A structural vulnerability analysis method and related apparatus

Abstract

A structural vulnerability analysis method and related apparatus include: collecting structural design parameter distribution information θ and ground motion time history T; establishing a model based on the design parameter distribution information; inputting the ground motion time history T into the analysis model for deterministic analysis to obtain m sets of high-precision structural responses R. H and n sets of low-precision structural responses R L Establish a neural network model P ‑net Learn the mapping relationship between input and output; convert m sets of high-precision responses R H The low-precision response R after mapping to n-m groups L2H By combining the two methods and performing regression analysis, an earthquake probability demand model D is obtained. Then, by combining the demand model D with the bearing capacity C specified in relevant codes, the structural failure probability P is calculated. f This invention allows for the efficient and accurate acquisition of structural vulnerability analysis results using only a small amount of high-precision structural analysis data, combined with a large amount of low-cost, low-precision structural analysis data. These results can be used for regional structural performance evaluation.

Description

Technical Field

[0001] This invention relates to the field of structural analysis technology, specifically to a method and related apparatus for analyzing structural vulnerability. Background Technology

[0002] For structural vulnerability analysis, due to the need to comprehensively consider various uncertainties related to earthquakes and structural performance, traditional methods often require a large number of detailed numerical analyses of the structure, followed by statistical regression to obtain accurate results. However, this process involves excessive computation and is time-consuming, making it unsuitable for post-earthquake emergencies and even less applicable to the more pressing need for regional-level structural vulnerability analysis. Therefore, efficiently and accurately assessing structural vulnerability levels and promptly and accurately specifying appropriate maintenance strategies has become a top priority. Summary of the Invention

[0003] The purpose of this invention is to provide a structural vulnerability analysis method and related apparatus to solve the problems of existing technologies having excessive overall computational load, long processing time, being unsuitable for post-earthquake emergency situations, and being unable to be applied to the more needed regional-level structural vulnerability analysis work.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a method for structural vulnerability analysis, comprising:

[0006] Collect information on the distribution of structural design parameters θ and seismic motion time history T;

[0007] A high-precision analysis model M is established based on the design parameter distribution information. H and low-precision analysis model M L ;

[0008] Input the seismic motion time history T into the analysis model M H and M L Deterministic analysis was performed, with m and n analysis cycles respectively, resulting in m sets of high-precision structural responses R. H and n sets of low-precision structural responses R L ;

[0009] The design parameter distribution information θ, the seismic motion time history T characteristic Ft, and the corresponding R L As input features, R H A dataset containing m sets of data is established as the output features, and a neural network model P is built. -net Learn the mapping relationship between input and output;

[0010] The remaining nm group of low-precision structural response R L Enter P -netMapped to high-precision response R L2H ;

[0011] m sets of high-precision responses R H The low-precision response R after mapping with nm group L2H By combining and performing regression analysis, we obtain the earthquake probability demand model D.

[0012] By combining the demand model D and the bearing capacity C specified in relevant standards, the structural failure probability P is calculated. f The vulnerability analysis results were obtained.

[0013] Furthermore, the design parameter distribution information θ includes structural geometry and material property parameters.

[0014] Furthermore, the high-precision analysis model is a refined model of solid units or a fiber unit model; the low-precision analysis model is a multi-mass point model.

[0015] Furthermore, the number of high-precision structural response RH and low-precision structural response RL, m and n, are selected. The value of n is chosen to be greater than or equal to 10 times the θ dimension, while m satisfies: 10%×n≤m≤30%×n.

[0016] Furthermore, the ground motion time history T features Ft include peak ground acceleration (PGA), peak ground velocity (PGV), spectral acceleration (SA), and complete ground motion time history data AT.

[0017] Furthermore, the neural network model includes two branches for fitting the linear mapping relationship PL and the nonlinear mapping relationship PNL, and the final network output is obtained by weighted averaging of the two branches based on an adaptive weight α: Network Output = α·P L +(1-α)·P NL .

