Finite Element Model Updating via Response Surface Parameter Screening and Intelligent Algorithm Optimization

WU Daoqi; DU Ke; LUO Huan; MA Jialu; NIE Guibo

doi:10.13409/j.cnki.jdpme.20231220003

您当前的位置：

首页 >

文章列表页 >

Finite Element Model Updating via Response Surface Parameter Screening and Intelligent Algorithm Optimization

更新时间：2025-05-07

- Finite Element Model Updating via Response Surface Parameter Screening and Intelligent Algorithm Optimization
- Journal of Disaster Prevention and Mitigation Engineering Vol. 45, Issue 2, Pages: 295-306(2025)
- 作者机构：
  
  1.中国地震局工程力学研究所地震工程与工程振动重点实验室，黑龙江哈尔滨，150080
  2.地震灾害防治应急管理部重点实验室，黑龙江哈尔滨 150080
  3.三峡大学土木与建筑学院，湖北宜昌 443002
- 作者简介：
- 基金信息：
- DOI：10.13409/j.cnki.jdpme.20231220003
  CLC： TU443
- Received：20 December 2023，
  
  Revised：2024-03-26，
  
  Published：28 April 2025
- 稿件说明：
移动端阅览
吴道奇,杜轲,骆欢等.响应面参数筛选与智能算法优化的有限元模型更新[J].防灾减灾工程学报,2025,45(02):295-306.

WU Daoqi,DU Ke,LUO Huan,et al.Finite Element Model Updating via Response Surface Parameter Screening and Intelligent Algorithm Optimization[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(02):295-306.
吴道奇,杜轲,骆欢等.响应面参数筛选与智能算法优化的有限元模型更新[J].防灾减灾工程学报,2025,45(02):295-306. DOI： 10.13409/j.cnki.jdpme.20231220003.

WU Daoqi,DU Ke,LUO Huan,et al.Finite Element Model Updating via Response Surface Parameter Screening and Intelligent Algorithm Optimization[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(02):295-306. DOI： 10.13409/j.cnki.jdpme.20231220003.

摘要

在当前工程和科学研究中，由于大规模结构优化问题中的有限元模型受到高计算成本和复杂性的限制，引入响应面模型成为克服这一挑战的有效途径，研究人员可在保持相对准确性的同时显著降低计算成本，为结构设计和优化提供更为可行和经济的解决方案。然而，面对复杂模型拟合响应面时，考虑到个体差异和高成本的灵敏度分析，传统参数筛选导致了模型更新的准确性和效率下降。为解决参数筛选问题，本研究以一栋26层框架剪力墙结构有限元模型为对象，在构建响应面时引入了单因素试验和爬坡试验两个预处理步骤。这两步骤的目的是缩小搜索空间、筛选关键因素，并提供梯度信息，从而使得响应面的构建更为准确和可操作，为后续模型处理提供可靠基础。通过结合多种智能算法，本研究完成了对响应面的模型更新和优化操作。研究结果表明，经过预处理步骤筛选之后的参数构建的响应面，在多重类型算法应用时能与识别结果保持较低的误差率。这项研究为未来工程实践和相关领域的研究提供了有益的指导，为大规模结构优化问题中提高有限元模型更新的准确性和效率提供了更为灵活和通用的优化解决方案。

Abstract

In the current engineering and scientific research

finite element models for large-scale structural optimization face limitations due to high computational costs and complexity. The integration of response surface models has emerged as an effective approach to overcome these challenges

enabling researchers to significantly reduce computational costs while maintaining acceptable accuracy. However

when fitting response surfaces for complex models

conventional parameter screening methods often lead to reduced accuracy and efficiency

particularly when considering individual variations and the high costs of sensitivity analysis. Focusing on the finite element model of a 26-story frame-shear wall structure

this study integrated two preprocessing steps—single-factor experiments and hill-climbing tests—during response surface construction. These steps aimed to narrow the search space

screen key factors

and provide gradient information

making the construction of the response surface more accurate and operable

and providing a reliable foundation for subsequent model processing. By integrating multiple intelligent algorithms

this study completed the model updating and optimization operations for the response surface. The research results showed that the response surface constructed using parameters screened through preprocessing steps maintained consistently low error rates with identification results when multiple algorithm types were applied. This study provides valuable guidance for future engineering practices and research on related fields

offering a more flexible and universal optimization solution for enhancing the accuracy and efficiency of finite element model updating in large-scale structural optimization.

关键词

Keywords

references

蒋寅军 . 基于响应面方法的复杂结构模型修正方法研究［D］. 武汉：武汉大学， 2011 .

Jiang Y J . Research on model updating methods for complex structures based on response surface methodology ［D］. Wuhan ： Wuhan University . （in Chinese）

任伟新，陈华斌 . 基于响应面的桥梁有限元模型修正［J］. 土木工程学报， 2008 ， 41 （ 12 ）： 73 - 78 .

Ren W X ， Chen H B . Response surface-based finite element model updating for bridge structures ［J］. China Civil Engineering Journal ， 2008 ， 41 （ 12 ）： 73 - 78 . （in Chinese）

鲍诺，王春洁，赵军鹏，等 . 基于响应面法的结构动力学模型修正［J］. 振动与冲击， 2013 ， 32 （ 16 ）： 54 - 58 .

