Layer shear model parameter calibration method based on quasi-static analysis and optimization algorithm

Abstract

The invention provides a layer shear model parameter calibration method based on quasi-static analysis and an optimization algorithm. According to the method, layer-by-layer quasi-static analysis is carried out on a fine finite element model of a building to obtain an interlayer hysteresis curve of each layer of the building, and then parameters of a four-broken-line hysteresis model are calibrated based on a particle swarm optimization algorithm and the interlayer hysteresis curves to establish an interlayer hysteresis model of each layer of a building structure. According to the method, the mass of each floor of the building is simplified into a mass point, the mass points are connected layer by layer through an interlayer hysteresis model, and a high-fidelity MDOF model corresponding to a fine finite element model is established, so that the efficiency of building earthquake damage simulation is greatly improved on the premise of keeping the calculation precision.

Description

Technical Field

[0001] This invention relates to the field of building earthquake damage simulation technology, and in particular to a method for calibrating the parameters of a layer shear model based on quasi-static analysis and optimization algorithms. Background Technology

[0002] Building seismic damage simulation is currently a core tool in performance-based seismic design of structures. Engineers rely on seismic damage simulation results to assess the damage level and performance of buildings under earthquakes of varying intensities, thereby optimizing design schemes to ensure they meet pre-set safety objectives. Furthermore, for existing urban building complexes, reliable seismic damage simulation is a crucial basis for conducting regional seismic risk assessments, identifying high-risk buildings, and developing scientific reinforcement and retrofit strategies. Therefore, reliable building seismic damage simulation is a fundamental prerequisite for ensuring the scientific validity of engineering design, disaster assessment, and retrofit decisions, helping to minimize casualties and economic losses caused by earthquakes.

[0003] Currently, numerical simulation is commonly used to obtain the seismic response of buildings under earthquake motion and to assess their post-earthquake damage. Numerical simulation is a physics-driven method that obtains the seismic response of building structures by establishing finite element models of the structures and performing dynamic analysis under seismic excitation, thereby assessing the post-earthquake damage state of the buildings. The modeling method of refined finite element models has been widely validated by academia and engineering and is considered a high-fidelity model that can simulate the damage of buildings under earthquakes with sufficient accuracy. However, refined finite element models require multiple assembly of system matrices, including mass, stiffness, and damping matrices, and iterative calculations at each time step to solve the structural motion equations, resulting in high computational costs and extremely low efficiency. Therefore, the refined finite element model can be appropriately simplified to improve computational efficiency. The commonly used simplified building analysis model is the multi-free layer shear (MDOF) model. This model concentrates and simplifies the mass of each layer into a single mass point, and then defines a shear spring as the layer hysteresis model. However, the computational accuracy of the simplified model is often significantly different from that of the refined finite element model. The main reasons are: (1) the commonly used three-segment model is difficult to fully characterize the bearing capacity curve of the structure; (2) the existing method of calibrating the layer hysteresis model parameters based on statistical data and manual selection is difficult to reflect the actual relationship between inter-story shear force and inter-story displacement.

[0004] In summary, existing methods struggle to balance accuracy and computational efficiency. Improving current parameter calibration methods to enhance computational accuracy would enable high-precision and high-efficiency simulation of building earthquake damage, allowing for rapid post-earthquake assessment of building damage and remaining functionality. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of high computational cost and low efficiency of existing refined finite element models, and insufficient computational accuracy of simplified finite element models. It proposes a parameter calibration method for layer shear models based on quasi-static analysis and optimization algorithms.

[0006] This invention is achieved through the following technical solution: This invention proposes a method for calibrating the parameters of a layer shear model based on quasi-static analysis and optimization algorithms, the method comprising: Step 1: Establish a detailed finite element model of the building, perform a layer-by-layer quasi-static analysis on the detailed finite element model, and obtain the inter-story hysteresis curves of each floor of the building. Step 2: Determine the location of the cracking point based on the period of the refined finite element model and the set limit of the elastic interlayer displacement ratio. Take the point corresponding to the peak value of the interlayer shear force on the interlayer hysteresis curve as the peak point. Then determine the yield point and the ultimate point based on the farthest point method and the peak bearing capacity reduction method. Step 3: Based on the feature points determined in Step 2 and the particle swarm optimization algorithm, calibrate the hysteresis parameters of the four-segment hysteresis model, thereby minimizing the error between the hysteresis curve corresponding to the four-segment hysteresis model and the actual value; Step 4: Based on the mass of each floor of the building, the feature points determined in Step 2, and the hysteresis parameters in Step 3, establish an MDOF model. Compare the response and failure state of the MDOF model and the refined finite element model under various earthquake levels, evaluate its calculation accuracy, and compare it with the calculation accuracy of the MDOF model established based on the three-segment hysteresis model.