[0018] In a second aspect, the present invention provides a structural vulnerability analysis system, comprising:

[0019] The data acquisition module is used to collect structural design parameter distribution information θ and ground motion time history T;

[0020] The model building module is used to build a high-precision analysis model M based on the design parameter distribution information. H and low-precision analysis model M L ;

[0021] The input module is used to input the ground motion time history T into the analysis model M. H and M L Deterministic analysis was performed, with m and n analysis cycles respectively, resulting in m sets of high-precision structural responses R. H and n sets of low-precision structural responses R LThe design parameter distribution information θ, the seismic motion time history characteristic Ft, and the corresponding R... L As input features, R H A dataset containing m sets of data is established as the output features, and a neural network model P is built. -net Learn the mapping relationship between input and output;

[0022] The earthquake probability demand model acquisition module is used to obtain the remaining nm group of low-precision structural response R. L Enter P -net Mapped to high-precision response R L2H ; to set m high-precision responses R H The low-precision response R after mapping with nm group L2H By combining and performing regression analysis, we obtain the earthquake probability demand model D.

[0023] The calculation output module is used to combine the demand model D and the bearing capacity C specified in relevant standards to calculate the structural failure probability P. f The vulnerability analysis results were obtained.

[0024] In a third aspect, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a structural vulnerability analysis method.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a structural vulnerability analysis method.

[0026] Compared with the prior art, the present invention has the following technical effects:

[0027] This invention can efficiently and accurately obtain structural vulnerability analysis results using only a small amount of high-precision structural analysis data, combined with a large amount of low-cost, low-precision structural analysis data, for the evaluation of regional structural performance.

[0028] This invention can improve the accuracy of a large number of low-precision model analysis results based on a small number of high-precision model analysis results, and obtain vulnerability analysis results with comparable accuracy to those based solely on high-precision models. Since only a small number of high-precision model analyses are performed, the overall analysis efficiency is greatly improved, and it can effectively serve real-time high-precision regional structural vulnerability analysis. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the steps of the efficient structural vulnerability analysis method of the present invention;

[0030] Figure 2This is a typical frame structure and its design parameter θ;

[0031] Figure 3 For the high-precision fiber unit model and low-precision multi-mass model established based on the frame structure design parameters;

[0032] Figure 4 The maximum inter-story drift angles RH and RL of the structure were obtained using high-precision and low-precision models.

[0033] Figure 5 This is a schematic diagram of the P-net neural network structure used to establish the mapping. Detailed Implementation

[0034] The present invention will now be described in detail with reference to the accompanying drawings.

[0035] like Figure 1 As shown, the present invention provides an efficient structural vulnerability analysis method, comprising the following steps:

[0036] Step 1: Collect structural design parameter distribution information θ (including structural geometric dimensions and material property parameters) and seismic motion time history T, such as Figure 2 This illustrates a typical framework combination and key design parameters;

[0037] Step 2: Establish a high-precision analysis model M based on the design parameter θ H and low-precision analysis model M L ,like Figure 3 The figure corresponds to Figure 2 High-precision fiber unit model and low-precision multi-mass point model of medium frame model;

[0038] Step 3: Input the ground motion time history T into the analysis model M H and M L Deterministic analysis was performed, with m and n analysis cycles respectively, resulting in m sets of high-precision structural responses R. H and n sets of low-precision structural responses R L ,like Figure 4 The maximum inter-story drift angle R obtained by the two models shown in the figure is... H and R L ;

[0039] Step 4: Combine θ, the seismic motion time history T characteristic Ft, and the corresponding R L As input features, R H A dataset containing m sets of data is established as the output features, and a neural network model P is built. -ne t learns the mapping relationship between input and output. Figure 5 A schematic diagram of the network structure is provided;

[0040] Step 5: Calculate the remaining (nm) groups of low-precision structural responses R L Enter P -net Mapped to high-precision response R L2H ;

[0041] Step 6: Convert m sets of high-precision responses R H The low-precision response R after mapping to (nm) groups L2H By combining and performing regression analysis, we obtain the earthquake probability demand model D.

[0042] Step 7: Combine the demand model D and the bearing capacity C specified in the relevant standards to calculate the structural failure probability Pf and obtain the vulnerability analysis results. It can be seen that the vulnerability curve obtained by this method is very close to the vulnerability curve obtained based on the pure high-precision model. Moreover, the calculation efficiency is greatly improved because only a small number of high-precision model analyses are required.

