Heavy haul railway sleeper life prediction method, device, equipment, medium and product

Abstract

The application discloses a heavy haul railway sleeper life prediction method, device, equipment, medium and product, relates to the heavy haul railway detection technical field, and the method comprises the following steps: obtaining initial characteristic values of sleeper parameters and initial characteristic values of track structure parameters of a target sleeper; based on the initial characteristic values of the sleeper parameters and the initial characteristic values of the track structure parameters, simulating variation laws of the sleeper parameter characteristic values and variation laws of the track structure parameter characteristic values within a set total time length; in the simulation process, every set time step, based on the current time, a set number of random samples composed of each sleeper parameter characteristic value and each track structure parameter characteristic value are generated; according to whether the sleeper individual corresponding to the random sample is functionally failed, the reliability value of the target sleeper at the current time is determined, and the life prediction value of the target sleeper is determined by comparing the reliability value of each set time step with a reliability limit value. The application realizes heavy haul railway sleeper life prediction.

Description

Technical Field

[0001] This application relates to the field of railway testing technology, and in particular to a method, device, equipment, medium and product for predicting the life of heavy-haul railway sleepers. Background Technology

[0002] Heavy-haul railways have become the future direction of global railway freight development due to their large capacity, high efficiency, and low transportation costs. As a key component of the track structure in heavy-haul lines, sleepers play a crucial role in distributing train loads, fixing rails, and effectively maintaining track geometry. Their service condition and lifespan significantly impact the operational safety of heavy-haul lines, and currently, there is a lack of methods for predicting the lifespan of heavy-haul railway sleepers. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, equipment, medium, and product for predicting the lifespan of heavy-haul railway sleepers, thereby realizing the prediction of the lifespan of heavy-haul railway sleepers.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for predicting the lifespan of heavy-haul railway sleepers, including: Obtain the initial characteristic values ​​of the sleeper parameters of the target sleeper; Obtain the initial characteristic values ​​of the track structure parameters of the track corresponding to the target sleeper; Based on the initial characteristic values ​​of sleeper parameters and track structure parameters, the simulation examines the variation patterns of both sleeper and track structure parameter characteristic values ​​over a set total time period. During the simulation, at each set time step, a set number of random samples, each consisting of the characteristic values ​​of each sleeper parameter and track structure parameter, are generated based on the current moment. The reliability value of the target sleeper at the current moment is determined based on whether the individual sleeper corresponding to the random sample has failed. The reliability value at each set time step is compared with the reliability limit to determine the predicted lifespan of the target sleeper.

[0005] Secondly, this application provides a heavy-haul railway sleeper life prediction device, comprising: The initial feature value acquisition module for sleeper parameters is used to obtain the initial feature values ​​of the sleeper parameters of the target sleeper. The initial feature value acquisition module for track structure parameters is used to obtain the initial feature values ​​of the track structure parameters of the track corresponding to the target sleeper. The sleeper life prediction module is used to simulate the variation patterns of sleeper parameter characteristic values ​​and track structure parameter characteristic values ​​over a set total time period, based on the initial characteristic values ​​of sleeper parameters and track structure parameters. During the simulation, at set time steps, a set number of random samples composed of the characteristic values ​​of each sleeper parameter and each track structure parameter are generated based on the current time. The reliability value of the target sleeper at the current time is determined according to whether the individual sleeper corresponding to the random sample has failed. The reliability value at each set time step is compared with the reliability limit to determine the predicted life value of the target sleeper.

[0006] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the heavy-haul railway sleeper life prediction method described in any one of the above.

[0007] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the heavy-haul railway sleeper life prediction method described in any one of the above descriptions.

[0008] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the heavy-haul railway sleeper life prediction method described above.

[0009] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, apparatus, equipment, medium, and product for predicting the lifespan of heavy-haul railway sleepers. Based on the initial characteristic values ​​of sleeper parameters and track structure parameters, it simulates the variation patterns of these characteristic values ​​over a set total time period. During the simulation, at set time steps, a set number of random samples, each consisting of the characteristic values ​​of each sleeper parameter and track structure parameter, are generated based on the current moment. The reliability value of the target sleeper at the current moment is determined based on whether the individual sleeper corresponding to the random sample has functional failure. The reliability value at each set time step is compared with a reliability limit to determine the predicted lifespan of the target sleeper. The prediction process considers the temporal variation patterns of sleeper parameters and track components, achieving accurate prediction of the lifespan of heavy-haul railway sleepers, thereby improving the operational safety of heavy-haul railways. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a method for predicting the lifespan of heavy-haul railway sleepers, provided as an embodiment of this application.

[0012] Figure 2 This is a detailed flowchart illustrating a method for predicting the lifespan of heavy-haul railway sleepers, provided as an embodiment of this application.

[0013] Figure 3 This is a schematic diagram of the minimum protective layer thickness for railway sleeper reinforcement provided in an embodiment of this application.

[0014] Figure 4 The thickness of the concrete protective layer for railway sleepers provided in one embodiment of this application c A schematic diagram showing the calculation results of the initial corrosion time of the sleeper structure when the thickness is 28.5mm.

[0015] Figure 5 Provided for an embodiment of this application c A schematic diagram showing the calculation results of the initial corrosion time of the sleeper structure when the thickness is 42.3mm.

[0016] Figure 6 Provided for an embodiment of this application c A schematic diagram showing the calculation results of the initial corrosion time of the sleeper structure when the thickness is 49.5mm.

[0017] Figure 7 This is a first-view schematic diagram of a finite element model of a track structure provided in an embodiment of this application.

[0018] Figure 8 This is a second-view schematic diagram of a finite element model of a track structure provided in an embodiment of this application.

[0019] Figure 9 This is a schematic diagram of the time-varying effect curve of the load amplification factor as a function of stiffness, provided in an embodiment of this application.

[0020] Figure 10 This is a schematic diagram of the track bed support reaction force for calculating the design value of the bending moment under the load of the track section, provided in an embodiment of this application.

[0021] Figure 11 This is a schematic diagram of the track bed support reaction force for calculating the design value of the bending moment under load at the sleeper mid-section, provided as an embodiment of this application.

[0022] Figure 12 This is a schematic diagram of the lower section of a Type III sleeper rail provided in an embodiment of this application.

[0023] Figure 13 This is a schematic diagram of the mid-section of a Type III pillow provided in an embodiment of this application.

[0024] Figure 14 The time-varying reliability values ​​of the sleeper structure under three environmental coupling effects are provided in one embodiment of this application.

[0025] Figure 15 This is a schematic diagram of the failure of the rail section under the bearing bending moment according to an embodiment of this application.

[0026] Figure 16 This is a schematic diagram of the failure of the bearing moment of the pillow section in an embodiment of this application.

[0027] Figure 17 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] In one exemplary embodiment, this application discloses a method for predicting the lifespan of heavy-haul railway sleepers, such as... Figure 1 As shown, the method for predicting the lifespan of heavy-haul railway sleepers includes steps 101-103.

[0031] Step 101: Obtain the initial characteristic values ​​of the sleeper parameters of the target sleeper.

[0032] The sleeper parameters include sleeper size, material, prestressed steel reinforcement, and load.

[0033] The target sleeper is a sleeper for a target heavy-haul railway. Specifically, the sleeper is a concrete sleeper.

[0034] Step 102: Obtain the initial characteristic values ​​of the track structure parameters of the track corresponding to the target sleeper.

