A prediction method for the fatigue life of a wind barrier under the action of corrosion damage

Abstract

The present invention discloses a method for predicting the fatigue life of a wind barrier under the action of corrosion damage, which relates to the technical field of predicting the fatigue life of wind barriers for railway bridges, and includes the following steps: establishing a finite element model of steel with different corrosion pit sizes according to the material properties of the steel; obtaining the S-N curve of the steel with corrosion pits; establishing a finite element model of the wind barrier with corrosion pits; obtaining the stress time history curve of the wind barrier of a rusty railway bridge during service; obtaining the fatigue cycle times under different stress amplitudes through the rain flow counting method; calculating the linear cumulative damage of the wind barrier of the rusty railway bridge in one year through the Palmgren-Miner rule, and predicting the fatigue life of the wind barrier of the rusty railway bridge. The present invention adopts the above method for predicting the fatigue life of a wind barrier under the action of corrosion damage, and predicts the fatigue life from the perspective of the wind barrier structure, providing a scientific and reliable basis for the maintenance and replacement of wind barriers for railway bridges.

Description

Technical Field

[0001] The present invention relates to the technical field of fatigue life prediction of wind barriers for railway bridges, and particularly to a method for predicting the fatigue life of a wind barrier under the action of corrosion damage. Background Art

[0002] In the field of railway bridge engineering, especially for railway bridges under the coupling of natural random pulsating wind loads and a high-salt environment, the wind barrier, as a key facility to ensure the safety and comfort of train operation, the accurate prediction of its fatigue life is of great significance for the long-term stable operation of the bridge. The high-salt environment will accelerate the corrosion process of the wind barrier structure, greatly increasing the risk of its fatigue failure.

[0003] However, the existing fatigue life prediction technologies for wind barriers of railway bridges have obvious deficiencies when facing the special working condition of the coupling of natural random pulsating wind loads and a high-salt environment. Usually, when predicting the fatigue life, it mainly focuses on conventional load actions, material inherent properties, and structural forms, etc., but fails to fully consider the serious impact of the corrosion damage caused by the high-salt environment on the fatigue life of the wind barrier, resulting in a large deviation between the predicted fatigue life and the actual situation, and unable to accurately reflect the true fatigue condition of the wind barrier of the railway bridge under high-salt service conditions.

[0004] Therefore, researching and developing a fatigue life prediction technology method that can fully consider the influence of corrosion damage under the coupling of natural random pulsating wind loads and a high-salt environment has extremely important practical significance and urgency for improving the prediction accuracy of the fatigue life of wind barriers for railway bridges, ensuring the safe operation of railway bridges under the coupling of natural random pulsating wind loads and a high-salt environment, and realizing scientific and reasonable maintenance management. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for predicting the fatigue life of a wind barrier under the action of corrosion damage, to solve the technical problem of predicting the fatigue life of a corroded wind barrier of a railway bridge under the coupling of natural random pulsating wind loads and a high-salt environment. At the same time, predicting the fatigue life from the perspective of the wind barrier structure rather than from the perspective of the steel material properties is also more accurate, providing a scientific and reliable basis for the maintenance and replacement decision-making of the wind barrier of the railway bridge.

[0006] To achieve the above purpose, the present invention provides a method for predicting the fatigue life of a wind barrier under the action of corrosion damage, including the following steps:

[0007] S1. Establish a finite element model of steel with different corrosion pit sizes according to the material properties obtained from the uniaxial tensile experiment of Q235B steel;

[0008] S2. Introduce a corrosion pit size coefficient into the traditional S-N curve, and fit the S-N curve of the steel with corrosion pits by the least square method;

[0009] S3. Convert the S-N curve of steel with corrosion pits to the S-N curve of the wind barrier of a rusty railway bridge using the nominal stress method, and establish a finite element model of the wind barrier with corrosion pits.

[0010] S4. Simulate the random natural wind pulsation wind field acting on the wind barrier of a rusty railway bridge during service, and input the obtained natural wind pulsation wind field and the S-N curve of the wind barrier of a rusty railway bridge into the finite element model of the wind barrier of a rusty railway bridge to obtain the stress time history curve of the wind barrier of a rusty railway bridge during service.

