; Failure modes that lead to failure of the lateral restraint system of the track plate and base plate include: cracking and falling of the limiting boss, and crushing of the track plate limiting groove; The failure mode that causes the longitudinal restraint system of the track plate to fail is: the track plate limiting groove is crushed.

6. The method for obtaining sample data when analyzing dynamic reliability of ballastless track according to claim 1, characterized in that S2 include: S21. Establish a three-dimensional explicit dynamic numerical model of ballastless track structure; S22. Consider the effect of freeze-thaw environment on the deterioration of the compressive strength of concrete materials, determine the deterioration rate of concrete materials, and calculate the compressive strength of deteriorated concrete materials at different service times; S23. Using the degraded compressive strength of the concrete material at different service times as a calculation parameter, inputting it into a three-dimensional explicit dynamic numerical model of the ballastless track structure, and simulating the degraded state of the track structure at different service times; S24, determine the value range of the applied load, according to the deterioration state of the track structure at different service times, design the corresponding calculation condition through the failure mode of the root node event in the Bayesian network, and calculate the load number of the track structure components at different service times under the calculation condition through the three-dimensional explicit dynamics calculation model of the ballastless track structure; S25, determine the mapping relationship between the load number of the track structure components in the numerical model of the ballastless track structure at failure and the real time, and obtain the real failure time of the failure mode of the root node event in the Bayesian network model under each calculation condition.

7. The method for obtaining sample data when analyzing dynamic reliability of ballastless track according to claim 6, characterized in that, S22 includes: S221, considering the influence of freeze-thaw environment on the deterioration of concrete material, determine the relationship between the dynamic elastic modulus and Poisson's ratio of the material and the freeze-thaw cycle number, which is expressed as: E c = 1.25E d -19 where E0and v are the initial modulus of elasticity and Poisson's ratio, respectively, and are the modulus of elasticity and Poisson's ratio after deterioration, respectively, E c is the static modulus of elasticity, E d is the dynamic modulus of elasticity, and N is the number of freeze-thaw cycles experienced by the material. S222. According to the relationship between the dynamic elastic modulus and Poisson's ratio of concrete materials and the degradation of the number of cycles, determine the compressive strength f of concrete materials that degrades with the number of cycles. cu , whose expression is: f cu = (((E0(1 - 0.00007417N) + 19) / 1.25 - 14) / 7.8) 3 ; S223、According to the concrete material and the compressive strength f cu , the compressive strength of the concrete material after deterioration at different service times is calculated.

8. The method for obtaining sample data in analyzing dynamic reliability of ballastless track according to claim 6, characterized in that, S24 includes: S241, establish a vehicle-track coupling explicit dynamics model, calculate and determine the amplitude range of wheel-rail force; S242, taking the maximum wheel-rail force as the reference, determine the load value that makes the track structure in the three-dimensional explicit dynamics model of the ballastless track fail in advance based on the acceleration experiment theory; S243, design the corresponding calculation condition through the failure mode of the track structure components in the Bayesian network; S244, take the load value that makes the track structure in the three-dimensional explicit dynamics model of the ballastless track fail in advance as the loading condition, calculate the corresponding calculation condition using the three-dimensional explicit numerical model of the ballastless track structure, and obtain the load action number of the track structure components at different service times when they fail.

9. The method for obtaining sample data in analyzing dynamic reliability of ballastless track according to claim 6, characterized in that, S25 includes: S251, determine the first failure time T1 of the ballastless track structure after being put into use under the condition of large deformation of the foundation, the load number n1 of the numerical model at failure when the parameter of the compressive strength of concrete is not deteriorated, and the load number N1 of the three-dimensional explicit dynamics numerical model of the ballastless track structure at failure at different service times; S252, determine the mapping relationship between the load number of the track structure components in the three-dimensional numerical calculation model of the ballastless track structure at failure and the real failure time thereof according to T1, n1 and N1, which is expressed as: t=N1×T1 / n1 Wherein, t is the component failure time calculated by the three-dimensional explicit dynamics numerical model of the ballastless track structure; S253, according to the mapping relationship between the load number of the track structure components in the three-dimensional explicit dynamics numerical model of the ballastless track structure at failure and the real failure time thereof, calculate the specific failure time of the track structure components under each calculation condition at different service times through the three-dimensional explicit dynamics numerical model of the ballastless track structure.

10. The method for obtaining sample data when analyzing dynamic reliability of ballastless track according to claim 1, characterized in that S4 Including: S41, take the initial compressive strength of the concrete material as the maximum value, and the compressive strength after experiencing 60 years of service period deterioration as the minimum value, determine the sampling interval; S42, use the Monte Carlo random sampling method to randomly sample the compressive strength of the concrete material in the determined sampling interval; S43, according to the sampled concrete material compressive strength data, solve the corresponding failure time through the regression model to obtain sufficient component failure sample data.

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