[0035] Step 103: Based on the initial characteristic values ​​of the sleeper parameters and the initial characteristic values ​​of the track structure parameters, simulate the variation patterns of the sleeper parameter characteristic values ​​and the track structure parameter characteristic values ​​within a set total time period. During the simulation, at each set time step, generate a set number of random samples consisting of the characteristic values ​​of each sleeper parameter and each track structure parameter based on the current moment. Determine the reliability value of the target sleeper at the current moment based on whether the individual sleeper corresponding to the random sample has failed. Compare the reliability value at each set time step with the reliability limit to determine the predicted lifespan of the target sleeper.

[0036] The variation law of the characteristic values ​​of track structure parameters is to obtain the variation law of sleeper load.

[0037] The total time is set as the service time T of the target sleeper, and the time step is set to 1 year.

[0038] This application takes into account the time variation of sleeper parameters and track structure parameters (parameters of various components in the track structure), and realizes accurate prediction of sleeper life of heavy-haul railways, thereby improving the operational safety of heavy-haul railways.

[0039] In one exemplary embodiment, the specific process of a method for predicting the lifespan of heavy-haul railway sleepers is as follows: Figure 2 As shown.

[0040] (1) Determine the probability distribution type and corresponding initial characteristic value of sleeper parameters such as sleeper size, material and prestressed steel bars.

[0041] (2) Determine the initial characteristic values ​​of the characteristic parameters (track structure parameters) of each component of the track structure.

[0042] (3) Determine the influence of the service environment (single environment) on the sleeper-related parameters; if environmental coupling is considered, it is necessary to determine the influence of the coupled environment on the sleeper-related parameters.

[0043] (4) Construct a pillow pressure calculation model and determine the probability distribution type and time-varying characteristics of pillow pressure.

[0044] (5) Determine the function for sleeper failure.

[0045] (6) Determine the allowable reliability limit based on the failure probability of the function. β ].

[0046] (7) From the calculation time t Starting from 0, a sample database is constructed using random sampling, and the functional function values ​​of individual sleepers corresponding to the random samples are analyzed to determine the reliability value of the target sleeper at the current moment.

[0047] (8) Calculate the time in a loop (each interval is...) By using random sampling, a sample database is constructed, and the functional function values ​​of individual sleepers corresponding to the random samples generated at the current moment are analyzed to determine the reliability value of the target sleeper at the current moment.

[0048] (9) Obtain the reliability values ​​of the target sleepers corresponding to different calculation times, and compare them with the reliability limits [ β By comparison, the service life of the sleepers can be determined.

[0049] In an exemplary embodiment, in step 101, while obtaining the initial characteristic values ​​of the sleeper parameters of the target sleeper, the probability distribution type of the sleeper parameters is also obtained. Step 101 specifically includes: according to the "Design Code for Heavy-Haul Railways" (TB10625-2017), "Battery Track Sleepers and Concrete Sleepers" (GB / T 37330-2019), "Steel Wire for Prestressed Concrete" (GB / T5223-2014), "Design Code for Concrete Structures" (GB50010-2010), etc., the initial characteristic values ​​of the sleeper parameters can be obtained, that is, the initial characteristic values ​​of the sleeper size, material and prestressed steel bars are shown in Table 1.

[0050] Table 1 Initial Eigenvalues ​​of Basic Parameters

[0051] When using Monte Carlo simulation to calculate structural reliability indices, the probability distribution of random variables directly affects the sample parameters generated by sampling, thus influencing the analysis results of structural reliability. In practical engineering, the normal distribution is a common and easily handled probability distribution. For the statistical analysis of many physical quantities, it is often assumed that they follow a normal distribution under certain conditions. In this example, it is assumed that all random variables follow a normal distribution.

[0052] The standard deviation is estimated using the parameter's permissible deviation. Based on the normal distribution... The principle is that the numerical values ​​are distributed in ( , The probability in the interval is 0.9974. This interval can be regarded as the possible value range of the random variable. The standard deviation of the random variable can be calculated directly from the value range of the random variable.

[0053] The standard deviations of sleeper parameters are based on the dimensional limits of Type III sleepers in the "Concrete Sleeper" (TB / T 2190-2013), the allowable diameter deviation of steel wire for prestressed concrete (GB / T 5223-2014), the standard deviation of concrete cube compressive strength in the "Specifications for Concrete Construction of Waterway Engineering" (ITS 236-2022), and statistical data from a certain heavy-haul railway. Table 2 shows the standard deviation values ​​for sleeper parameters.

[0054] Table 2 Parameter Tolerances

[0055] Taking sleeper length as an example, the allowable deviation is ±5 mm, meaning the sleeper length fluctuates within the range of (2595 mm, 2605 mm). Therefore, we can obtain... The standard deviation of sleeper length is 1.67 mm.

[0056] The probability distribution types of each sleeper parameter at this time are shown in Table 3.

[0057] Table 3. Types of probability distributions for parameters

[0058] Where N represents a normal distribution; the coefficient of variation is the ratio of the standard deviation of each random variable to its mean, reflecting the dispersion of the random variable. The probability distribution type of each sleeper parameter in this application is either a normal distribution or a constant value.

[0059] In an exemplary embodiment, step 102 specifically includes: the track structure parameters specifically refer to the parameters of each structural component in the track. Each structural component includes six parts: rails, fasteners, rail pads, concrete sleepers, prestressed steel strands, track bed, and subgrade (roadbed). Specifically, the vertical stiffness of the fastener nodes is taken as 140 kN / mm; the sleeper spacing is 600 mm; the track bed thickness is 300 mm, the top surface width is 3300 mm, and the slope ratio is 1:1.75; the roadbed thickness is 2500 mm and the width is 5000 mm.

[0060] The parameter characteristic values ​​of each component of the heavy-haul railway track structure are shown in Table 4.

[0061] Table 4 Initial characteristic values ​​of track structure parameters

[0062] In one exemplary embodiment, during the simulation, the sleeper parameters and track structure parameters change over time under the influence of general atmospheric conditions, chloride ion erosion, and freeze-thaw cycles.

[0063] Under normal atmospheric conditions, the erosion of concrete is mainly due to surface carbonation. Concrete carbonation begins as soon as the structure enters service and is an inevitable process. Over time, concrete carbonation causes cracking and spalling on the structural surface, resulting in a continuous reduction in the load-bearing cross-section of the sleeper structure.

[0064] Among them, the dimensional parameters of the sleeper component are the side length ( b , h The reduction factor due to carbonization is: (1) (2) Approximate the cross-sections of the sleeper as having a side length of... b and h One of the components. The two formulas above are calculations of the time-varying characteristic values ​​of all geometric dimensional parameters of the sleeper. Both belong to the reduction factor, and the reduction values ​​are the same, but they represent different directions of the sleeper component. Among them, Represents the side length along the length of the sleeper. b Decrease factor Represents the side length along the height direction of the sleeper. h Decrease factor The initial characteristic values ​​of the parameters representing the sleeper along its length (including the distance from the center of the sleeper bearing groove to the sleeper end in this application). a 1. Support length of sleepers under general steel rails sleeper length Effective width of the compression zone section , ), This represents the initial characteristic values ​​of the parameters of the sleeper components along the height direction (including the width of the rail groove in this application). h q sleeper cross-sectional height h 1. h 2) express t The carbonation depth of concrete at any given time. t Indicates carbonization time. K The carbonation rate coefficient of concrete can be calculated by equation (3).