[0011] S5. Obtain the fatigue cycle times at different stress amplitudes through the rain flow counting method.

[0012] S6. Calculate the linear cumulative damage of the wind barrier of a rusty railway bridge in one year through the Palmgren-Miner rule, and predict the fatigue life of the wind barrier of a rusty railway bridge.

[0013] Preferably, S1 includes the following steps:

[0014] S11. Fabricate a batch of standard tensile specimens of Q235B steel plates with a thickness of 8 mm and conduct uniaxial tensile tests.

[0015] S12. Obtain the material parameters of the steel through uniaxial tensile tests, including elastic modulus, Poisson's ratio, yield strength, ultimate strength, engineering stress, and engineering strain.

[0016] S13. Convert the engineering stress and engineering strain curves obtained from the uniaxial tensile test to true stress and strain through the following formula:

[0017] (1);

[0018] In the formula: is the true stress of the steel, is the engineering stress of the steel, is the true strain of the steel, is the engineering strain of the steel;

[0019] S14. Establish a three-dimensional finite element model of steel with corrosion pits having the same dimensions as the uniaxial tensile test, input the material parameters and the true stress-strain curve of the steel into the model, set the same boundary conditions and load values as the uniaxial tensile test, and set a reasonable mesh size.

[0020] Preferably, S2 includes the following steps:

[0021] S21. The general expression of the traditional S-N curve is:

[0022] (2);

[0023] Where: N is the fatigue life of the steel without corrosion pits, is the stress amplitude, , are material constants;

[0024] Considering that the exponential function shifts the S-N curve downward as a whole and also changes the change rate of the S-N curve, it is assumed that the relationship between the fatigue life of the steel with corrosion pits and the fatigue life without corrosion pits is as shown in Equation (3). Substituting Equation (2) into it, the unified S-N curve formula (4) considering the corrosion pit coefficient is obtained:

[0025] (3);

[0026] (4);

[0027] Where: is the fatigue life of the steel with corrosion pits, is the fatigue life of the steel without corrosion pits, is the corrosion pit coefficient, is the coefficient related to the stress amplitude;

[0028] S22. By changing the characteristic parameters of the corrosion pits, namely the corrosion depth H, the corrosion width W, the stress amplitude Sa, and the spacing L between the corrosion pits, in the finite element software, the post-processing results under different corrosion pit characteristic parameters are obtained, and the obtained post-processing results are respectively imported into the fatigue analysis software to calculate the fatigue life;

[0029] S23. According to the calculation results, the depth-width ratio η is introduced, and nonlinear fitting is performed using origin to finally obtain the S-N curve of the steel component with corrosion pits:

[0030] (5).

[0031] Preferably, in the fatigue life prediction model of the S3 structural member, the nominal stress method and the S-N curve are used. By changing the boundary conditions of the material finite element model, the S-N curve of the material is replaced with the S-N curve of the structure, and finite element calculation is performed to obtain the fatigue life prediction model applied to the wind barrier structural member.

[0032] Preferably, S4 includes the steps:

[0033] S41. Simulate the random natural wind pulsating wind pressure to obtain the wind pressure time history curves and power spectral density curves at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier respectively;

[0034] S42. Input the obtained natural wind pulsating wind field and the S-N curve of the wind barrier of the rusty railway bridge into the finite element model. Among them, the random natural wind pulsating wind pressure is loaded in a zoned manner, and the time history curves of the wind pressure at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier are respectively loaded at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier, and then the stress time history curve of the wind barrier of the rusty railway bridge during service is calculated.

[0035] Preferably, S41 includes the following steps:

[0036] S411. The wind speed spectrum adopts the Davenport pulsating wind speed spectrum, and its formula is:

[0037] (6);

[0038] In the formula: is the pulsating wind speed power spectrum; k is the ground roughness coefficient; is the average wind speed at a height of 10 m at this location; is the pulsating wind frequency; is the turbulence integral scale coefficient, ;

[0039] S412. According to the Wiener-Khintchine theorem and the pulsating wind pressure power spectrum formula, the power spectral density of the random natural wind pulsating wind pressure is deduced:

[0040] (7);