[0007] Furthermore, in step 2, the four-segment hysteresis model Pinching4 from OpenSees is used as the interlayer hysteresis model. The Pinching4 model defines the skeleton curve through the crack point, yield point, peak point, and limit point.

[0008] Furthermore, in step 2, the inter-story displacement corresponding to the crack point is set to 1 / 1000 of the story height. The tangent stiffness of each story is determined according to the hysteresis curve of each story, and the ratio of the stiffness of each story is calculated. While keeping the stiffness ratio of each story unchanged, the tangent stiffness is adjusted so that the first period of the MDOF model is consistent with the fine finite element model. The product of the adjusted initial stiffness and the inter-story displacement of the crack point is the inter-story shear force corresponding to the crack point.

[0009] Furthermore, in step 3, the mean square error between the hysteresis curve corresponding to the “Pinching4” model and the hysteresis curve obtained from the quasi-static analysis is used as the loss function. The particle swarm optimization algorithm is used to calibrate the parameters of the “Pinching4” model, including the three parameters that define the unloading-reloading curve, namely rDisp_p, rForce_p and uForce_p, and the five parameters that define the unloading stiffness degradation, namely gd1, gd2, gd3, gd4 and gdL. Other parameters use default values.

[0010] Furthermore, in step 3, the particle swarm optimization algorithm optimizes the parameters by minimizing the loss function, thereby obtaining the optimal hysteresis parameter values.

[0011] Furthermore, in step 4, based on the mass of each floor of the building and the feature points and hysteresis parameters determined in steps 2 and 3, an MDOF model is established using the "Two Node Link Element" unit and "Pinching4" material of OpenSees.

[0012] Further, in step 4, 20 ground motion records are selected and scaled to adjust their peak ground acceleration to specific levels, including 0.05g, 0.1g, 0.2g, 0.3g, 0.4g, 0.5g, 0.6g, 0.7g, and 0.8g; using the amplitude-modulated ground motion records as external excitation, dynamic analysis is performed on the MDOF model and the refined finite element model to obtain the maximum inter-story drift angle of the building and assess the seismic damage to the building.

[0013] Furthermore, in step 4, the evaluation criteria include: using the inter-story drift angles corresponding to the cracking point, yield point, peak point, and limit point as the limits for minor damage (SD), moderate damage (MD), severe damage (ED), and complete damage (CD) of the building, respectively. When the inter-story drift angle is less than the value corresponding to the cracking point, the structure is determined to be in a basically intact (ND) state.

[0014] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the layer shear model parameter calibration method based on quasi-static analysis and optimization algorithm.

[0015] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the layer shear model parameter calibration method based on quasi-static analysis and optimization algorithm.

[0016] The beneficial effects of this invention are: 1. High calculation accuracy: Based on the calculation results of the fine finite element model, the MDOF model established by the proposed method has an error of less than 20% in calculating the seismic response of buildings, which is 14.3 percentage points higher than the accuracy of the existing MDOF model based on the three-segment hysteresis model in earthquake damage simulation.

[0017] 2. High computational efficiency: Compared with the refined finite element model based on fiber beam-column elements, the MDOF model established by the proposed method has a computational efficiency that is more than 100 times higher. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method for calibrating the parameters of a layer shear model based on quasi-static analysis and optimization algorithms as described in this invention.

[0020] Figure 2 This is a flowchart for layer-by-layer pseudostatic analysis.

[0021] Figure 3 This is a schematic diagram of the MDOF model and the four-segment hysteresis model.

[0022] Figure 4 A schematic diagram illustrating the method for determining the yield point and limit point.

[0023] Figure 5 The diagram shows the error distribution of the inter-story drift angle calculation for the MDOF model established based on the existing method (three-segment hysteresis model) and the proposed method (four-segment hysteresis model).