[0065] (3) in, This is the regional influence coefficient, which can be set to a value of 1.0. This is the indoor / outdoor impact coefficient, with an outdoor value of 1.0. The maintenance time impact coefficient is generally taken as 1.5; The standard value for the compressive strength of concrete cubes is 60 MPa, which is used for railway sleepers.

[0066] The time-varying mean values ​​of the various geometric parameters of the sleeper are expressed as follows: (4) The time-varying standard deviation of each geometric dimensional parameter of the sleeper is expressed as follows: (5) in, and These are the initial time (time 0) and... tThe mean of the geometric dimensional parameters of a sleeper at a certain time. and They are the initial time and t The standard deviation of the geometric dimensional parameters of a railway sleeper at a given time. for or , For a certain structural parameter t The eigenvalues ​​at time t, Let be the initial characteristic value of a certain structural parameter, where the structural parameter is the geometric dimension parameter of the sleeper.

[0067] At this point, the time-varying effects of the mean and standard deviation of the sleeper cross-sectional geometric parameters, taking sleeper length and rail groove width as examples, are expressed by the following formula: (6) in, and These are the average values ​​of sleeper length and rail groove width at time 0, respectively. and These are respectively the length of the sleeper and the width of the rail groove. t The mean at any given time; and These are the standard deviations of sleeper length and rail groove width at time 0, respectively. and These are respectively the length of the sleeper and the width of the rail groove. t Standard deviation of time and These represent the lengths of the railway sleepers. t The parameter eigenvalues ​​at time 0 and time 0, and These represent the widths of the rail bearing grooves. t The parameter eigenvalues ​​at time 0 and time 0 are given in two examples to illustrate the calculation of time-varying eigenvalues. In fact, all the time-varying eigenvalues ​​of the geometric parameters mentioned above are considered.

[0068] Under normal atmospheric conditions, the corrosion of steel bars in in-service railway sleeper structures is mainly caused by concrete carbonation. The following formula can be used to preliminarily calculate the time when prestressed steel bars in the sleeper structure begin to corrode.

[0069] (7) in, This refers to the time it takes for steel bars to begin corroding under atmospheric conditions. This refers to the thickness of the concrete protective layer for the railway sleepers. The carbonation coefficient of concrete; This refers to the residual carbonation content of concrete.

[0070] The theoretical calculation model for carbonization residue is as follows: (8) in, The ambient humidity is expressed as (%).

[0071] Based on the design drawings of Type III sleepers, the minimum protective layer thickness of each reinforcing bar appears at the cross-section of the sleeper. Therefore, the reinforcing bars can be divided into three rows and numbered. The top row of four bars, from left to right, are A1 to A4; the middle two are A5 to A6; and the bottom row is A7 to A10. Figure 3 As shown in Table 5, the minimum protective layer thickness and initial corrosion time for reinforcing bars are as follows.

[0072] Table 5 Minimum protective layer thickness and initial corrosion time for reinforcing bars

[0073] Regarding the calculation models for the time-varying area of ​​reinforcing bars after corrosion begins, there are two main approaches: 1. Dividing the corrosion pattern into overall corrosion and partial corrosion, and calculating the time-varying area of ​​the reinforcing bars under each condition; 2. Ignoring the corrosion pattern, expressing the time-varying area of ​​the corroded reinforcing bars as the product of the initial cross-sectional area and a deterministic function. This example uses the second calculation model. Under general atmospheric conditions, the formulas for calculating the mean and standard deviation of the cross-sectional area of ​​the reinforcing bars after corrosion begins are as follows.

[0074] (9) In the formula, , These represent the corrosion of the prestressed steel bars in the sleeper structure. t Mean and standard deviation of time-varying cross-sectional area after the new year; , These represent the mean and standard deviation of the initial cross-sectional area of ​​the reinforcing bars, respectively. , These are functions of the mean and standard deviation as a function of time, respectively. for t The cross-sectional area of ​​prestressed steel bars at all times. This represents the initial value of the cross-sectional area of ​​the prestressed steel reinforcement.

[0075] (10) in, , , , ; , , , .

[0076] During actual service, the compressive strength of railway sleepers' concrete changes over time. Studies show that concrete strength increases initially, then the rate of increase gradually slows down, and finally decreases over time. Currently, in practical engineering applications, the time-varying change in concrete strength is mainly simulated as the product of the initial concrete strength and a certain defined function. Research results indicate that the time-varying effect model of the average concrete strength under general atmospheric conditions... It can be represented as: (11) Where e is the natural base.

[0077] Then passed t After 10 years, the average compressive strength of the concrete in the sleeper structure can be expressed as: (12) in, for t The average compressive strength of the concrete in the sleeper structure at any given time. The mean value of the concrete compressive strength of the sleeper structure at time 0.

[0078] Time-varying effect model of concrete strength standard deviation obtained using linear regression method for: (13) The standard deviation of concrete strength can be expressed as: (14) in, for t Standard deviation of concrete strength at any given time The standard deviation of concrete strength at time 0.

[0079] Regarding chloride ion corrosion, in railway sleeper structures, the small radius of chloride ions allows them to penetrate concrete pores and defects, damaging the passivation film on the steel reinforcement surface and initiating localized electrochemical corrosion, leading to pitting corrosion. In practical engineering applications, Fick's second diffusion law is often used to calculate the chloride ion concentration in concrete structures. According to Fick's second diffusion law, over a certain period of service life... t After the year, the distance from the concrete surface c Chloride ion concentration at the location for: (15) in, This represents the initial chloride ion concentration in the concrete structure. Chloride ion concentration on the concrete surface; It is the error function; , For input variables; is the chloride ion diffusion coefficient.

[0080] In actual calculations of the initial corrosion time of reinforcing steel, the initial chloride ion concentration in the concrete structure can be ignored. Therefore, the time for initial corrosion of reinforcing steel under chloride ion corrosive environment can be calculated using the following formula: (16) in, This is the critical chloride ion concentration. This indicates the thickness of the concrete protective layer for the railway sleepers. It can also be expressed as: (17) in, and All are model parameters; Water-cement ratio, For the quality of water, The quality of the cement.

[0081] Many factors influence chloride ion corrosion, and these factors exhibit significant variability. Therefore, a probabilistic analysis method is recommended when calculating the initial corrosion time of reinforcing bars, assuming that all parameters follow a normal distribution or are constant values. This example also adopts this assumption. Table 6 shows the distribution of different parameters in the calculation of the initial corrosion time of reinforcing bars under chloride ion corrosion conditions. The mean protective layer thickness in Table 6 is selected based on the minimum protective layer thickness in Table 5, and the standard deviation is taken as the standard deviation of the height deviation.

[0082] Table 6 Distribution of different parameters

[0083] For the sleeper structure, the calculation results of its initial corrosion time are as follows: Figures 4-6 As shown.

[0084] Under the influence of chloride ion corrosion environment, Figures 4-6 The initial corrosion times of the steel bars were 3.43 years, 7.38 years, and 10.19 years, respectively.

[0085] Generally speaking, the corrosion forms of steel bars include uniform corrosion and pitting corrosion, with pitting corrosion being the typical form of steel bar corrosion.

[0086] The cross-sectional area loss of the corrosion layer can be calculated using the following formula: (18) (19) in, fort The cross-sectional area loss of prestressed steel bars due to corrosion at all times; The radius of the steel strand; The maximum pitting depth (mm / year) is the maximum pitting depth along the radial direction. This represents the angle between the maximum pitting point and the tangent to the pitting edge at the center of the circle.