[0041] In the formula: is the power spectral density function of the random natural wind pulsating wind pressure time history; is the atmospheric density; is z the wind speed at a height of, , is the wind speed power exponent; is Z the wind pressure value at a height of, and other symbols are the same as those in the formula in step S411;

[0042] S413. According to the Shinozuka theory, the formula of the random natural wind pulsating wind pressure time history is expressed as follows:

[0043] (8);

[0044] In the formula: N is a sufficiently large positive integer; is the frequency increment; is a random variable uniformly distributed in the interval (0, 2). According to the central limit theorem, when N is large enough, the simulated random process approaches a Gaussian random process;

[0045] S414. Derive the time - history function of the pulsating wind load from the time - history formula of the random natural wind pulsating wind pressure:

[0046] (9);

[0047] In the formula: is the shape coefficient at height, is the windward area at height and is the random pulsating wind pressure at height.

[0048] Preferably, for the stress - time - history curve results of the rusty railway bridge wind barrier obtained in S42 during service, the rain - flow counting method is used to process the data to obtain the fatigue action times under different stress amplitudes .

[0049] Preferably, S6 includes the following steps:

[0050] S61. Calculate the fatigue life under different stress amplitudes from the S - N curve of the steel member with corrosion pits obtained in S4;

[0051] S62. Obtain the fatigue action times under different stress amplitudes from the data processed by the rain - flow counting method obtained in S5;

[0052] S63. Calculate according to the Palmgren - Miner rule according to the following formula:

[0053] (10);

[0054] (11);

[0055] In the formula: D is the fatigue cumulative damage, is the number of fatigue life cycles used by the structure under this cyclic load, , have the same meaning as above;

[0056] S64. Query the daily train schedule to find the number of shifts passing by the rusty railway bridge wind barrier per day, i.e., the daily fatigue cycle times , multiply this value by 365 to get the annual fatigue cycle times , and calculate the number of years the rusty railway bridge wind barrier is used under this cyclic load through the following formula:

[0057] ;

[0058] In the formula: is the number of years the wind barrier of the rusty railway bridge has been used under this cyclic load, is the number of fatigue life cycles of the structure under this cyclic load, is the annual fatigue cycle number.

[0059] Therefore, the present invention adopts the above-mentioned method for predicting the fatigue life of the wind barrier under the action of rust damage, solves the technical problem of predicting the fatigue life of the wind barrier of the rusty railway bridge under the coupling of natural random pulsating wind load and high-salt environment, and is more accurate in predicting the fatigue life from the perspective of the wind barrier structure rather than from the perspective of the steel material properties, which provides a scientific and reliable basis for the maintenance and replacement of the wind barrier of the railway bridge.

[0060] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0061] Figure 1 is a flowchart of a method for predicting the fatigue life of a wind barrier under the action of rust damage according to the present invention;

[0062] Figure 2 is a schematic diagram of the influence curve of different characteristic parameters on the fatigue life according to the present invention, (a) is the pitting depth, (b) is the pitting width, and (c) is the stress amplitude;

[0063] Figure 3 is the S-N curve of a steel member with corrosion pits provided by an embodiment of the present invention;

[0064] Figure 4 is the wind pressure time history curve at 1 m, 2 m, 3 m, and 4 m of the wind barrier according to the present invention, (a) is the wind pressure time history curve at 1 m, (b) is the wind pressure time history curve at 2 m, (c) is the wind pressure time history curve at 3 m, and (d) is the wind pressure time history curve at 4 m. Detailed Embodiments

[0065] The technical solution of the present invention will be further described below with reference to the drawings and embodiments.

[0066] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0067] Embodiment

[0068] Please refer to Figures 1-4 , the present invention provides a method for predicting the fatigue life of a wind barrier under the action of corrosion damage, including the following steps:

[0069] S1. First, according to the uniaxial tensile experiment of Q235B steel, material parameters such as the elastic modulus, Poisson's ratio, yield strength, ultimate strength, and stress-strain curve of the steel are obtained. These obtained material parameters are imported into finite element software to establish multiple three-dimensional finite element models of steel with corrosion pits of different sizes.

