华南理工大学学报(自然科学版) ›› 2015, Vol. 43 ›› Issue (10): 57-66.doi: 10.3969/j.issn.1000-565X.2015.10.009

• 土木建筑工程 • 上一篇    下一篇

基于 ISC-Kriging 响应面法的桥梁抗震动力可靠度分析

贾布裕 余晓琳 颜全胜 陈舟   

  1. 华南理工大学 土木与交通学院,广东 广州 510640
  • 收稿日期:2014-10-13 修回日期:2015-06-23 出版日期:2015-10-25 发布日期:2015-09-06
  • 通信作者: 余晓琳( 1978-) ,女,博士,副教授,主要从事桥梁检测和安全评估 E-mail:xlyul@scut.edu.cn
  • 作者简介:贾布裕( 1983-) ,男,博士后,主要从事大跨度桥梁可靠度研究
  • 基金资助:
    国家自然科学基金资助项目( 51208208) ; 中国博士后科学基金资助项目( 2013M542174) ; 华南理工大学中央高 校基本科研业务费专项资金资助项目( 2015ZM114)

Seismic Dynamic Reliability Analysis of Bridges Based on ISC-Kriging Response Surface Method

Jia Bu-yu Yu Xiao-lin Yan Quan-sheng Chen Zhou   

  1. School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2014-10-13 Revised:2015-06-23 Online:2015-10-25 Published:2015-09-06
  • Contact: 余晓琳( 1978-) ,女,博士,副教授,主要从事桥梁检测和安全评估 E-mail:xlyul@scut.edu.cn
  • About author:贾布裕( 1983-) ,男,博士后,主要从事大跨度桥梁可靠度研究
  • Supported by:
    Supported by the National Natural Science Foundation of China ( 51208208) and China Postdoctoral Science Foundation( 2013M542174)

摘要: 响应面法是解决桥梁抗震可靠度中非线性和复杂性等问题的有效方法,但其存 在代理模型类型和训练样本的选取问题. 鉴于此,提出了基于样本填充准则( ISC) 最优化 和 Kriging 模型的改进序贯抽样响应面法. 其以 Kriging 模型作为代理模型,结合蒙特卡洛 抽样技术,利用 Kriging 模型优秀的预测值估计性能及独有的预测均方差估计能力,建立 包含未知样本预测值和预测均方差的 ISC 函数,在迭代阶段通过求解 ISC 最优化问题,进 行局部和全局的平衡搜索,逐一序贯产生后续训练样本. 最后采用所提的基于 ISC-Kriging 改进响应面法对随机地震激励下某悬索桥的动力可靠度问题进行了计算分析. 结果表明, 所提方法具有较高的准确性、高效性.

关键词: 桥梁, 动力可靠度, 样本填充准则, 序贯抽样, 响应面, Kriging 模型, 蒙特卡洛抽样

Abstract: The response surface method is regarded as an effective way to solve the nonlinearity and complexity problems of the seismic reliability of bridges,but it has problems in selecting both agent model types and training samples. In order to solve these problems,an improved sequential sampling response surface method is proposed based on the infill sampling criterion ( ISC) optimization method and the Kriging model. In the proposed method, by taking the Kriging model as the agency and by drawing on the Monte Carlo sampling technique,an ISC function,which includes the predictive values of unknown samples and the corresponding MSE,is established by making use of the excellent prediction performance of the Kriging model and its unique ability to estimate the mean square error ( MSE) . In the iterative phase,by solving the ISC optimization problem,the local and global searches are conducted and then the subsequent training samples are generated sequentially. Finally,the proposed method is utilized to analyze the dynamic reliability of a suspension bridge under random seismic excitation. The results show that the proposed method is of high accuracy and high efficiency.

Key words: bridge, dynamic reliability, infill sampling criterion, sequential sampling, response surface, Kriging model, Monte Carlo sampling

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