Reliability-based design sensitivity by efficient simulation

被引:278
作者
Au, SK [1 ]
机构
[1] Nanyang Technol Univ, Sch Civil & Environm Engn, Singapore 639798, Singapore
关键词
failure analysis; Monte Carlo method; performance-based design; reliability; sensitivity analysis; subset simulation;
D O I
10.1016/j.compstruc.2004.11.015
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Reliability-based design sensitivity analysis involves studying the dependence of the failure probability on design parameters. Conventionally, this requires repeated evaluations of the failure probability for different values of the design parameters, which is a direct but computationally expensive task. An efficient simulation approach is presented to perform reliability-based design sensitivity analysis using only one simulation run. The approach is -based on consideration of an 'augmented reliability problem' where the design parameters are artificially considered as uncertain. It is shown that the desired information about reliability sensitivity can be extracted through failure analysis of the augmented problem. The required computational effort is relatively insensitive to the number of uncertain parameters but generally grows exponentially with the number of design parameters whose sensitivity is to be studied. The latter implies that the proposed approach is applicable for studying the sensitivity of a small number of design parameters, say, less than 3, although this drawback appears unavoidable whenever multi-dimensional information is sought. Examples are presented to illustrate applications of the approach to reliability-based retrofit of structures. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1048 / 1061
页数:14
相关论文
共 38 条
[1]  
[Anonymous], 1996, Probability theory: the logic of science
[2]  
[Anonymous], 1996, P 11 WORLD C EARTHQ
[3]  
[Anonymous], 1996, Monte Carlo Concepts, Algorithms and Applications
[4]  
*ATC NEHRP, 1997, FEMA273 FED EM MAN A
[5]   Stochastic modeling of California ground motions [J].
Atkinson, GM ;
Silva, W .
BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, 2000, 90 (02) :255-274
[6]   Probabilistic failure analysis by importance sampling Markov chain simulation [J].
Au, SK .
JOURNAL OF ENGINEERING MECHANICS, 2004, 130 (03) :303-311
[7]   Subset simulation and its application to seismic risk based on dynamic analysis [J].
Au, SK ;
Beck, JL .
JOURNAL OF ENGINEERING MECHANICS, 2003, 129 (08) :901-917
[8]   Important sampling in high dimensions [J].
Au, SK ;
Beck, JL .
STRUCTURAL SAFETY, 2003, 25 (02) :139-163
[9]   Estimation of small failure probabilities in high dimensions by subset simulation [J].
Au, SK ;
Beck, JL .
PROBABILISTIC ENGINEERING MECHANICS, 2001, 16 (04) :263-277
[10]  
AU SK, 2001, 200102 EERL CAL I TE