[0024] Figure 6 A schematic diagram illustrating the simulation accuracy of building seismic damage using MDOF models based on three-segment and four-segment hysteresis models is presented. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] This invention addresses the problems of high computational cost and low efficiency of existing refined finite element models, while simplified finite element models suffer from insufficient computational accuracy. It proposes a method for calibrating layer shear model parameters based on quasi-static analysis and optimization algorithms. This method involves performing layer-by-layer quasi-static analysis on a refined finite element model of a building to obtain the inter-story hysteresis curves (inter-story shear force-inter-story displacement relationship) for each floor. Then, based on particle swarm optimization and the inter-story hysteresis curves, the parameters of a four-segment hysteresis model are calibrated to establish the inter-story hysteresis models for each floor of the building structure. The mass of each floor is simplified to a point mass and connected layer by layer through the inter-story hysteresis models to establish a high-fidelity MDOF model corresponding to the refined finite element model, thereby significantly improving the efficiency of building seismic damage simulation while maintaining computational accuracy. This invention is applicable to buildings with regular structural layouts and minimal torsional effects.

[0027] Specifically, in combination Figures 1 to 6 This invention proposes a method for calibrating parameters of a layer shear model based on quasi-static analysis and optimization algorithms. The method includes: Step 1: Establish a detailed finite element model of the building, perform a layer-by-layer quasi-static analysis on the detailed finite element model, and obtain the inter-story hysteresis curves of each floor of the building. Step 2: Determine the location of the cracking point based on the period of the refined finite element model and the set limit of the elastic interlayer displacement ratio. Take the point corresponding to the peak value of the interlayer shear force on the interlayer hysteresis curve as the peak point. Then determine the yield point and the ultimate point based on the farthest point method and the peak bearing capacity reduction method. Step 3: Based on the feature points determined in Step 2 and the particle swarm optimization algorithm, calibrate the hysteresis parameters of the four-segment hysteresis model, thereby minimizing the error between the hysteresis curve corresponding to the four-segment hysteresis model and the actual value; Step 4: Based on the mass of each floor of the building, the feature points determined in Step 2, and the hysteresis parameters in Step 3, establish an MDOF model. Compare the response and failure state of the MDOF model and the refined finite element model under various earthquake levels, evaluate its calculation accuracy, and compare it with the calculation accuracy of the MDOF model established based on the three-segment hysteresis model.

[0028] In step 2, the four-segment hysteresis model Pinching4 from OpenSees is used as the interlayer hysteresis model. The Pinching4 model defines the skeleton curve through the crack point, yield point, peak point and limit point.

[0029] In step 2, the inter-story displacement corresponding to the crack point is set to 1 / 1000 of the story height. The tangent stiffness of each story is determined according to the hysteresis curve of each story, and the stiffness ratio of each story is calculated. While keeping the stiffness ratio of each story unchanged, the tangent stiffness is adjusted so that the first period of the MDOF model is consistent with the fine finite element model. The product of the adjusted initial stiffness and the inter-story displacement of the crack point is the inter-story shear force corresponding to the crack point.

[0030] In step 3, the mean square error between the hysteresis curve corresponding to the "Pinching4" model and the hysteresis curve obtained from the quasi-static analysis is used as the loss function. The particle swarm optimization algorithm is used to calibrate the parameters of the "Pinching4" model, including the three parameters that define the unloading-reloading curve, namely rDisp_p, rForce_p and uForce_p, and the five parameters that define the unloading stiffness degradation, namely gd1, gd2, gd3, gd4 and gdL. The other parameters use the default values.

[0031] In step 3, the particle swarm optimization algorithm optimizes the parameters by minimizing the loss function, thereby obtaining the optimal hysteresis parameter values.

[0032] In step 4, based on the mass of each floor of the building and the feature points and hysteresis parameters determined in steps 2 and 3, an MDOF model is established using the "Two Node Link Element" unit and "Pinching4" material in OpenSees.

[0033] In step 4, 20 ground motion records are selected and scaled to adjust their peak ground acceleration to specific levels, including 0.05g, 0.1g, 0.2g, 0.3g, 0.4g, 0.5g, 0.6g, 0.7g, and 0.8g. Using the amplitude-modulated ground motion records as external excitation, dynamic analysis is performed on the MDOF model and the refined finite element model to obtain the maximum inter-story drift angle of the building and assess the seismic damage to the building.

[0034] In step 4, the evaluation criteria include: using the inter-story drift angles corresponding to the cracking point, yield point, peak point, and limit point as the limits for minor damage (SD), moderate damage (MD), severe damage (ED), and complete damage (CD) of the building, respectively. When the inter-story drift angle is less than the value corresponding to the cracking point, the structure is determined to be in the basically intact (ND) state.

