Method for evaluating functional earthquake vulnerability of railway station

Abstract

The invention provides a railway station functional earthquake vulnerability assessment method. According to the method, a multi-degree-of-freedom concentrated mass shear layer simplified model is optimized through an iterative optimization framework based on a hunting algorithm, functional responses of stations in a large number of earthquake scenes are calculated in combination with a Monte Carlo method, a confidence rule base and an evidence reasoning algorithm, and a lognormal distribution cumulative density function model is adopted to fit a station vulnerability curve. The uncertainty of multiple aspects such as seismic oscillation, component equipment damage and function evaluation rules is comprehensively considered. According to the method, the function vulnerability of the high-speed railway station under different earthquake scenes can be accurately evaluated, the model calculation efficiency is greatly improved and the calculation cost is reduced by optimizing and simplifying the model, a scientific, reliable and efficient basis is provided for the earthquake resistance evaluation of the railway station, and the method has important practical application value and wide market prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of building structure seismic performance assessment, and in particular to a railway station function seismic vulnerability assessment method. Background Art

[0002] Seismic vulnerability analysis of structures is an important method for evaluating their seismic performance. However, for important public functional buildings such as railway stations, traditional structural vulnerability studies focus solely on the structure itself, failing to fully reflect the station's actual functional state after an earthquake. Existing technologies lack effective methods that comprehensively consider the uncertainties of multiple factors, including ground motion, structural components, nonstructural components, equipment, and functional assessment rules, and accurately assess the functional vulnerability of railway stations. Furthermore, while detailed finite element models offer accurate calculations, they are inefficient and unable to meet the demands of extensive dynamic response calculations. Summary of the Invention

[0003] The present invention aims to solve the problems in the prior art and proposes a method for assessing the seismic vulnerability of railway station functions.

[0004] The present invention is achieved through the following technical solutions. The present invention proposes a method for assessing the seismic vulnerability of railway station functions, the method comprising:

[0005] Data preparation: Based on the ground motion record set recommended by ATC-63 in FEMA P-695, screening work based on the target response spectrum was carried out;

[0006] Model parameter optimization based on the Cheetah algorithm: For the simplified multi-degree-of-freedom concentrated mass shear layer model of the station, the restoring force parameters are calibrated using the upper-to-lower isolation structure method;

[0007] Random scenario generation and functional status reasoning stage: The generated component equipment damage conditions are input into the established railway station function confidence rule library. Using the evidence reasoning algorithm, the functional status of the station under each random scenario is reasoned and judged, and the confidence level corresponding to the station being in normal operation, basic function operation, and out of operation under the damage condition is determined;

[0008] Vulnerability curve fitting: The probability of a station being in different functional states under different seismic peak accelerations is statistically analyzed, and the log-normal distribution cumulative density function model is used to fit the data to obtain the functional vulnerability curve of the railway station, which intuitively reflects the relationship between the station's functional state and seismic intensity.

[0009] Furthermore, the data preparation specifically includes: determining the key parameters of the target response spectrum, including spectral acceleration and characteristic period, based on the seismic design requirements of the railway station; comparing the vibration response spectrum curves of various locations with the target response spectrum, and selecting 11 seismic motions from the record set for subsequent incremental dynamic analysis of railway station functions.

[0010] Furthermore, during the calibration of the restoring force parameters, the two-story station structure is targeted. Specifically, the two-story station structure is first isolated from the overall model, and the acceleration response calculated from the fine model of the first-story structure under earthquake action is applied to the two-story structure as an excitation. On this basis, the Cheetah algorithm is used to adjust the restoring force parameters of the two-story structure, with the objective function of minimizing the difference between the displacement and inter-story displacement ratio of the two-story isolated simplified model and the corresponding parameters of the fine model. By continuously searching the parameter space until the two-story structure restoring force parameters that minimize the difference between the two are found, the calibration of the restoring force parameters of the two-story isolated structure is completed. After the calibration of the restoring force parameters of the two-story structure is completed, the parameter value is fixed, and the restoring force parameters of the first-story structure are optimized. Similarly, with the goal of minimizing the difference between the displacement and inter-story displacement ratio of the first-story simplified model and the fine model, the Cheetah algorithm is used to iteratively search and adjust the restoring force parameters of the first-story structure until the optimal parameter value is reached, thereby realizing the calibration of the restoring force parameters of the first-story structure, and then completing the calibration of the overall restoring force parameters of the simplified model of the multi-degree-of-freedom concentrated mass shear layer of the two-story station structure.