[0070] S11. For the material parameters in the three-dimensional finite element model of steel with corrosion pits, according to "Metallic materials - Tensile testing - Part 1: Method of test at ambient temperature", a batch of standard tensile specimens of 8 mm thick Q235B steel plates produced by a certain steel structure company are fabricated, and uniaxial tensile experiments are carried out in accordance with "Metallic materials - Fatigue testing - Method of axial force control".

[0071] S12. Parameters such as the elastic modulus, Poisson's ratio, yield strength, ultimate strength, engineering stress, and engineering strain of the steel are obtained through uniaxial tensile experiments.

[0072] The specific parameters are: elastic modulus 210 GPa, Poisson's ratio taken as 0.3, yield strength 235 MPa, and tensile strength 415 MPa.

[0073] S13. The engineering stress and engineering strain curves obtained from the uniaxial tensile experiment are converted into true stress and strain through the following formula. The conversion of engineering stress and strain into the true stress and strain curves actually input into the finite element software can be carried out by the following formula:

[0074] (1);

[0075] In the formula, is the true stress of the steel, is the engineering stress of the steel, is the true strain of the steel, is the engineering strain of the steel.

[0076] S14. Establish a three-dimensional finite element model of corroded steel with the same size as that in the uniaxial tensile test, and input the material parameters obtained from the uniaxial tensile test and the true stress-strain curve of the steel into the model. The boundary condition is set to be load-controlled in the way of reference points. Couple reference point 1 and reference point 2 with the surface of the specimen clamping end respectively. Set reference point 1 as a fully fixed constraint and reference point 2 as an axial tensile cyclic load. The load value is based on the statistical scheme and analysis method of fatigue test data of metallic materials. The loading is divided into 6 equally spaced stress levels, and the stress ratio is taken as 0, that is, the minimum stress is 0, and the applied stress magnitude is the stress amplitude magnitude. Use the loading coefficient to represent the relationship between the stress level applied to the model and the yield stress level. The loading coefficient is the ratio of the applied load to the yield load. .

[0077] Apply six different load levels to the model. The specific values are shown in Table 1 below. Considering the calculation time and accuracy comprehensively, set the element size of the parallel section of the specimen to be 2 mm and the size of other elements to be 4 mm. The mesh type is C3D8R.

[0078] Table 1 Loads applied to the three-dimensional finite element model of corroded steel

[0079] ;

[0080] S2. Obtain the fatigue life of corroded steel under different pitting depths, pitting widths, stress amplitudes, and corrosion pit spacings, and fit the relationship curves between different parameters and the fatigue life. It is not difficult to find that the pitting depth and pitting width have a greater impact on the fatigue life of the steel. Therefore, introduce the aspect ratio of the corrosion pit considering the corrosion pit size coefficient into the traditional S-N curve, and fit the S-N curve of the corroded steel component by the least square method.

[0081] S21. The general expression of the traditional S-N curve is:

[0082] (2);

[0083] where: N is the fatigue life of the steel without corrosion pits, is the stress amplitude, , are material constants.

[0084] Considering that the exponential function can not only shift the S-N curve downward as a whole but also change the variation rate of the S-N curve, it is assumed that the relationship between the fatigue life of steel with corrosion pits and the fatigue life without corrosion pits is as shown in Equation (3). Substituting Equation (2) into it, the unified S-N curve formula (4) considering the corrosion pit coefficient can be obtained:

[0085] (3);

[0086] (4);

[0087] In the formula: is the fatigue life of steel with corrosion pits, is the fatigue life of steel without corrosion pits, is the corrosion pit coefficient, is the coefficient related to the stress amplitude;

[0088] S22. In the finite element software, by changing the characteristic parameters of the corrosion pits, such as the corrosion depth L, corrosion width W, stress amplitude Sa, and the spacing L between corrosion pits, the post-processing results under different characteristic parameters of corrosion pits are obtained. Subsequently, the obtained post-processing result files are respectively imported into the fatigue analysis software to calculate the fatigue life. The calculation results and the specific values of the characteristic parameters are shown in Table 2 below.