[0035] Example This invention proposes a method for calibrating parameters of a layer shear model based on quasi-static analysis and optimization algorithms. The method specifically includes: Step 1: This invention is verified using a 4-story concrete frame structure. The structure has a floor height of 3.9m, 3 spans both horizontally and vertically, with horizontal and vertical spans of 7.5m and 4.5m respectively. The column cross-section dimensions are 650 mm × 650 mm, and the cross-section dimensions of the horizontal and vertical beams are 300 mm × 550 mm and 250 mm × 500 mm respectively. The seismic fortification intensity is 8 degrees (0.30g). Structural reinforcement was determined using commercial software, and a detailed model of the building was created using the open-source finite element analysis software OpenSees. Fiber-coated beams and columns were used, with the element type being "Displacement-Based Beam-ColumnElement". Rigid floor slabs were set using the "RigidDiaphragm" command. Then, a layer-by-layer quasi-static analysis was performed on the detailed finite element model to obtain the inter-story hysteresis curves for each floor. The flowchart of the layer-by-layer quasi-static analysis is shown below. Figure 2 .

[0036] Step 2: The four-segment hysteresis model (Pinching4) of OpenSees is used as the interlayer hysteresis model. The "Pinching4" model defines the skeleton curve through the cracking point, yield point, peak point, and limit point. See details. Figure 3 The inter-story displacement corresponding to the cracking point is set to 1 / 1000 of the story height. The tangential stiffness of each story is determined based on its hysteresis curve, and the stiffness ratio of each story is calculated. While maintaining the stiffness ratio relationship between stories, the tangential stiffness is adjusted to ensure the first-order period of the MDOF model is consistent with the refined finite element model. The product of the adjusted initial stiffness and the inter-story displacement at the cracking point is the inter-story shear force corresponding to the cracking point. The point corresponding to the peak value of the inter-story shear force on the inter-story hysteresis curve is taken as the peak point, and then based on... Figure 4 The farthest point method and the peak bearing capacity reduction method are used to determine the yield point and the ultimate point.

[0037] Step 3: Using the mean square error between the hysteresis curve corresponding to the "Pinching4" model and the hysteresis curve obtained from the quasi-static analysis as the loss function, the parameters of the "Pinching4" model are calibrated using the particle swarm optimization algorithm. This includes defining three parameters (rDisp_p, rForce_p, and uForce_p) for the unloading-reloading curve and five parameters (gd1, gd2, gd3, gd4, and gdL) for the unloading stiffness degradation. Other parameters use default values. The particle swarm optimization algorithm performs efficient parameter optimization by minimizing the loss function, thereby obtaining the optimal hysteresis parameter values.

[0038] Step 4: Based on the mass of each floor of the building and the characteristic points and hysteresis parameters determined in Steps 2 and 3, an MDOF model is established using the "Two Node Link Element" element and "Pinching4" material in OpenSees. Twenty ground motion records are selected and scaled to adjust their peak ground acceleration to specific levels, including 0.05g, 0.1g, 0.2g, 0.3g, 0.4g, 0.5g, 0.6g, 0.7g, and 0.8g. Using the amplitude-modulated ground motion records as external excitation, dynamic analysis is performed on the MDOF model and the refined finite element model to obtain the maximum inter-story drift angle of the building and assess the seismic damage. The specific criteria are as follows: the inter-story drift angles corresponding to the crack point, yield point, peak point, and limit point are used as the limits for minor damage (SD), moderate damage (MD), severe damage (ED), and complete damage (CD) of the building, respectively. When the inter-story drift angle is less than the value corresponding to the crack point, the structure is judged to be in a basically intact (ND) state. Based on the calculation results of the refined finite element model, the accuracy of earthquake damage simulation of the MDOF model established by the proposed method is evaluated. The accuracy of the proposed method is compared with that of the MDOF model established using the three-segmented hysteretic model of yield point, peak point, and limit point (i.e., the "Hysteretic" model in OpenSees), to assess the improvement in calculation accuracy compared to existing technologies. A confusion matrix is ​​used to evaluate the accuracy of earthquake damage monitoring. The confusion matrix includes three indices: Recall, Precision, and Accuracy, as shown in equations (1)-(3): (1) (2) (3) TP: Positive class is classified as positive; FN: Positive class is classified as false; FP: False class is classified as positive; TN: False class is classified as false.

[0039] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the layer shear model parameter calibration method based on quasi-static analysis and optimization algorithm.