[0011] Furthermore, the parameter optimization process based on the Cheetah algorithm includes:

[0012] Initial population generation: At the beginning of the process, the number of external iterations is set to 0. First, a cheetah population is randomly generated. This population serves as the initial set of individuals for the algorithm search and represents the initial exploration position in the parameter space.

[0013] Internal iteration process: The internal iteration process includes entering the cheetah algorithm cycle, fitness evaluation, search phase, updating prey position, population mutation and internal iteration termination judgment;

[0014] External iteration process: The external iteration process includes the generation and input of a new population and the update of the number of external iterations and termination judgment; specifically, the generation and input of a new population: randomly generate a new population, and input the optimal solution obtained in the last external iteration as an individual into the new population, and continue to search for a better solution based on this; the update of the number of external iterations and termination judgment: add 1 to the number of external iterations, and then judge whether the set maximum number of external iterations is reached; if not, return to continue the internal iteration process; if reached, output the optimal solution of this external iteration, and this optimal solution is the optimal solution in all iterative processes, and the entire algorithm process ends here.

[0015] Furthermore, during the inner iteration process,

[0016] Enter the Cheetah algorithm loop: Enter the Cheetah algorithm module, start the internal iterative calculation, and set the initial value of the internal iteration number to 0;

[0017] Fitness evaluation: The fitness of each individual in the current cheetah population is evaluated. The objective function of the fitness evaluation is to optimize the inter-layer resilience parameters to ensure the matching between the simplified model and the refined model.

[0018] Search phase: includes acceleration phase, random search and dynamic adjustment;

[0019] Update prey position: Based on the results of the above search phase, update the position of the prey in the parameter space, which represents the potential optimal parameter solution;

[0020] Population mutation: Perform mutation operations on the cheetah population by randomly changing the parameter values of some individuals in the population to further increase the diversity of the population and prevent the algorithm from premature convergence;

[0021] Inner iteration termination judgment: Check whether the set maximum number of inner iterations has been reached; if not, the number of inner iterations is increased by 1, and the process returns to continue fitness evaluation and subsequent steps; if reached, the optimal solution in the current inner iteration process is output.

[0022] Furthermore, in the random scenario generation and functional state inference stage, the optimized interlayer restoring force parameters were applied to the simplified multi-degree-of-freedom concentrated mass shear layer model. For the selected 11 seismic motion strips, the seismic motion intensity gradient was extended to the range of 0.05g-1.5g. Opensees was used to calculate the seismic response of the station structure under different earthquake intensities, and the maximum acceleration and interlayer displacement ratio response parameters of each layer of the station structure were obtained.

[0023] Furthermore, in the random scenario generation and functional status reasoning stage, for each set of earthquake response results, the vulnerability data of station components and equipment were extracted from FEMA reports and the PACT tool. Combined with the vulnerability characteristics, the Monte Carlo method was used to randomly generate 1,000 sets of component damage status scenarios. Based on the confidence rule library of railway station functions, the station function of each scenario was calculated using an evidential reasoning algorithm.

[0024] Furthermore, the fitting formula is expressed as:

[0025]

[0026] Where F(x) is the earthquake vulnerability function of the railway station, which means that under the earthquake intensity x, the station performance is at or above F i FS is the post-earthquake functional status of the railway station; IM is the earthquake intensity parameter; F iis the performance index of the railway station, which is divided into normal operation i=0, basic function operation i=1, and shutdown i=2; Φ is the cumulative density function of the standard normal distribution; μ is the logarithmic mean of the seismic intensity; σ is the logarithmic standard deviation of the seismic intensity.

