基于多目标进化算法的多尺度有限元模型更新方法

胡文哲; 崔闯; 王昊; 张清华

doi:10.13204/j.gyjzG22061306

基于多目标进化算法的多尺度有限元模型更新方法

doi: 10.13204/j.gyjzG22061306

西南交通大学桥梁工程系, 成都 610031

基金项目:

国家自然科学基金项目（52108176，51978579，51878561）；桥梁结构健康与安全国家重点实验室开放课题（BHSKL19-06-KF）；四川省交通运输科技项目（2019-ZL-12）；四川省科技计划项目（2021YJ0037）。

详细信息

作者简介:
胡文哲,男,1999年出生,硕士研究生,wzh_112358@163.com。

通讯作者:
崔闯,男,1989年出生,工学博士,副教授,ccui@swjtu.edu.cn。

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- 被引次数: 0
出版历程
- 收稿日期: 2022-06-13
- 网络出版日期: 2023-10-17

Multi-Scale Finite Element Model Update Method Based on a Multi-Objective Evolutionary Algorithm

Department of Bridge Engineering, Southwest Jiaotong University, Chengdu 610031, China

摘要

摘要: 确定大跨桥梁当前状态，明确各关键部位的受力特性，是评估其性能退化及剩余寿命的前提。采用Kriging元模型建立了大跨度桥梁多尺度的代理模型，并使用多目标进化算法和演化控制算法，建立了大跨桥梁模型更新方法。以典型的某大跨度斜拉桥为例，建立大跨度斜拉桥多尺度有限元模型，全局模型整体结构采用梁单元、局部采用板壳单元，通过使用多点约束方法（MPC）满足边界条件。以健康监测系统实测数据为基础，对模型自振频率、位移响应和应力响应进行修正。结果表明：采用多目标进化算法得出的全局和局部指标修正结果与实测数据均吻合较好，相较于初始有限元计算值，自振频率平均相对误差降低了3.38%，位移响应平均相对误差降低了10%，应力响应平均相对误差降低了5%。总体而言，采用Kriging元模型和多目标进化算法可实现大跨桥梁多尺度模型的修正和更新。
- 大跨度桥梁 /
- 多尺度有限元模型修正 /
- Kriging元模型 /
- R2-MOEA多目标优化 /
- 演化控制
Abstract: Determining the current state of long-span bridges and clarifying the force characteristics of key parts are the prerequisites for evaluating their performance degradation and remaining life. A multi-scale surrogate model for long-span bridges was established by using the Kriging metamodel, and a large-span bridge model update method was developed by using a multi-objective evolutionary algorithm and an evolutionary control algorithm. Taking a typical long-span cable-stayed bridge as an example, a multi-scale finite element model of the long-span cable-stayed bridge was established. The overall structure of the global model adopted beam elements and local shell elements, and the boundary conditions were satisfied by using the multi-point constraint method (MPC). Based on the measured data of the health monitoring system, the natural vibration frequency, displacement response and stress response of the model were corrected, and the corrected influence line was obtained. The results showed that the global and local index correction results obtained by the multi-objective evolutionary algorithm were in good agreement with the measured data. Compared with the initial finite element calculation values, the average relative error of the natural frequency was reduced by 3.38%, and the average relative error of the displacement response was relatively high. The error was reduced by 10% and the average relative error of the stress response was reduced by 5%. In general, the Kriging metamodel and the multi-objective evolutionary algorithm could be used to correct and update the multi-scale model of long-span bridges.
- long-span bridge /
- multi-scale finite element model correction /
- Kriging metamodel /
- R2-MOEA multi-objective optimization /
- evolutionary control

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