[0089] Table 2 Characteristic parameters of corrosion pits and calculation results of fatigue life

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] S23. As shown in Figure 2 , according to the calculation results, it can be seen that the depth L of the corrosion pits, the width W of the corrosion pits, and the stress amplitude Sa have a greater impact on the fatigue life, while the spacing L between the corrosion pits has little impact on the fatigue life. Therefore, the depth-to-width ratio η is introduced, and the obtained data is non-linearly fitted with a fitting degree of Finally, the S-N curve of the steel component with corrosion pits as shown in Figure 3 can be obtained:

[0096] (5);

[0097] The S-N curve of the material can be converted into the S-N curve of the structure according to the following formula:

[0098] (6);

[0099] Wherein, is the stress of the structural S-N curve, is the stress of the material S-N curve, is the size coefficient, is the surface quality coefficient, is the loading method, is the fatigue notch coefficient of the structure.

[0100] (7);

[0101] Wherein, is a material constant, only related to the material strength; r is the hole radius, is the stress concentration coefficient.

[0102] S3. Convert the S-N curve of the steel with corrosion pits to the S-N curve of the rusty railway wind barrier by using the nominal stress method.

[0103] (8);

[0104] Wherein, is the local maximum stress in the finite element software, is the nominal stress.

[0105] S4. Simulate the random natural wind pulsation wind field received during the service of the rusty wind barrier, and input the obtained random natural wind pulsation wind field and the S-N curve of the rusty railway wind barrier into the finite element model to calculate the stress time history curve of the structure of the rusty railway wind barrier during service.

[0106] S41. When the wind barrier is subjected to wind load, the wind pressure received at each height is usually not equal. Therefore, simulate the random natural wind pulsation wind pressure, and obtain the wind pressure time history curves at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier as Figure 4 shown.

[0107] S411. The wind speed spectrum adopts the Davenport pulsating wind speed spectrum, and its formula is:

[0108] (9);

[0109] Wherein: is the pulsating wind speed power spectrum; k is the ground roughness coefficient; is the average wind speed at a height of 10 m at this location; is the pulsating wind frequency; xis the turbulent integral scale coefficient, .

[0110] S412. Deduce the power spectral density of the random natural wind pulsating wind pressure according to the Wiener-Khintchine theorem and the pulsating wind pressure power spectrum formula:

[0111] (10);

[0112] In the formula: is the power spectral density function of the random natural wind pulsating wind pressure time history; is the atmospheric density; is z the wind speed at height , is the wind speed power exponent; is Z the wind pressure value at height, and other symbols are the same as those in the formula of step S411.

[0113] S413. According to the Shinozuka theory, the random natural wind pulsating wind pressure time history formula is expressed as follows:

[0114] (11);

[0115] In the formula: N is a sufficiently large positive integer; is the frequency increment; is a random variable uniformly distributed in the interval (0, 2). According to the central limit theorem, when N is large enough, the simulated random process approaches a Gaussian random process.

[0116] S414. The pulsating wind load time history function can be deduced from the random natural wind pulsating wind pressure time history formula:

[0117] (12);

[0118] In the formula: is the shape coefficient at height is the windward area at height and is the random pulsating wind pressure at height.

[0119] S42. Input the obtained natural wind pulsation time history curve and the S-N curve of the wind barrier of the corroded railway bridge into the finite element model. When loading the random natural wind pulsation wind pressure, adopt the zonal loading method to load the wind pressure time history curves at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier respectively, and calculate the stress time history curve of the corroded railway wind barrier during its service life.

[0120] S5. Calculate the fatigue action times under different stress amplitudes through the rain-flow counting method.

[0121] Based on the stress time history curve results of the wind barrier of the corroded railway bridge obtained, calculate the fatigue action times under different stress amplitudes according to the rain-flow counting method where the fatigue action times corresponding to the stress amplitude is and the fatigue action times corresponding to the remaining stress amplitudes also correspond one by one to the fatigue action times.

[0122] S6. Calculate the linear cumulative damage of the wind barrier of the corroded railway bridge in one year through the Palmgren-Miner rule, so as to predict the fatigue life of the wind barrier of the corroded railway bridge.

[0123] S61. According to the S-N curve of the steel component with corrosion pits obtained in step S4, calculate the fatigue life under different stress amplitudes .

[0124] S62. Denote the fatigue action times under different stress amplitudes obtained after processing by the rain-flow counting method as .