[0043] In another embodiment of the present invention, a structural vulnerability analysis system is provided, which can be used to implement the above-described structural vulnerability analysis method. Specifically, the system includes:

[0044] The data acquisition module is used to collect structural design parameter distribution information θ and ground motion time history T;

[0045] The model building module is used to build a high-precision analysis model M based on the design parameter distribution information. H and low-precision analysis model M L ;

[0046] The input module is used to input the ground motion time history T into the analysis model M. H and M L Deterministic analysis was performed, with m and n analysis cycles respectively, resulting in m sets of high-precision structural responses R. H and n sets of low-precision structural responses R L The design parameter distribution information θ, the seismic motion time history characteristic Ft, and the corresponding R... L As input features, R H A dataset containing m sets of data is established as the output features, and a neural network model P is built. -net Learn the mapping relationship between input and output;

[0047] The earthquake probability demand model acquisition module is used to obtain the remaining nm group of low-precision structural response R. L Enter P -net Mapped to high-precision response R L2H ; to set m high-precision responses R H The low-precision response R after mapping with nm group L2H By combining and performing regression analysis, we obtain the earthquake probability demand model D.

[0048] The calculation output module is used to combine the demand model D and the bearing capacity C specified in relevant standards to calculate the structural failure probability P. f The vulnerability analysis results were obtained.

[0049] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0050] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a structural vulnerability analysis method.

[0051] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the structural vulnerability analysis method in the above embodiments.

[0052] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A structural vulnerability analysis method, characterized in that, include: Collect information on the distribution of structural design parameters θ and seismic motion time history T; Based on the design parameter distribution information, a high-precision analysis model M H and a low-precision analysis model M L are established. Inputting the time history T of ground motion into the analysis model M H and M L Performing deterministic analysis, the analysis times are m and n respectively, obtaining m groups of high-precision structural responses R H and n groups of low-precision structural responses R L ; The design parameter distribution information θ, the seismic motion time history T characteristic Ft, and the corresponding R L As input features, R H A dataset containing m sets of data is established as the output features, and a neural network model P is built. -net Learn the mapping relationship between input and output; The remaining nm group of low-precision structural response R L Enter P -net Mapped to low-precision response R L2H ; m sets of high-precision responses R H The low-precision response R after mapping with nm group L2H By combining and performing regression analysis, we obtain the earthquake probability demand model D. By combining the demand model D and the bearing capacity C specified in relevant standards, the structural failure probability P is calculated. f The vulnerability analysis results were obtained. High-precision analysis models are refined solid element models or fiber element models; low-precision analysis models are multi-mass point models. The value of n is chosen to be greater than or equal to 10 times the dimension of θ, while m satisfies: 10%×n ≤ m ≤ 30%×n; The neural network model includes a method for fitting a linear mapping relationship P. L and nonlinear mapping relationship P NL The network has two branches, and the final output is based on an adaptive weight α. The network output is obtained by weighted averaging the results of the two branches: Network Output = α·P L +(1-α)·P NL .

2. The structural vulnerability analysis method according to claim 1, characterized in that, The design parameter distribution information θ includes structural geometric dimensions and material property parameters.

3. The structural vulnerability analysis method according to claim 1, characterized in that, The time history characteristics of ground motion (T) include peak ground acceleration (PGA), peak ground velocity (PGV), spectral acceleration (SA), and complete time history data (AT).

4. A structural vulnerability analysis system, characterized in that, To implement the structural vulnerability analysis method as described in claim 1, the method includes: The data acquisition module is used to collect structural design parameter distribution information θ and ground motion time history T; The model building module is used to build a high-precision analysis model M based on the design parameter distribution information. H and low-precision analysis model M L ; The input module is used to input the ground motion time history T into the analysis model M. H and M L Deterministic analysis was performed, with m and n analysis cycles respectively, resulting in m sets of high-precision structural responses R. H and n sets of low-precision structural responses R L The design parameter distribution information θ, the seismic motion time history characteristic Ft, and the corresponding R... L As input features, R H A dataset containing m sets of data is established as the output features, and a neural network model P is built. -net Learn the mapping relationship between input and output; The earthquake probability demand model acquisition module is used to obtain the remaining nm group of low-precision structural response R. L Enter P -net Mapped to low-precision response R L2H ; to set m high-precision responses R H The low-precision response R after mapping with nm group L2H By combining and performing regression analysis, we obtain the earthquake probability demand model D. The calculation output module is used to combine the demand model D and the bearing capacity C specified in relevant standards to calculate the structural failure probability P. f The vulnerability analysis results were obtained.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the structural vulnerability analysis method as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the structural vulnerability analysis method as described in any one of claims 1 to 3.

Images