[0087] Maximum pitting depth of steel reinforcement corrosion It can be calculated using the following formula: (20) in, The pitting coefficient ranges from 4 to 8. In this example, the pitting coefficient is taken as its average value of 6. This represents the time at which steel corrosion begins. As a time-dependent parameter that decreases over time, the corrosion rate can be calculated using the following formula: (twenty one) in, for t Corrosion rate at time (μA / cm) 2 ), The initial corrosion rate at the onset of corrosion can be calculated using the following formula: (twenty two) At this point, the cross-sectional area of ​​the prestressed steel reinforcement in the sleeper structure The time-varying effect of the mean is shown in the following equation: (twenty three) in, This represents the initial value of the cross-sectional area of ​​the prestressed steel reinforcement. express t The cross-sectional area loss of prestressed steel bars after corrosion (the reduction in the cross-sectional area of ​​the prestressed steel bars).

[0088] When reinforcing bars are in a state of localized corrosion, the standard deviation of the ratio of their time-varying cross-sectional area to their initial cross-sectional area can be calculated using the following formula: (twenty four) In the formula, This is the coefficient of the standard deviation of the ratio of the time-varying cross-sectional area to the initial cross-sectional area of ​​the reinforcing steel under localized corrosion conditions. The value is an integer from 1 to 7. , , , , , , .

[0089] According to the Code for Design of Concrete Structures (GB50010-2010), the design strength value of steel reinforcement is obtained by dividing the standard strength value by the material partial factor.

[0090] (25) in, This represents the design value of the tensile strength of the reinforcing steel. This indicates the standard value of the tensile strength of the reinforcing steel. This represents the partial factor for materials.

[0091] When prestressed steel bars corrode, the standard value of their tensile strength decreases. To simplify the calculation, the calculation model for the standard value of the tensile strength of the steel bars in this example is as follows: (26) in, This refers to the standard value of the time-varying tensile strength of corroded steel bars, i.e. t Standard value of tensile strength of corroded steel bars at any given time. The initial tensile strength of the steel reinforcement. for t Constant-time steel reinforcement corrosion section loss rate.

[0092] (27) in, This represents the initial characteristic value of the cross-sectional area of ​​the prestressed steel reinforcement. express t The cross-sectional area loss of prestressed steel bars after corrosion (the reduction in the cross-sectional area of ​​the prestressed steel bars).

[0093] Combining equations (25) and (27), the time-varying calculation formula for the design value of the tensile strength of prestressed steel bars can be expressed as: (28) The change in the design value of the tensile strength of prestressed steel bars over time is related to the cross-sectional area of ​​the steel bars. In the subsequent reliability calculation, this is regarded as the decrease in the mean value and substituted into the calculation, without considering the randomness of the tensile strength parameters of the steel bars.

[0094] Under freeze-thaw cycles, the structural material properties of railway sleepers degrade over time. It has been pointed out that concrete compressive strength is a fundamental parameter for determining structural members, and its temporal variation is the basis for establishing a model of the resistance degradation of in-service structures. This example demonstrates a time-dependent variation model of the mean relative strength of concrete in railway sleeper structures under freeze-thaw cycles. The variation patterns derived from coupling over 5-year periods are used, and the relative intensity standard deviation is modeled linearly. The correlation coefficients for fitting can reach 0.889 and 0.903, respectively.

[0095] (29) (30) Then passed t After 10 years, the average concrete compressive strength of the sleeper structure It can be represented as: (31) in, The mean value of the concrete compressive strength of the sleeper structure at time 0.

[0096] Standard deviation of concrete strength It can be represented as: (32) in, The standard deviation of concrete strength at time 0.

[0097] Based on the finite element method, and relying on the initial characteristic values ​​of the track structure parameters in step 102, a finite element model of the track structure is established. By substituting the time-varying stiffness of the track pads and the track bed, the variation law of the sleeper pressure with service time is indirectly analyzed.

[0098] According to the "Technical Conditions for Rubber Pads Under Concrete Railway Sleepers" (TB_T 2626-1995), the static stiffness of the 60-10-17 pad is taken as 55~80 kN / mm. The relationship between the static stiffness of the rubber pad under the rail and time is as follows: (33) in, Service life Static stiffness of the rubber pad under the time track. The service life of the pad (in hours); This is the initial value for the static stiffness of the track pad; in this example, it is taken as 55 kN / mm.

[0099] When time is measured in years, equation (33) can be rewritten as equation (34).

[0100] (34) in, Service life t Annual static stiffness of the track pad.

[0101] Since the elastic modulus of the material needs to be input when performing finite element simulation, under the premise that the vertical static stiffness of the pad is known, the elastic modulus of the rail pad can be determined according to the "pad's compressive area". A With thickness H Perform reverse conversion: (35) in, E Enter the elastic modulus value of the material.

[0102] A ballast track model was established using the discrete element method (DEM). Taking the sleeper at the middle position of the model as the research object, vertical displacements of 35 kN and 7.5 kN were applied at the rail bearing grooves at both ends of the sleeper, respectively. The support stiffness was calculated using the secant slope. The track density was set to 0.5 g·cm³. -3 Incremental application, from 1.60 g·cm⁻¹ -3 The initial changes yielded the relationship between the track bed density and the stiffness of the under-pillar support, as shown in equation (36).

[0103] (36) in, The under-pillow support stiffness (kN / mm); The density of the track bed (g / cm³) 3 ).

[0104] In the finite element method (FEM) calculation, it is assumed that the stiffness of the track bed and the under-rail rubber pads changes uniformly over time. That is, in the model, the material properties of the track bed and the under-rail rubber pads are isotropic, and their elastic modulus is a fixed value at each time point in the calculation. Using 10 years as a time increment, the time-varying material stiffness of the under-rail rubber pads and track bed is modified to obtain the relationship between the reaction force at the sleeper bearing groove and time.

[0105] A finite element model of a heavy-haul railway track structure has been established to analyze the influence of different rail pad stiffnesses on the structural stress and deformation characteristics under train loads, providing a reference for the establishment of the finite element model in this example. The track structure consists of six parts: rails, fasteners, rail pads, concrete sleepers, prestressed steel strands, ballast, and substructure. Spring elements are used to simulate fasteners; truss elements (T3D2) are used to simulate the prestressed steel strands in the sleepers, with a cross-sectional area of ​​38.48 mm². 2 The remaining five components—rails, rail pads, sleepers, ballast, and subgrade—are simulated using solid element C3D8R. The total length of the track structure in the model is 3 m, with a total of 5 sleepers. The finite element model is as follows: Figure 7 and Figure 8 As shown.

[0106] The interaction between the prestressed steel strands and the sleeper concrete is simulated using an embedded region. The interactions between the bottom surface of the rail pad and the top surface of the sleeper, the bottom surface of the sleeper and the top surface of the ballast, and the bottom surface of the ballast and the top surface of the subgrade are all set as surface-to-surface contact with tangential hard friction. The normal direction is simulated by defining the friction coefficient to ensure that the load can be transferred vertically.

[0107] The boundary conditions at both ends of the rail are set as simply supported constraints. For the spring element simulating the vertical stiffness of the fastener, it needs to transmit the load vertically downwards; therefore, the y-direction degree of freedom at the node is released, while all other x- and z-direction degrees of freedom are constrained. A reference point RP-1 is established at the center of the roadbed surface and coupled to the roadbed surface. A completely fixed boundary condition is applied to this point, i.e., the degrees of freedom in all six directions are constrained.