[0040] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the layer shear model parameter calibration method based on quasi-static analysis and optimization algorithm.

[0041] The memory in this application embodiment can be volatile memory or non-volatile memory, or it can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but 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 linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the methods described in this invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0042] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as 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 this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0043] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are omitted here.

[0044] It should be noted that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The 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 devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied as execution by a hardware decoding processor, or as a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0045] The above provides a detailed description of the method for calibrating the parameters of the layer shear model based on quasi-static analysis and optimization algorithms proposed in this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for calibrating parameters of a layer shear model based on quasi-static analysis and optimization algorithms, characterized in that, The method includes: Step 1: Establish a detailed finite element model of the building, perform a layer-by-layer quasi-static analysis on the detailed finite element model, and obtain the inter-story hysteresis curves of each floor of the building. Step 2: Determine the location of the cracking point based on the period of the refined finite element model and the set limit of the elastic interlayer displacement ratio. Take the point corresponding to the peak value of the interlayer shear force on the interlayer hysteresis curve as the peak point. Then determine the yield point and the ultimate point based on the farthest point method and the peak bearing capacity reduction method. Step 3: Based on the feature points determined in Step 2 and the particle swarm optimization algorithm, calibrate the hysteresis parameters of the four-segment hysteresis model, thereby minimizing the error between the hysteresis curve corresponding to the four-segment hysteresis model and the actual value; Step 4: Based on the mass of each floor of the building, the characteristic points determined in Step 2, and the hysteresis parameters in Step 3, establish a multi-degree-of-freedom layer shear model, namely the MDOF model. Compare the response and failure state of the MDOF model and the refined finite element model under various level earthquakes, evaluate its calculation accuracy, and compare it with the calculation accuracy of the MDOF model established based on the three-segment hysteresis model.

2. The method according to claim 1, characterized in that, In step 2, the four-segment hysteresis model Pinching4 from OpenSees is used as the interlayer hysteresis model. The Pinching4 model defines the skeleton curve through the crack point, yield point, peak point and limit point.

3. The method according to claim 2, characterized in that, In step 2, the inter-story displacement corresponding to the crack point is set to 1 / 1000 of the story height. The tangent stiffness of each story is determined according to the hysteresis curve of each story, and the stiffness ratio of each story is calculated. While keeping the stiffness ratio of each story unchanged, the tangent stiffness is adjusted so that the first period of the MDOF model is consistent with the fine finite element model. The product of the adjusted initial stiffness and the inter-story displacement of the crack point is the inter-story shear force corresponding to the crack point.

4. The method according to claim 1, characterized in that, In step 3, the mean square error between the hysteresis curve corresponding to the "Pinching4" model and the hysteresis curve obtained from the quasi-static analysis is used as the loss function. The particle swarm optimization algorithm is used to calibrate the parameters of the "Pinching4" model, including the three parameters that define the unloading-reloading curve, namely rDisp_p, rForce_p and uForce_p, and the five parameters that define the unloading stiffness degradation, namely gd1, gd2, gd3, gd4 and gdL. The other parameters use the default values.

5. The method according to claim 4, characterized in that, In step 3, the particle swarm optimization algorithm optimizes the parameters by minimizing the loss function, thereby obtaining the optimal hysteresis parameter values.

6. The method according to claim 1, characterized in that, In step 4, based on the mass of each floor of the building and the feature points and hysteresis parameters determined in steps 2 and 3, an MDOF model is established using the "Two Node Link Element" unit and "Pinching4" material of OpenSees.

7. The method according to claim 6, characterized in that, In step 4, 20 ground motion records are selected and scaled to adjust their peak ground acceleration to specific levels, including 0.05g, 0.1g, 0.2g, 0.3g, 0.4g, 0.5g, 0.6g, 0.7g, and 0.8g. Using the amplitude-modulated ground motion records as external excitation, dynamic analysis is performed on the MDOF model and the refined finite element model to obtain the maximum inter-story drift angle of the building and assess the seismic damage to the building.

8. The method according to claim 7, characterized in that, In step 4, the evaluation criteria include: using the inter-story drift angles corresponding to the cracking point, yield point, peak point, and limit point as the limits for minor damage (SD), moderate damage (MD), severe damage (ED), and complete damage (CD) of the building, respectively. When the inter-story drift angle is less than the value corresponding to the cracking point, the structure is determined to be in the basically intact (ND) state.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-8.

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

Images