防灾减灾工程学报2025，Vol.45Issue(2)：384-392,9.DOI:10.13409/j.cnki.jdpme.20231212006
基于模态参数修正的桥梁地震易损性分析方法研究
Research on Seismic Vulnerability Analysis Method for Bridges Based on Modification of Modal Parameters
摘要
Abstract
To investigate the seismic vulnerability of in-service bridges,considering the uncertainty of system parameters in finite element modeling,this study presents a bridge model modified by a back-propagation(BP)neural network.A variable cross-section continuous girder bridge in East China was taken as an example.Utilizing Midas Civil software,a refined finite element model was established,where the bridge´s dynamic responses served as inputs and structural parameters as outputs.The origi-nal finite element model was modified using the measured modal data of the bridge.The modification results showed that the BP neural network reduced model error from 22.92%to 4.58%,enhancing computational accuracy.Following the seismic isolation design theory in China´s"Code for Seismic De-sign of Highway Bridges",lead rubber bearings were installed.The incremental dynamic analysis(IDA)method was employed to conduct nonlinear time-history analyses on three models:original mod-el,modified model,and isolation-optimized model.Structural responses under different seismic waves were extracted to develop vulnerability curves.The data results indicated that the modified model had slightly lower damage probability than the original one.The use of seismic isolation bearings effective-ly reduced the probability of structural failure under seismic load,with the maximum damage probabili-ty decreasing by approximately 42%,demonstrating significant isolation effectiveness.关键词
连续梁桥/模型修正/时程分析/增量动力法/地震易损性Key words
continuous girder bridge/model modification/time-history analysis/incremental dynamic analysis/seismic vulnerability分类
建筑与水利引用本文复制引用
李宁波,朱清帅,周宇,束庆东,程才..基于模态参数修正的桥梁地震易损性分析方法研究[J].防灾减灾工程学报,2025,45(2):384-392,9.基金项目
安徽省高校科学研究重点项目(2022AHO50248)、建筑健康监测与灾害预防技术国家地方联合工程实验室主任基金(GG22KF002)、安徽省高校优秀拔尖人才培育项目(gxgnfx2022021)、教育厅科研编制科学研究重点项目(2023AH050182)、企业委托技术开发课题(HYB20220240、HYB20230001)资助 （2022AHO50248）