[0108] In ABAQUS software, the application of prestress in prestressed steel strands is mainly simulated using the cooling method and the initial strain method. This example selects the cooling method to apply prestress to 10 prestressed steel strands in the sleeper structure. The cooling method primarily works by defining an expansion coefficient for the prestressed steel strands; after cooling, the strands will shrink and bend, leading to the cambering of the concrete sleeper. Its advantages include its applicability in any analysis step of the model, its simplicity, and its ability to accurately simulate the mechanical properties of prestressed concrete structures. Calculate using equation (37).

[0109] (37) in, This represents the actual prestressing force (MPa) of the prestressed steel strand. The coefficient of linear expansion of prestressed steel strand ( ); The required cooling value (°C); This is the elastic modulus (MPa) of the prestressed steel strand.

[0110] The control parameters and calculation results of prestressed steel strands in the cooling method are shown in Table 7.

[0111] Table 7 Control parameters and calculation results of prestressed steel strands in the cooling method

[0112] Based on the above calculations, when a train load of 25t is applied to the track structure according to the C80 train model, with a dynamic load factor of 1.5 and a rail pad stiffness of 55 kN / mm, the calculated vertical displacement of the rail is 1.80 mm. Therefore, this example uses displacement loading to apply a displacement of 1.80 mm to the rail at the middle position of the model.

[0113] The calculation results are used to derive the vertical reaction force (RF) data at the rail support point at the middle position of the model, obtaining the load-time relationship curve of the track structure under the premise of elastic failure of the track bed and under-rail pads over time. This paper defines an amplification factor for the load variation with stiffness. To determine its time-varying effect.

[0114] (38) in, The reaction force of the sleeper bearing part when the elasticity of the under-rail rubber pad and the track bed fails; This refers to the reaction force of the sleeper bearing the rail when the stiffness of the under-rail rubber pad and the ballast bed remains unchanged. This represents the change in the stiffness of the track structure parameters at different times, obtained through software simulation.

[0115] This application uses track structure parameters to obtain the variation of the load on the vertical rail-bearing portion of the sleepers. In practical applications, over time, the stiffness of the track structure, including the ballast bed and rail pads, gradually increases (the change in stiffness over time is known), leading to elastic failure. The model is substituted with parameters from different time periods. t The stiffness values ​​of the components were used to calculate the reaction force of the sleeper's rail-bearing section. The reaction force gradually increased, indicating that the load on the vertical rail-bearing section of the sleeper gradually increased over time. The increase was calculated using a magnification factor. To determine the time-varying effects of load parameters.

[0116] Magnification factor See the detailed calculation results Figure 9 As shown. By Figure 9 It can be seen that the amplification factor increases with time. Since the selected time-varying stiffness model for the track pads and ballast bed is a linear variation model, the amplification factor of the load with stiffness variation can be observed. It also shows a relatively obvious linear correlation with time, and the correlation coefficient R of the fitted curve is... 2 It is 0.99983.

[0117] This model can be used as a time-varying load model in subsequent calculations. At this point, the vertical load on the rail-supporting portion above the sleeper... The calculation needs to be multiplied by the amplification factor of the load as a function of stiffness. Calculate using the following formula: (39) in, for t The vertical load (pressure value on the sleeper) on the rail bearing section above the sleeper at any time. This represents the initial characteristic value of the vertical load on the rail-bearing portion above the sleeper. for t The amplification factor of the load as stiffness changes over time.

[0118] For heavy-haul railways, during the service life of sleepers, which bear continuous heavy axle loads, the main cause of sleeper failure is the excessive positive bending moment at the rail sub-section and the excessive negative bending moment at the sleeper mid-section, exceeding the design threshold. Under the action of the excessive bending moment, cracks occur in the sleeper section, causing the sleeper structure to reach its ultimate bearing capacity.

[0119] The failure function for the positive bending moment at the track section is: (40) in, for t The value of the positive bending moment failure function at the track-subgrade section at time t. for t Design value of bending moment under load at the track section; for t The bending moment borne by the cross section under the track at that moment. and All of these parameters change over time, and the design value calculation process also involves some parameters. The time-varying characteristic values ​​of these parameters are substituted into the calculation.

[0120] At this point, the limit state equation for the sleeper is: (41) Less than At that time, the positive bending moment function of the rail section fails.

[0121] The negative bending moment failure function of the pillow section is: (42) Limit state equations: (43) in, for t Design value of bending moment under load at the mid-section of the sleeper at any given time; Let t be the bending moment borne by the cross section at the center of the pillow.

[0122] During normal operation of heavy-haul railway trains, locomotives and rolling stock exert force on the sleepers via the rails. In the "Provisional Specification for Design of Railway Tracks Using the Limit State Method" (Q / CR 9130-2015), the vertical load on the rail-bearing portion of the prestressed concrete sleeper is... Calculate using the following formula: (44) in, The static wheel weight (kN) is half of the design static axle weight; The wheel load distribution coefficient is generally taken as ≤0.45 for lines using 75kg / m rails and 600 mm sleeper spacing. The dynamic load factor is determined by factors such as train speed and track condition. It is 1.5 for heavy-load tracks and 1.0 for other tracks.

[0123] When calculating the vertical design load bending moment of the sleeper section, the reaction force diagram of the track bed support under the sleeper is shown in the figure below. Figure 10 and Figure 11 As shown, Figure 11 middle q This represents the value of a uniformly distributed load, which is the reaction force provided by the track bed to the sleepers.

[0124] When calculating the design value of the bending moment under the rail section, it is assumed that the middle part of the sleeper is completely hollowed out. Therefore, the design value of the bending moment under the rail section of the sleeper adopts... Figure 10 calculate: (45) In the formula, for t Vertical load (kN) on the rail-supporting section above the sleeper at any time; for t The distance from the center of the sleeper rail groove to the sleeper end at any given time, initial value. The value is 0.5 m; for t The initial value of the support length of the sleeper under the general rail. The value is 0.95 m; For heavy-haul railways, the main line rails can be 60 kg / m or 75 kg / m. According to the rail design section diagram of "Rail Part 1: 43kg_m~75kg_m Rails" (TB_T 2344.1-2020), the rail base width is 0.15 m. and The parameters related to size are obtained by generating random samples from the time-varying mean and standard deviation of formulas (4) and (5).

[0125] When calculating the design value of the bending moment at the mid-section of the sleeper, it is generally assumed that the middle part of the sleeper is partially supported, and the support reaction force is taken as 3 / 4 of that of a fully supported sleeper. Therefore, the design value of the bending moment at the mid-section of the sleeper structure is adopted. Figure 11 calculate: (46) in, for t Sleeper length at any given time (m); for t The length of the middle section of the sleeper at any given time. .

[0126] Substituting the formula for calculating the support length of the middle section of the sleeper into equation (46), the formula for calculating the design value of the bending moment of the load at the middle section of the sleeper becomes: (47) Bending moment under rail section and the bending moment of the sleeper section Based on their respective cross-sectional characteristic values, the calculation is performed according to the following process. Type III prestressed concrete sleepers, when checked according to the ultimate limit state of bearing capacity, should meet the requirements of formula (48): (48) in, This is the load partial factor.