[0027] The present invention also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for assessing the seismic vulnerability of railway station functions when executing the computer program.

[0028] The present invention also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for assessing the seismic vulnerability of railway station functions.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] This paper proposes a method for assessing the functional seismic vulnerability of railway stations. This method uses the Cheetah algorithm to optimize a simplified multi-degree-of-freedom concentrated mass shear layer model through an iterative optimization framework. It then combines the Monte Carlo method, a confidence rule base, and an evidence-based reasoning algorithm to calculate the functional response of stations under a wide range of earthquake scenarios. The log-normal cumulative density function model is used to fit the station vulnerability curve, comprehensively considering uncertainties in seismic motion, component and equipment damage, and functional assessment rules. This method not only accurately assesses the functional vulnerability of high-speed railway stations under different earthquake scenarios, but also significantly improves model calculation efficiency and reduces computational costs by optimizing and simplifying the model. This method provides a scientific, reliable, and efficient basis for evaluating the seismic performance of railway stations, and has important practical application value and broad market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0032] Figure 1 This is a comparison chart of the response spectrum and the standard spectrum.

[0033] Figure 2 This is a flow chart of the isolation structure method for solving the restoring force parameters.

[0034] Figure 3 It is a flow chart of the iterative optimization method for solving the restoring force parameters.

[0035] Figure 4 This is a schematic diagram of the station function vulnerability curve. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0037] Combine Figures 1-4 The present invention proposes a method for assessing the seismic vulnerability of railway station functions, the method comprising:

[0038] 1. Data preparation: Based on the ground motion record set recommended by ATC-63 in FEMA P-695, screening based on the target response spectrum was carried out;

[0039] The data preparation is specifically as follows: based on the seismic design requirements of the railway station, the key parameters of the target response spectrum, including spectral acceleration and characteristic period, are determined; the vibration response spectrum curves of various locations are compared with the target response spectrum, and 11 seismic motions are selected from the record set for the subsequent incremental dynamic analysis of the railway station function. The selected average seismic wave response spectrum differs from the target response spectrum by no more than 20% at the period points of the main vibration modes of the structure, strictly meeting the requirements of the "Code for Seismic Design of Buildings" (GB50011-2010), ensuring the validity and applicability of the seismic motion data. Figure 1 shown.

[0040] 2. Model parameter optimization based on the Cheetah algorithm: For the simplified model of the multi-degree-of-freedom concentrated mass shear layer of the station, the restoring force parameters are calibrated using the upper-to-lower isolation structure method;

[0041] In the process of restoring force parameter calibration, the two-story station structure is targeted at: first, the two-story station structure is isolated from the overall model, and the acceleration response calculated by the fine model of the first-story structure under earthquake action is applied to the two-story structure as an excitation; on this basis, the cheetah algorithm is used to adjust the restoring force parameters of the two-story structure, with the objective function of minimizing the difference between the displacement and inter-story displacement ratio of the two-story isolated simplified model and the corresponding parameters of the fine model. By continuously searching the parameter space until the two-story structure restoring force parameters that minimize the difference between the two are found, the calibration of the restoring force parameters of the two-story isolated structure is completed; after completing the calibration of the restoring force parameters of the two-story structure, the parameter value is fixed, and then the restoring force parameters of the first-story structure are optimized; similarly, with the objective of minimizing the difference between the displacement and inter-story displacement ratio of the first-story simplified model and the fine model, the cheetah algorithm is used to iteratively search and adjust the restoring force parameters of the first-story structure until the optimal parameter value is reached, thereby realizing the calibration of the restoring force parameters of the first-story structure, and then completing the calibration of the overall restoring force parameters of the simplified model of the multi-degree-of-freedom concentrated mass shear layer of the two-story station structure, as shown in Figure 2. Figure 2 shown.