[0125] S63. Calculate according to the Palmgren-Miner rule according to the following formula

[0126] (13);

[0127] (14);

[0128] where: D is the fatigue cumulative damage, is the number of fatigue life cycles that the structure can use under this cyclic load, , have the same meaning as above.

[0129] S64. Query the train schedule to query the number of daily trips passing by the wind barrier of the corroded railway bridge, that is, the daily fatigue cycle times , and multiply this value by 365 to obtain the annual fatigue cycle times , the number of years that the wind barrier of the rusty railway bridge can be used under this cyclic load can be calculated by the following formula:

[0130] (15);

[0131] In the formula is the number of years that the wind barrier of the rusty railway bridge can be used under this cyclic load, is the number of fatigue life cycles that the structure can be used under this cyclic load, is the annual fatigue cycle number.

[0132] Therefore, the present invention adopts the above-mentioned method for predicting the fatigue life of the wind barrier under the action of rust damage, solves the technical problem of predicting the fatigue life of the wind barrier of the rusty railway bridge under the coupling of natural random pulsating wind load and high-salt environment, and is more accurate to predict the fatigue life from the perspective of the wind barrier structure rather than from the perspective of the steel material properties, which provides a scientific and reliable basis for the maintenance and replacement of the wind barrier of the railway bridge.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions of the present invention or make equivalent replacements, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predicting the fatigue life of a wind barrier under the action of rust damage, characterized in that It includes the following steps: S1. Establish a finite element model of steel with different corrosion pit sizes according to the material properties obtained from the uniaxial tensile experiment of Q235B steel. S2. Introduce a corrosion pit size coefficient into the traditional S-N curve, and fit the S-N curve of steel with corrosion pits by the least square method. S3. Use the nominal stress method to convert the S-N curve of steel with corrosion pits into the S-N curve of the wind barrier of a rusty railway bridge, and establish a finite element model of the wind barrier with corrosion pits. S4. Simulate the random natural wind pulsation wind field suffered by the wind barrier of a rusty railway bridge during service, and input the obtained natural wind pulsation wind field and the S-N curve of the wind barrier of a rusty railway bridge into the finite element model of the wind barrier of a rusty railway bridge to obtain the stress time history curve of the wind barrier of a rusty railway bridge during service. S5. Obtain the fatigue cycle times under different stress amplitudes through the rain flow counting method. S6. Calculate the linear cumulative damage of the wind barrier of a rusty railway bridge in one year by the Palmgren-Miner rule, and predict the fatigue life of the wind barrier of a rusty railway bridge. S4 includes the steps of: S41. Simulate the random natural wind pulsation wind pressure, and respectively obtain the wind pressure time history curve and the power spectral density curve at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier. S42. Input the obtained natural wind pulsation wind field and the S-N curve of the wind barrier of a rusty railway bridge into the finite element model. When loading the random natural wind pulsation wind pressure, the wind pressure time history curves at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier are respectively loaded at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier in a zoned loading manner, and then calculate the stress time history curve of the wind barrier of a rusty railway bridge during service. S41 includes the following steps: S411. The wind speed spectrum adopts the Davenport pulsating wind speed spectrum, and its formula is: (6) In the formula: is the power spectrum of fluctuating wind speed; k is the ground roughness coefficient; is the average wind speed at a height of 10 m at this location; is the fluctuating wind frequency; x is the turbulence integral scale coefficient, ; S412. Deduce the random natural wind pulsation wind pressure power spectral density according to the Wiener-Khintchine theorem and the pulsating wind pressure power spectrum formula. （7） In the formula: is the power spectral density function of the time history of random natural wind pulsation wind pressure; is the atmospheric density; is the wind speed at height z, , is the wind speed power exponent; is the wind pressure value at height Z, and other symbols are the same as those in the formula in step S411; S413. According to the Shinozuka theory, the random natural wind pulsation wind pressure time history formula is expressed as follows: （8） Where: N is a sufficiently large positive integer; is the frequency increment; is a random variable uniformly distributed in the interval (0, 2). According to the central limit theorem, when N is large enough, the simulated random process approaches a Gaussian random process; S414. Deduce the pulsating wind load time history function from the random natural wind pulsation wind pressure time history formula. （9） In the formula: is the shape factor for height, is the windward area for height, and is the random fluctuating wind pressure for height.