[0127] (49) in, The height of the concrete compression zone under the rail section is calculated using the following formula: (50) in, for t The bending moment borne by the cross section under the track at that moment. express t Design value of axial compressive strength of concrete at any given time. The mean and variance of the concrete compressive strength are calculated using formulas (31) and (32), and then obtained through random sampling. The difference between formulas (12) and (14) is that the mean and variance deteriorate more severely in freeze-thaw environments than in general atmospheric environments. Therefore, the formula with the most severe deterioration is used in the examples. for t Effective width of the compression zone section under the time track. , for t Width of the top surface of the cross-section under the time track. for t Width of the bottom surface of the cross section under the time track. , for t The cross-sectional area of ​​the longitudinal prestressing tendons in the tension and compression zones at all times; for t The stress in the prestressing tendon when the normal stress in the concrete at the resultant point of the longitudinal prestressing tendon in the compression zone is equal to zero. for t Effective height of the cross-section under the time track. , for t Height of the cross section under the time track, It is the vertical distance from the resultant point of all longitudinal tensile reinforcement bars to the tension edge of the section under the rail; for t The distance from the resultant point of the prestressed tendons in the compression zone to the compression surface at any given time; , for t The design values ​​of tensile and compressive strength of prestressed steel bars at all times. express t Design value of axial compressive strength of concrete at any given time.

[0128] Since the sleeper's calculated cross-section is trapezoidal, after determining the height of the compression zone, when calculating according to formula (49), the width of the compression zone is the average width of the upper and lower bases of the trapezoid. Because the height of the sleeper's compression zone is relatively small, the resulting calculation error due to this simplification can be ignored.

[0129] Because the sleeper cross-section height is small, the prestressed steel bars are uniformly and symmetrically arranged, and no additional non-prestressed steel bars are required, the expression can be significantly simplified compared to the more common one. In addition, the reinforcement ratio of the sleeper cross-section is relatively high, and the height of the compression zone calculated according to formula (50) is relatively small, that is, the prestressed steel bars are not in the compression zone. Therefore, the second term on the right side of the formula (i.e., the case where the compression zone is equipped with prestressed steel bars) can be ignored. In this case, the calculation formulas for the design value of the sleeper cross-section resistance are shown in formulas (51) and (52). This part simplifies the calculation of formulas (49) and (50). Calculation during comparison Equations (51) and (52) are used.

[0130] (51) (52) according to Figure 12 It can be obtained that the calculation hour, , for t Effective width of the compression zone section under the time track. , They are respectively t The initial values ​​for the top and bottom widths of the cross-section under the time track are 170.5 mm and 314 mm, respectively. , for t Effective height of the cross-section under the time track. for t The height of the track under section is set at a time, with an initial value of 230 mm. It is the vertical distance from the resultant point of all longitudinal tensile reinforcement bars to the tension edge of the section under the rail. Formula (51) is used to calculate t The bearing bending moment of the rail under section at time t is given by formula (52), which is used to inversely calculate the height of the concrete compression zone of the rail under section in formula (51). .

[0131] The bending moment borne by the sleeper section Calculate using equations (53)-(54).

[0132] (53) (54) according to Figure 13 It can be obtained that the calculation hour, , for t The effective width of the cross-section of the compression zone in the pillow at any given moment. , They are respectively t The initial values ​​for the top and bottom widths of the cross-section in the pillow at time points are 220 mm and 280 mm, respectively. , for t Effective height of the pillow cross section at all times for t The height of the pillow section at all times The value is 185 mm. It is the vertical distance from the resultant point of all longitudinal tensile reinforcement bars to the tension edge of the sleeper section. for t The height of the compression zone of the concrete at the mid-section of the pillow at any given time.

[0133] Among them, the following parameters are: the distance from the center of the sleeper bearing groove to the sleeper end, the support length of the sleeper under the rail, the width of the rail base, the length of the sleeper, the design value of the axial compressive strength of concrete, the top surface width of the sleeper, the bottom surface width of the sleeper, the height of the section under the rail, the design value of the tensile strength of the prestressed steel bars, the cross-sectional area of ​​the longitudinal prestressed steel bars in the tension zone, the top surface width of the sleeper section, the bottom surface width of the sleeper section, and the height of the sleeper section.

[0134] According to the recommendations in ISO 2394-2015, the target reliability index for the sleeper structure is selected as follows: the reliability limit is [value missing]. The corresponding structural failure probability at this time That is, when the sample size satisfies 10 6 During the simulation, the time interval dt =The sleeper structure failed 10 times within one year.

[0135] In an exemplary embodiment, in step 103, during the simulation process, at predetermined time steps, a predetermined number of random samples are generated based on the initial characteristic values ​​and variation patterns of each sleeper parameter, as well as the initial characteristic values ​​and variation patterns of the track structure parameters. Each random sample includes sleeper parameter characteristic values ​​that satisfy the time-varying mean and standard deviation of each sleeper parameter, and component characteristic values ​​that satisfy the time-varying mean and standard deviation of the track structure parameters. In short, this application generates random samples at different times using time-varying mean and standard deviation values.

[0136] The quantity is set to 10. 6 That is, the sample size corresponding to random samples at different times is 10. 6 .

[0137] In an exemplary embodiment, this application considers the randomness and time-varying characteristics of sleeper parameters (section geometry, prestressed steel bars, concrete materials, etc.) and track structure parameters (rail type, fastener stiffness, ballast stiffness, etc.); it can consider the time-varying characteristics of the influence of a single environment or various complex service environments (general atmosphere, chloride ion corrosion and freeze-thaw cycle coupling, etc.) on sleeper parameters; it can consider the randomness and time-varying characteristics of loads; based on this, the reliability time-varying law corresponding to different sleeper failure modes can be determined.

[0138] The reliability value of the target sleeper at the current moment is determined based on whether the individual sleepers corresponding to the random sample are functionally failed, specifically including the following steps 201-204.

[0139] Step 201: For the random sample generated at the current moment, calculate the design value of the load bending moment of the rail section, the design value of the load bending moment of the sleeper section, the bearing bending moment of the rail section, and the bearing bending moment of the sleeper section for the individual sleeper corresponding to the random sample.

[0140] Step 202: If the bearing bending moment of the rail section is less than the design value of the load bending moment of the rail section, or the bearing bending moment of the sleeper section is less than the design value of the load bending moment of the sleeper section, then the individual sleeper corresponding to the random sample is deemed to have failed.

[0141] Step 203: Count the number of sleepers with functional failures in the random sample generated at the current moment, and determine the probability of functional failure based on the number of sleepers with functional failures and the sample size of the random sample.

[0142] Step 204: Determine the reliability value of the target sleeper at the current moment based on the aforementioned functional failure probability.

[0143] In an exemplary embodiment, the values ​​in step 201 are calculated according to formulas (45), (47), (51) and (53).

[0144] The variation of the vertical load on the rail-bearing portion above the sleeper over time is expressed as follows: .

[0145] in, for t The vertical load on the rail-supporting section above the sleeper at any given time. This represents the initial characteristic value of the vertical load on the rail-bearing portion above the sleeper. for t The amplification factor of the load as stiffness changes over time.

[0146] in, ; for t When the elasticity of the track pads and ballast fails, the reaction force of the sleeper bearing part is considered. This represents the change in the stiffness of the track structure parameters at different times. Depend on t The characteristic values ​​of the orbital structure parameters at each time point were determined through software simulation. This refers to the reaction force of the sleeper bearing the rail when the stiffness of the under-rail rubber pad and the ballast bed remains unchanged.