[0042] Considering the high randomness of the solution quality of the heuristic algorithm, an iterative optimization method is used in each parameter calibration process. During each iteration, the Cheetah algorithm is activated to explore the current parameter search space and search for the optimal solution for that iteration.

[0043] The parameter optimization process based on the Cheetah algorithm includes:

[0044] Initial population generation: At the beginning of the process, the number of external iterations is set to 0. First, a cheetah population is randomly generated. This population serves as the initial set of individuals for the algorithm search and represents the initial exploration position in the parameter space.

[0045] Internal iteration process: The internal iteration process includes entering the cheetah algorithm cycle, fitness evaluation, search phase, updating prey position, population mutation and internal iteration termination judgment;

[0046] Enter the Cheetah algorithm loop: Enter the Cheetah algorithm module, start the internal iterative calculation, and set the initial value of the internal iteration number to 0;

[0047] Fitness evaluation: The fitness of each individual in the current cheetah population (i.e., each possible parameter solution) is evaluated. Fitness evaluation is based on a specific objective function, which is used to measure the performance of each individual in solving the problem. The objective function is defined as the degree to which the simplified model matches the refined model by optimizing the inter-layer resilience parameters.

[0048] The search phase includes an acceleration phase, random search, and dynamic adjustment. During the acceleration phase, individual cheetahs conduct an accelerated search within the search space, simulating their rapid approach to prey. Using specific mathematical rules, the cheetahs move toward potentially optimal solutions within the parameter space. After the acceleration phase, a random search is performed, introducing a degree of randomness to prevent the algorithm from becoming trapped in a local optimum and allowing for a more extensive exploration of the parameter space. Dynamic adjustment dynamically adjusts the search strategy and parameters based on the search results and the current population state to optimize subsequent search directions and step sizes.

[0049] Update prey position: Based on the results of the above search phase, update the position of the prey in the parameter space, which represents the potential optimal parameter solution;

[0050] Population mutation: Perform mutation operations on the cheetah population by randomly changing the parameter values of some individuals in the population to further increase the diversity of the population and prevent the algorithm from premature convergence;

[0051] Inner iteration termination judgment: Check whether the set maximum number of inner iterations has been reached; if not, the number of inner iterations is increased by 1, and the process returns to continue fitness evaluation and subsequent steps; if reached, the optimal solution in the current inner iteration process is output.

[0052] External iteration process: The external iteration process includes the generation and input of a new population and the update of the number of external iterations and termination judgment; specifically, the generation and input of a new population: randomly generate a new population, and input the optimal solution obtained in the last external iteration as an individual into the new population, and continue to search for a better solution based on this; the update of the number of external iterations and termination judgment: add 1 to the number of external iterations, and then judge whether the set maximum number of external iterations is reached; if not, return to continue the internal iteration process; if reached, output the optimal solution of this external iteration, and this optimal solution is the optimal solution in all iterative processes, and the entire algorithm process ends here.

[0053] 3. Random Scenario Generation and Functional Status Reasoning Phase: The generated component equipment damage conditions are input into the established railway station function confidence rule library. Using the evidence reasoning algorithm, the functional status of the station under each random scenario is reasoned and judged, and the confidence level corresponding to the station being in normal operation, basic function operation, and out of operation under the damage condition is determined.

[0054] During the random scenario generation and functional state inference stage, the optimized interlayer restoring force parameters were applied to a simplified multi-degree-of-freedom concentrated mass shear layer model. For the 11 selected seismic motion strips, the seismic motion intensity gradient was extended to the range of 0.05g-1.5g. Opensees was used to calculate the seismic response of the station structure under different earthquake intensities, and the maximum acceleration and interlayer displacement ratio response parameters of each layer of the station structure were obtained.

