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核密度估计的桥梁结构地震易损性分析 被引量:12

Seismic fragility analysis of railway bridge using kernel density estimation
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摘要 为弥补常用桥梁地震易损性分析方法的不足,提高易损性分析的计算精度,将核密度估计法引入到桥梁结构地震易损性分析中,提出一种核密度估计的桥梁结构地震易损性分析方法(简称KDE法).以某铁路典型4跨高墩刚构-连续梁桥为工程背景,开展了该桥的地震易损性分析.以蒙特卡罗法计算结果为依据,验证了桥梁KDE法的正确性,并将KDE法与常用方法(云图法、极大似然估计法)计算结果进行对比分析.结果表明:桥梁KDE分析法弥补了常用桥梁易损性分析方法的不足,具有良好的稳定性,在满足精度要求的前提下,显著提升了桥梁地震易损性分析的计算效率;云图法的计算精度最低,建议采用KDE法开展桥梁地震易损性分析,建立的易损性曲线可评估桥梁的抗震性能. To overcome shortcomings and improve calculation accuracy of the common seismic fragility analysis method,the kernel density estimation method( KDE) is introduced and used to analysis the seismic fragility of bridge structures. A typical four span railway rigid frame-continuous beam bridge with high pier was taken as engineering case,and the fragility analysis of this bridge was carried out. The calculation result by using of KDE method was validated by using of Monte Carlo method,and then was compared with traditional methods( cloud method,maximum likelihood estimation method). Result shows that the KDE method could overcome the shortcomings of common fragility analysis methods of bridge and presents a good stability,and also improves the computational efficiency of seismic fragility analysis method on the premise of ensuring the accuracy. The calculation accuracy of cloud method is the lowest among of the using methods,and the KDE method was suggested to use for seismic vulnerability analysis of bridge. The seismic fragility curves could be applied to evaluate the performances of bridge seismic.
作者 董俊 曾永平 单德山 DONG Jun;ZENG Yongping;SHAN Deshan(China Railway Eryuan Engineering Group Co., Ltd., Chengdu 610031, China;Civil Engineering School, Southwest Jiaotong University, Chengdu 610031, China)
出处 《哈尔滨工业大学学报》 EI CAS CSCD 北大核心 2018年第3期109-117,共9页 Journal of Harbin Institute of Technology
基金 国家重点基础研究发展计划(2013CB036300-2) 中国中铁二院科研项目(13164190(13-15) 13164191(13-15))
关键词 桥梁工程 核密度估计 地震易损性分析 蒙特卡罗法 云图法 极大似然法 bridge engineering kernel density estimation method seismic vulnerability analysis Monte Carlos imulation method cloud method maximum likelihood method
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