[0147] ; in, For static wheel weight, This is the wheel weight distribution coefficient. This is the comprehensive dynamic load factor.

[0148] In one exemplary embodiment, t The bending moment under the rail section and the bending moment at the sleeper section at any given time must meet the following formula requirements: ; ; in, This is the load partial factor; if it does not meet the requirement, the individual corresponding to the random sample is deemed to have failed.

[0149] In an exemplary embodiment, in accordance with the "Unified Standard for Reliability Design of Railway Engineering Structures" (GB 50216-2019), the structural limit state equations can be used to calculate the reliability index using the Monte Carlo simulation method according to the following steps.

[0150] 1) Select a group of representative structures or components, whose structural function is: , It is a random variable.

[0151] 2) Generate random numbers for a known distribution variable through random sampling. , For random variables Random numbers.

[0152] 3) Calculate the function value .

[0153] 4) Let the number of samplings be... The function value calculated for each set of random variables is , The number of times L After a large number of samples, the probability of functional failure of the structure is... .

[0154] 5) Calculate the reliability value from the probability of functional failure. , .

[0155] In one exemplary embodiment, when computation time t When =0, select the sleeper effect calculated by equations (44) to (47) and the sleeper structural resistance calculated by equations (51) to (52). Select the initial mean and standard deviation of each parameter appearing in the sleeper structural function and its corresponding probability distribution type from step 101; generate a random sample with a size of 10 that satisfies the initial mean and standard deviation of the parameters using the normrnd function in Matlab programming software. 6 Calculate the function value for each individual sample, and statistically analyze the resistance of the sleeper structure to be less than the effect (i.e., ) number of times L Thus, the failure probability of the structure is obtained. The reliability index is derived from the failure probability. .

[0156] In one exemplary embodiment, the time (the time interval d each time) is calculated cyclically. t (Taking 1 year), the total time T is defined as the design service life of the sleeper structure of 60 years. According to the influence of the service environment on the sleeper size, material and prestressed steel bars and other parameters, the time-varying mean and time-varying standard deviation of the relevant sleeper parameters in each year within the total time T range can be obtained; based on the calculation result formula (39), the time-varying characteristics of the sleeper pressure can be obtained (the time-varying value of the sleeper pressure is obtained by multiplying formula (44) by the amplification factor. And formula (44) is obtained by multiplying the train static wheel weight by two constant values. This paper simulates the probability distribution of sleeper pressure by the probability distribution of train static wheel weight, generates a series of train static wheel weight values, substitutes them into formula (44), and multiplies them by the amplification factor to obtain the sample value of sleeper pressure. It follows a normal distribution); the number of random samples that satisfy the time-varying mean and time-varying standard deviation of the parameters in each year is 10. 6For each sample, calculate the corresponding function value and statistically analyze the resistance of the sleeper structure to the action effect (i.e., the resistance is less than the action effect). The failure probability of the structure is obtained by calculating the number of times L is applied. The reliability index is derived from the failure probability. .

[0157] Because the Monte Carlo simulation method involves random sampling during the calculation process, the calculation results also have randomness. Therefore, a method of calculating 10 sets of results and averaging them is adopted to reduce the calculation error. The time-varying reliability of the structure may show an "INF" result (infinite reliability) in the early stages of service. Increasing the number of Monte Carlo simulations might yield a more specific value, but due to low computational efficiency, only 10 simulations are implemented in this example. 6 This simulation was performed, and in the results graph, the first occurrence of the maximum reliability value of 4.7534 was used to replace the result of infinity. Under complex service environments, the reliability values ​​corresponding to different calculation times are as follows: Figure 14 As shown.

[0158] Depend on Figure 14 It can be seen that in the initial stage of the sleeper structure's service, both failure modes maintained high reliability, indicating that the structure had high reliability in the early stages of service. Over time, the reliability of all failure modes showed a decreasing trend. Specifically, the reliability of the positive bending moment failure at the rail sub-section began to decline around the 12th year of the sleeper structure's service; while the reliability of the negative bending moment failure at the sleeper mid-section declined earlier, starting around the 11th year of the structure's service.

[0159] Different failure modes have varying degrees of impact on the reliability of railway sleeper structures. Under the three coupled environmental conditions, the time-varying reliability of structures with positive bending moment failure at the rail sub-section decreases more rapidly, with an average annual decay rate of 12.98%; while the average annual decay rate of negative bending moment failure at the sleeper mid-section is only 2.03%. Due to the differences in decay rate and initial decay time, the time-varying reliability results of the two failure modes intersect between the 12th and 13th years of the structure's service life. Under the coupled environmental conditions, within the service life of the sleeper, it can be considered that positive bending moment failure at the rail sub-section plays a controlling role in the reliability of the railway sleeper structure.

[0160] The reliability calculation results were fitted with a quadratic polynomial curve, and the fitting results are as follows: Figure 15 and Figure 16 As shown, the fitting correlation coefficients can reach 0.9874 and 0.99447, respectively.

[0161] ; ; in, for Figure 15 Medium reliability fitting curve, for Figure 16 Medium reliability fitting curve. Take [ β With a value of 4.2, it can be concluded that when the failure of the sleeper based on the positive bending moment at the rail under section is taken as the basis for reliability failure, the actual service life of the structure is about 13.22 years; when the failure of the sleeper based on the negative moment at the sleeper mid-section is taken as the basis for reliability failure, the actual service life of the structure is about 14.61 years.

[0162] Based on the same inventive concept, this application also provides a device for predicting the lifespan of a heavy-haul railway sleeper as described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the heavy-haul railway sleeper life prediction device provided below can be found in the limitations of the heavy-haul railway sleeper life prediction method described above, and will not be repeated here.

[0163] In one exemplary embodiment, this application provides a heavy-haul railway sleeper life prediction device, which includes the following modules.

[0164] The initial characteristic value acquisition module for sleeper parameters is used to obtain the initial characteristic values ​​of the sleeper parameters of the target sleeper; the sleeper parameters include sleeper size, material, prestressed steel reinforcement and load; the sleeper is a sleeper of the target heavy-haul railway.

[0165] The initial feature value acquisition module for track structure parameters is used to obtain the initial feature values ​​of the track structure parameters corresponding to the target sleeper.

[0166] The sleeper life prediction module is used to simulate the variation patterns of sleeper parameter characteristic values ​​and track structure parameter characteristic values ​​over a set total time period, based on the initial characteristic values ​​of sleeper parameters and track structure parameters. During the simulation, at set time steps, a set number of random samples composed of the characteristic values ​​of each sleeper parameter and each track structure parameter are generated based on the current time. The reliability value of the target sleeper at the current time is determined according to whether the individual sleeper corresponding to the random sample has failed. The reliability value at each set time step is compared with the reliability limit to determine the predicted life value of the target sleeper.

[0167] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 17As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database of the computer device is used for a method for predicting the lifespan of heavy-haul railway sleepers. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for predicting the lifespan of heavy-haul railway sleepers.