Bao N ， Wang C J ， Zhao J P ， et al . Model updating of structure dynamics based on response surface methodology ［J］. Journal of Vibration and Shock ， 2013 ， 32 （ 16 ）： 54 - 58 . （in Chinese）

陈东 . 基于响应面法的桥梁结构有限元模型修正与应用［D］. 西安：长安大学， 2018 .

Chen D . Finite element model updating and application of bridge structures based on response surface methodology ［D］. Xi'an ： Chang'an University ， 2018 . （in Chinese）

Aborehab A ， Kamel M ， Nemnem A F ， et al . Finite element model updating of a satellite honeycomb sandwich plate in structural dynamics ［J］. International Journal of Space Structures ， 2021 ， 36 （ 2 ）： 105 - 116 .

张梓乔 . 基于量子遗传算法及响应面的桥梁有限元模型修正［D］. 武汉：武汉理工大学， 2021 .

Zhang Z Q . Bridge finite element model updating based on quantum genetic algorithm and response surface methodology ［D］. Wuhan ： Wuhan University of Technology ， 2021 . （in Chinese）

陶冬旺，刘泉，马强，等 . 云南漾濞地震大理某高层建筑结构地震响应观测数据初步分析［J］. 世界地震工程， 2021 ， 37 （ 3 ）： 19 - 30 .

Tao D W ， Liu Q ， Ma Q ， et al . Preliminary analysis of structural seismic response of a tall building in Dali recorded in Yunnan Yangbi earthquakes ［J］. World Earthquake Engineering ， 2021 ， 37 （ 3 ）： 19 - 30 . （in Chinese）

Box G E P ， Wilson K B . On the experimental attainment of optimum conditions ［J］. Journal of the Royal Statistical Society： Series B （Methodological）， 1951 ， 13 （ 1 ）： 1 - 45 .

Fisher R A . Statistical methods for research workers ［C］∥Kotz S， Johnson N L. Breakthroughs in statistics： methodology and distribution . New York ： Springer ， 1992 ： 66 - 70 .

Kennedy J ， Eberhart R . Particle swarm optimization ［C］∥ Proceedings of the IEEE International Conference on Neural Networks . Perth ： IEEE ， 1995 ： 1942 - 1948 .

Dorigo M . Optimization， learning and natural algorithms ［D］. Milan ： Politecnico di Milano ， 1992 .

李晓磊，路飞，田国会，等 . 组合优化问题的人工鱼群算法应用［J］. 山东大学学报（工学版）， 2004 ， 34 （ 5 ）： 64 - 67 .

Li X L ， Lu F ， Tian G H ， et al . Application of artificial fish swarm algorithm in combinatorial optimization problems ［J］. Journal of Shandong University （Engineering Science）， 2004 ， 34 （ 5 ）： 64 - 67 . （in Chinese）

Karaboga D . idea based on honey bee swarm for numerical optimization ［R］. Kayseri ： Erciyes University， Engineering Faculty， Computer Engineering Department ， 2005 .

Lin K Q ， Xu Y L ， Lu X Z ， et al . Cluster computing-aided model updating for a high-fidelity finite element model of a long-span cable-stayed bridge ［J］. Earthquake Engineering & Structural Dynamics ， 2020 ， 49 （ 9 ）： 904 - 923 .

Meng Z ， Yang C . Hip-DE： Historical population based mutation strategy in differential evolution with parameter adaptive mechanism ［J］. Information Sciences ， 2021 ， 562 ： 44 - 77 .

Kirkpatrick S ， Gelatt C D ， Vecchi M P . Optimization by simulated annealing ［J］. Science ， 1983 ， 220 （ 4598 ）： 671 - 680 .

Farmer J D ， Packard N H ， Perelson A S . The immune system， adaptation， and machine learning ［J］. Physica D： Nonlinear Phenomena ， 1986 ， 22 （ 1 ）： 187 - 204 ..

Ruder S . An overview of gradient descent optimization algorithms ［EB/OL］. （ 2016-1-19 ）［ 2023-12-20 ］. http：∥sebasti-anruder.com/optimizing-gradient-descent/index. html http://sebasti-anruder.com/optimizing-gradient-descent/index.html .

Luo C ， Wang L ， Xie Y ， et al . A new conjugate gradient method for moving force identification of vehicle-bridge system ［J］. Journal of Vibration Engineering & Technologies ， 2024 ， 12 ： 19 - 36 .

Singh H ， Sharma J R ， Kumar S . A simple yet efficient two-step fifth-order weighted-Newton method for nonlinear models ［J］. Numerical Algorithms ， 2023 ， 93 ： 203 - 225 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on Long‑term Performance Prediction Models of Slope Pile Foundations under Cyclical Reservoir Water‑level Condition

Seismic Fragility Analysis of Column⁃Supported Silo Structures Based on IDA

Damage Evolution Analysis of Composite Lining Convey Tunnel under Reverse Fault

Disaster Response Characteristics of Rammed Earth Buildings to Debris Flow

Related Author

WANG Beichen

FENG Zihang

JIANG Xinjian

SONG Anxiang

YAO Guowen

ZHANG Gaofeng

LIU Tao

REN Guoqi

Related Institution

State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University

School of Civil Engineering, Chongqing Jiaotong University

School of Materials Science and Engineering, Chongqing Jiaotong University

The First Construction Institution of China Railway Tunnel Group

School of Civil Engineering, Henan University of Technology

⁰