2. The fatigue life prediction method of the wind barrier under the action of rust damage according to claim 1, characterized in that S1 includes the following steps: S11. Fabricate a batch of standard tensile specimens of 8-mm-thick Q235B steel plates and conduct uniaxial tensile experiments. S12. Obtain the material parameters of the steel through the uniaxial tensile experiment, including elastic modulus, Poisson's ratio, yield strength, ultimate strength, engineering stress, and engineering strain. S13. Convert the engineering stress and engineering strain curves obtained from the uniaxial tensile experiment into true stress and strain through the following formula: （1） In the formula: is the true stress of the steel,[ is the engineering stress of the steel,[ is the true strain of the steel,[ is the engineering strain of the steel; S14. Establish a three-dimensional finite element model of steel with corrosion pits having the same size as that in the uniaxial tensile experiment, input the material parameters and the true stress-strain curve of the steel into the model, set the same boundary conditions and load values as those in the uniaxial tensile experiment, and set a reasonable mesh size.

3. A method for predicting the fatigue life of a windbreak under the action of corrosion damage according to claim 2, characterized in that, S2 includes the following steps: S21. The general expression of the traditional S-N curve is: (2) Where: N is the fatigue life of the steel without corrosion pits, is the stress amplitude, , are material constants; Considering that the exponential function shifts the entire S-N curve downward and changes the variation rate of the S-N curve, it is assumed that the relationship between the fatigue life of steel with corrosion pits and the fatigue life without corrosion pits is as shown in Equation (3). Substituting Equation (2) into it, the unified S-N curve formula (4) considering the corrosion pit coefficient is obtained: (3) (4) In the formula: is the fatigue life of the steel with corrosion pits, is the fatigue life of the steel without corrosion pits, is the corrosion pit coefficient, is the coefficient related to the stress amplitude; S22. By changing the characteristic parameters of the corrosion pits, namely the corrosion depth H, the corrosion width W, the stress amplitude Sa, and the spacing L between the corrosion pits, in the finite element software, the post-processing results under different corrosion pit characteristic parameters are obtained. The obtained post-processing results are respectively imported into the fatigue analysis software to calculate the fatigue life; S23. Introduce the depth-width ratio η according to the calculation results and perform non-linear fitting using origin. Finally, the S-N curve of the steel member with corrosion pits is obtained: (5)。 4. A method for predicting the fatigue life of a windbreak under the action of corrosion damage according to claim 3, characterized in that: In the fatigue life prediction model of the S3 structural member, the nominal stress method and the S-N curve are used. By changing the boundary conditions of the material finite element model, the S-N curve of the material is replaced with the S-N curve of the structure, and finite element calculation is performed to obtain the fatigue life prediction model applied to the wind barrier structural member.

5. A method for predicting the fatigue life of a wind barrier under the action of corrosion damage according to claim 4, characterized in that: For the stress time history curve results of the rusty railway bridge wind barrier obtained in S42 during service, the rain-flow counting method is used to process the data to obtain the fatigue action times under different stress amplitudes , , … .​ 6. A method for predicting the fatigue life of a wind barrier under the action of corrosion damage according to claim 5, characterized in that S6 includes the following steps: S61. Based on the S-N curve of the steel component with corrosion pits obtained from S4, calculate the fatigue life under different stress amplitudes , , … ;​ S62. The fatigue action times under different stress amplitudes obtained after the rain flow counting method processing from S5 ; , , … ; S63. Calculate according to the Palmgren-Miner rule according to the following formula: (10) （11） Where: D is the cumulative fatigue damage, is the number of fatigue life cycles used by the structure under this cyclic load, , and the meanings are the same as above; S64. Query the daily fatigue cycle times of the shifts passing by the rusty railway bridge wind barrier according to the train operation schedule, and multiply this value by 365 to obtain the annual fatigue cycle times. Multiply this value by 365 to obtain the annual fatigue cycle times. Calculate the years of use of the rusty railway bridge wind barrier under this cyclic load through the following formula: （12） In the formula: is the number of years that the wind barrier of the rusty railway bridge has been used under this cyclic load, is the number of fatigue life cycles that the structure has been used under this cyclic load, is the annual fatigue cycle number.

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