[0168] Those skilled in the art will understand that Figure 17 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0169] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0170] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0171] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0172] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0173] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0174] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0175] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0176] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting the lifespan of heavy-haul railway sleepers, characterized in that, The method for predicting the lifespan of heavy-haul railway sleepers includes: Obtain the initial characteristic values ​​of the sleeper parameters of the target sleeper; Obtain the initial characteristic values ​​of the track structure parameters of the track corresponding to the target sleeper; Based on the initial characteristic values ​​of sleeper parameters and track structure parameters, the simulation examines the variation patterns of both sleeper and track structure parameter characteristic values ​​over a set total time period. During the simulation, at each set time step, a set number of random samples, each consisting of the characteristic values ​​of each sleeper parameter and track structure parameter, are generated based on the current moment. The reliability value of the target sleeper at the current moment is determined based on whether the individual sleeper corresponding to the random sample has failed. The reliability value at each set time step is compared with the reliability limit to determine the predicted lifespan of the target sleeper.

2. The method for predicting the lifespan of heavy-haul railway sleepers according to claim 1, characterized in that, During the simulation, at predetermined time steps, a predetermined number of random samples are generated based on the current time, consisting of the characteristic values ​​of each sleeper parameter and each track structure parameter. Specifically, these samples include: At set time steps, based on the initial characteristic values ​​of each sleeper parameter and the variation law of each sleeper parameter characteristic value, as well as the initial characteristic values ​​of the track structure parameter and the variation law of the track structure parameter characteristic value, a set number of random samples are generated. Each random sample includes sleeper parameter characteristic values ​​that satisfy the time-varying mean and time-varying standard deviation of each sleeper parameter, and component characteristic values ​​that satisfy the time-varying mean and time-varying standard deviation of the track structure parameter.

3. The method for predicting the lifespan of heavy-haul railway sleepers according to claim 2, characterized in that, The reliability value of the target sleeper at the current moment is determined based on whether the individual sleepers corresponding to the random sample are functionally failed. Specifically, this includes: For the random sample generated at the current moment, calculate the design value of the load bending moment of the rail section, the design value of the load bending moment of the sleeper section, the bearing bending moment of the rail section, and the bearing bending moment of the sleeper section for the individual sleeper corresponding to the random sample; If the bending moment of the track under section is less than the design value of the bending moment of the track under section, or the bending moment of the sleeper section is less than the design value of the bending moment of the sleeper section, then the individual sleeper corresponding to the random sample is deemed to have failed. The number of individual sleepers with functional failures in the random sample generated at the current moment is counted, and the probability of functional failure is determined based on the number of individual sleepers with functional failures and the sample size of the random sample. The reliability value of the target sleeper at the current moment is determined based on the probability of functional failure.

4. The method for predicting the lifespan of heavy-haul railway sleepers according to claim 3, characterized in that, For each random sample generated at the current moment, calculate the design values ​​of the bending moment under the rail section, the bending moment under the rail section, and the bending moment under the rail section for the corresponding individual sleeper. Specifically, this includes: The formula for calculating the design value of the bending moment under the rail section load is as follows: ； in, for t The design value of the bending moment under the rail section at time t. for t The vertical load on the rail-supporting section above the sleeper at any given time. for t The distance from the center of the rail groove to the end of the sleeper at any given time. for t The support length of the sleeper under the rail at all times. This refers to the width of the rail base. The formula for calculating the design value of the bending moment under load at the sleeper mid-section is as follows: ； in, for t The design value of the bending moment under load at the sleeper section at time _____. for t Sleeper length at any given time; The formula for calculating the bending moment of the section under the rail is: ； ； in, for t The bending moment borne by the track section at a given time. express t Design value of axial compressive strength of concrete at any given time. for t Effective width of the compression zone section under the time track. , for t Width of the top surface of the cross-section under the time track. for t Width of the bottom surface of the cross section under the time track. for t Effective height of the cross-section under the time track. , for t Height of the cross section under the time track, It is the vertical distance from the resultant point of all longitudinal tensile reinforcement bars to the tension edge of the section under the rail. for t Height of the concrete compression zone at the track section for t The design value of the tensile strength of prestressed steel bars at any given time. for t The cross-sectional area of ​​the longitudinal prestressed steel bars in the tension zone at all times; The formula for calculating the bending moment of the sleeper section is: ； ； in, for t The bending moment borne by the pillow section at any given time. for t The effective width of the cross-section of the compression zone in the pillow at any given moment. , for t Width of the top surface of the cross-section at the moment of birth. for t The width of the bottom surface of the pillow cross section at any given moment. for t Effective height of the pillow cross section at all times , for t The height of the pillow section at all times It is the vertical distance from the resultant point of all longitudinal tensile reinforcement bars to the tension edge of the sleeper section. for t The height of the compression zone of the concrete at the mid-section of the pillow at any given moment; Among them, the distance from the center of the sleeper bearing groove to the sleeper end, the support length of the sleeper under the rail, the width of the rail base, the length of the sleeper, the design value of the axial compressive strength of concrete, the width of the top surface of the rail section, the width of the bottom surface of the rail section, the height of the rail section, the design value of the tensile strength of the prestressed steel bars, the cross-sectional area of ​​the longitudinal prestressed steel bars in the tension zone, the width of the top surface of the sleeper section, the width of the bottom surface of the sleeper section, and the height of the sleeper section are all sleeper parameters.

5. The method for predicting the lifespan of heavy-haul railway sleepers according to claim 4, characterized in that, The variation of the vertical load on the rail-bearing portion above the sleeper over time is expressed as follows: ； in, for t The vertical load on the rail-supporting section above the sleeper at any given time. This represents the initial characteristic value of the vertical load on the rail-bearing portion above the sleeper. for t The amplification factor of load as stiffness changes over time; in, ; for t When the elasticity of the track pads and ballast fails, the reaction force of the sleeper bearing part is considered. The reaction force of the sleeper bearing the rail when the stiffness of the under-rail rubber pad and the ballast bed remains unchanged; Depend on t The characteristic values ​​of the track structure parameters at any given time are determined through software simulation. The track structure parameters include the structural parameters of the rails, fasteners, rail pads, concrete sleepers, prestressed steel strands, ballast, and subgrade. ； in, For static wheel weight, This is the wheel weight distribution coefficient. This is the comprehensive dynamic load factor.

6. The method for predicting the lifespan of heavy-haul railway sleepers according to claim 4, characterized in that, t The bearing bending moment of the track section and the bearing bending moment of the sleeper section at any given time must meet the requirements of the following formula. If they do not meet the requirements, the individual sleeper in the random sample is deemed to have failed. ； ； in, This is the load partial factor.

7. A device for predicting the lifespan of heavy-haul railway sleepers, characterized in that, The heavy-haul railway sleeper life prediction device includes: The initial feature value acquisition module for sleeper parameters is used to obtain the initial feature values ​​of the sleeper parameters of the target sleeper. The initial feature value acquisition module for track structure parameters is used to obtain the initial feature values ​​of the track structure parameters of the track corresponding to the target sleeper. The sleeper life prediction module is used to simulate the variation patterns of sleeper parameter characteristic values ​​and track structure parameter characteristic values ​​over a set total time period, based on the initial characteristic values ​​of sleeper parameters and track structure parameters. During the simulation, at set time steps, a set number of random samples composed of the characteristic values ​​of each sleeper parameter and each track structure parameter are generated based on the current time. The reliability value of the target sleeper at the current time is determined according to whether the individual sleeper corresponding to the random sample has failed. The reliability value at each set time step is compared with the reliability limit to determine the predicted life value of the target sleeper.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for predicting the life of heavy-haul railway sleepers according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for predicting the lifespan of heavy-haul railway sleepers as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for predicting the lifespan of heavy-haul railway sleepers as described in any one of claims 1-6.

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