[0055] During the random scenario generation and functional state inference phase, vulnerability data for station components and equipment was extracted from FEMA reports and the PACT tool for each set of earthquake response results. Combining these vulnerability characteristics, the Monte Carlo method was used to randomly generate 1,000 sets of component and equipment damage state scenarios. Based on a confidence rule base for railway station functions, an evidential reasoning algorithm was used to calculate the station function for each scenario. The scenario generation process fully considers the different failure modes and probabilities of structural and nonstructural components and equipment, ensuring that the generated scenarios comprehensively and realistically simulate various random failure combinations.

[0056] 4. Fragility curve fitting: Statistically calculate the probability of the station being in different functional states under different earthquake peak accelerations, and fit the data using the log-normal distribution cumulative density function model to obtain the functional fragility curve of the railway station, which intuitively reflects the relationship between the station's functional state and earthquake intensity. Figure 4 shown.

[0057] The fitting formula is expressed as:

[0058]

[0059] Where F(x) is the earthquake vulnerability function of the railway station, which means that under the earthquake intensity x, the station performance is at or above F i FS is the post-earthquake functional status of the railway station; IM is the seismic intensity parameter, such as PGA and the first cycle seismic influence coefficient of the structure; F i is the performance index of the railway station, which is divided into normal operation i=0, basic function operation i=1, and shutdown i=2; Φ is the cumulative density function of the standard normal distribution; μ is the logarithmic mean of the seismic intensity; σ is the logarithmic standard deviation of the seismic intensity.

[0060] The present invention also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for assessing the seismic vulnerability of railway station functions when executing the computer program.

[0061] The present invention also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for assessing the seismic vulnerability of railway station functions.

[0062] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DRRAM). It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0063] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0064] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0065] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0066] The above is a detailed introduction to the railway station functional seismic vulnerability assessment method proposed in the present invention. Specific examples are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core concept of the present invention. At the same time, for those skilled in the art, according to the concept of the present invention, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for assessing the seismic vulnerability of railway station functions, characterized in that: The method comprises: Data preparation: Based on the ground motion record set recommended by ATC-63 in FEMA P-695, screening work based on the target response spectrum was carried out; Model parameter optimization based on the Cheetah algorithm: For the simplified multi-degree-of-freedom concentrated mass shear layer model of the station, the restoring force parameters are calibrated using the upper-to-lower isolation structure method; Random scenario generation and functional status reasoning stage: The generated component equipment damage conditions are input into the established railway station function confidence rule library. Using the evidence reasoning algorithm, the functional status of the station under each random scenario is reasoned and judged, and the confidence level corresponding to the station being in normal operation, basic function operation, and out of operation under the damage condition is determined; Vulnerability curve fitting: The probability of a station being in different functional states under different seismic peak accelerations is statistically analyzed, and the log-normal distribution cumulative density function model is used to fit the data to obtain the functional vulnerability curve of the railway station, which intuitively reflects the relationship between the station's functional state and seismic intensity.

2. The method according to claim 1, characterized in that The data preparation specifically involves determining key parameters of the target response spectrum, including spectral acceleration and characteristic period, based on the seismic design requirements of railway stations. The vibration response spectrum curves of various locations are compared with the target response spectrum, and 11 seismic motions are selected from the record set for subsequent incremental dynamic analysis of railway station functions.

3. The method according to claim 1, characterized in that During the calibration of the restoring force parameters, the two-story station structure is calibrated. Specifically, the two-story station structure is first isolated from the overall model, and the acceleration response calculated from the fine model of the first-story structure under earthquake action is applied to the two-story structure as an excitation. On this basis, the Cheetah algorithm is used to adjust the restoring force parameters of the two-story structure. The objective function is to minimize the difference between the displacement and inter-story displacement ratio of the two-story isolated simplified model and the corresponding parameters of the fine model. By continuously searching the parameter space until the two-story structure restoring force parameters that minimize the difference between the two are found, the calibration of the restoring force parameters of the two-story isolated structure is completed. After the calibration of the restoring force parameters of the two-story structure is completed, the parameter value is fixed, and the restoring force parameters of the first-story structure are optimized. Similarly, with the goal of minimizing the difference between the displacement and inter-story displacement ratio of the first-story simplified model and the fine model, the Cheetah algorithm is used to iteratively search and adjust the restoring force parameters of the first-story structure until the optimal parameter value is reached, thereby realizing the calibration of the restoring force parameters of the first-story structure, and then completing the calibration of the overall restoring force parameters of the simplified model of the multi-degree-of-freedom concentrated mass shear layer of the two-story station structure.

