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贝叶斯估计的桥梁结构地震易损性分析 被引量:9

Seismic vulnerability analysis of bridge structures using Bayesian estimation
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摘要 地震易损性分析方法目前已成为评估桥梁结构抗震性能的重要手段,为弥补常用易损性分析方法的不足,提高易损性分析效率,引入贝叶斯估计方法,提出一种基于贝叶斯估计的桥梁地震易损性分析方法(简称BEM法),以某铁路高烈度地震区4跨典型铁路简支梁桥为工程背景,开展不同墩高简支梁桥的易损性分析。采用常用易损性分析方法,计算结果验证了BEM法的正确性,并开展了不同墩高简支梁桥的抗震性能评估,结果表明:BEM法弥补了常用方法的不足,在保证计算精度前提下提高了分析效率,大幅减少计算工作量,BEM法具有良好的稳定性,有着较好的工程应用前景。 The seismic vulnerability analysis method has been widely used to evaluate the seismic performance of bridge structures.In order to make up for the shortcomings of common analysis methods of bridge seismic vulnerability and improve the efficiency of vulnerability analysis,the Bayesian estimation method was introduced to the analysis of seismic vulnerability for bridge structures,and a bridge seismic vulnerability method based on Bayesian estimation(referred to as BEM)was proposed.Taking a typical four⁃span railway simply supported beam bridge in strong earthquake area as the engineering background,the vulnerability analysis of the bridge with different pier heights was carried out.The correctness of the proposed method was verified by taking the analysis results of common methods as reference.Results indicate that BEM can overcome the shortcomings of common bridge vulnerability analysis methods,improve the analysis efficiency of bridge seismic vulnerability analysis method,and greatly reduce the computational burden on the premise of ensuring accuracy.The method has good stability and a great prospect in engineering application.
作者 董俊 曾永平 陈克坚 郑晓龙 刘力维 庞林 DONG Jun;ZENG Yongping;CHEN Kejian;ZHENG Xiaolong;LIU Liwei;PANG Lin(China Railway Eryuan Engineering Group Co.,Ltd.,Chengdu 610031,China;Department of Transport and Municipal Engineering,Sichuan College of Architectural Technology,Chengdu 610399,China)
出处 《哈尔滨工业大学学报》 EI CAS CSCD 北大核心 2021年第9期88-98,共11页 Journal of Harbin Institute of Technology
基金 高速铁路基础研究联合基金(U1934207) 四川省科技计划项目(2019YFG0048) 中国中铁二院科研项目(KYY2018059(18-20))。
关键词 桥梁工程 贝叶斯估计 地震易损性分析 先验分布 后验分布 bridge engineering Bayesian estimation seismic vulnerability analysis prior distribution posterior distribution
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