4. The method according to claim 3, characterized in that The parameter optimization process based on the Cheetah algorithm includes: Initial population generation: At the beginning of the process, the number of external iterations is set to 0. First, a cheetah population is randomly generated. This population serves as the initial set of individuals for the algorithm search and represents the initial exploration position in the parameter space. Internal iteration process: The internal iteration process includes entering the cheetah algorithm cycle, fitness evaluation, search phase, updating prey position, population mutation and internal iteration termination judgment; External iteration process: The external iteration process includes the generation and input of a new population and the update of the number of external iterations and termination judgment; specifically, the generation and input of a new population: randomly generate a new population, and input the optimal solution obtained in the last external iteration as an individual into the new population, and continue to search for a better solution based on this; the update of the number of external iterations and termination judgment: add 1 to the number of external iterations, and then judge whether the set maximum number of external iterations is reached; if not, return to continue the internal iteration process; if reached, output the optimal solution of this external iteration, and this optimal solution is the optimal solution in all iterative processes, and the entire algorithm process ends here.

5. The method according to claim 4, characterized in that During the inner iteration process, Enter the Cheetah algorithm loop: Enter the Cheetah algorithm module, start the internal iterative calculation, and set the initial value of the internal iteration number to 0; Fitness evaluation: The fitness of each individual in the current cheetah population is evaluated. The objective function of the fitness evaluation is to optimize the inter-layer resilience parameters to ensure the matching between the simplified model and the refined model. Search phase: includes acceleration phase, random search and dynamic adjustment; Update prey position: Based on the results of the above search phase, update the position of the prey in the parameter space, which represents the potential optimal parameter solution; Population mutation: Perform mutation operations on the cheetah population by randomly changing the parameter values of some individuals in the population to further increase the diversity of the population and prevent the algorithm from premature convergence; Inner iteration termination judgment: Check whether the set maximum number of inner iterations has been reached; if not, the number of inner iterations is increased by 1, and the process returns to continue fitness evaluation and subsequent steps; if reached, the optimal solution in the current inner iteration process is output.

6. The method according to claim 2, characterized in that During the random scenario generation and functional state inference stage, the optimized interlayer restoring force parameters were applied to a simplified multi-degree-of-freedom concentrated mass shear layer model. For the 11 selected seismic motion strips, the seismic motion intensity gradient was extended to the range of 0.05g-1.5g. Opensees was used to calculate the seismic response of the station structure under different earthquake intensities, and the maximum acceleration and interlayer displacement ratio response parameters of each layer of the station structure were obtained.

7. The method according to claim 6, characterized in that During the random scenario generation and functional status reasoning stage, for each set of earthquake response results, vulnerability data of station components and equipment were extracted from FEMA reports and the PACT tool. Combined with the vulnerability characteristics, the Monte Carlo method was used to randomly generate 1,000 sets of damage status scenarios for component equipment. The station function for each scenario was calculated using an evidential reasoning algorithm based on the confidence rule library of railway station functions.

8. The method according to claim 1, characterized in that The fitting formula is expressed as: Where F(x) is the earthquake vulnerability function of the railway station, which means that under the earthquake intensity x, the station performance is at or above F i FS is the post-earthquake functional status of the railway station; IM is the earthquake intensity parameter; F i is the performance index of the railway station, which is divided into normal operation i=0, basic function operation i=1, and stop operation i=2; Φ is the cumulative density function of the standard normal distribution; μ is the logarithmic mean of the earthquake intensity; σ is the logarithmic standard deviation of the earthquake intensity.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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