Modified subset simulation method for reliability analysis of structural systems

被引:59
作者
Miao, Feng
Ghosn, Michel [1 ]
机构
[1] CUNY City Coll, Dept Civil Engn, New York, NY 10031 USA
关键词
Structural system reliability; Markov Chain simulations; Regenerative Adaptive Subset Simulation; HIGH DIMENSIONS; MARKOV-CHAINS; MONTE-CARLO; BENCHMARK;
D O I
10.1016/j.strusafe.2011.02.004
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A "Regenerative Adaptive Subset Simulation" (RASS) method is proposed for performing the reliability analysis of complex structural systems. Proposed modifications to the classic subset simulation method include the implementation of advanced Markov Chain processes to combine the benefits of a Markov Chain regeneration process, a Delayed Rejection and Adaptive sample selection algorithms and a Componentwise sampling model. The proposed modifications help to overcome the limitations of the original Metropolis-Hasting algorithm used in the subset simulation which include the "burn-in problem" and the difficulty of the selection of the proposal probability function. Several illustrative examples are presented to demonstrate the efficiency of the proposed simulation and compare its results to those of other methods. The results show that RASS is robust and efficient in estimating the probability of failure of structural systems with complex failure regions, large numbers of random variables, and small probabilities of failure. Published by Elsevier Ltd.
引用
收藏
页码:251 / 260
页数:10
相关论文
共 26 条
[1]   A new adaptive importance sampling scheme for reliability calculations [J].
Au, SK ;
Beck, JL .
STRUCTURAL SAFETY, 1999, 21 (02) :135-158
[2]   Probabilistic failure analysis by importance sampling Markov chain simulation [J].
Au, SK .
JOURNAL OF ENGINEERING MECHANICS, 2004, 130 (03) :303-311
[3]   Estimation of small failure probabilities in high dimensions by subset simulation [J].
Au, SK ;
Beck, JL .
PROBABILISTIC ENGINEERING MECHANICS, 2001, 16 (04) :263-277
[4]   Structural reliability analysis using a standard deterministic finite element code [J].
Borri, A ;
Speranzini, E .
STRUCTURAL SAFETY, 1997, 19 (04) :361-382
[5]  
Cowles M.K., 1996, A simulation approach to convergence rates for Markov chain Monte Carlo
[6]   A BENCHMARK STUDY ON IMPORTANCE SAMPLING TECHNIQUES IN STRUCTURAL RELIABILITY [J].
ENGELUND, S ;
RACKWITZ, R .
STRUCTURAL SAFETY, 1993, 12 (04) :255-276
[7]  
Fishman G, 1996, MONTE CARLO CONCEPTS
[8]  
Gelman AG, 1996, EFFICIENT METROPOLIS, P1035
[9]  
Ghosn M, 1999, BRIDGE SAFETY AND RELIABILITY, P83
[10]   Adaptive Markov chain Monte Carlo through regeneration [J].
Gilks, WR ;
Roberts, GO ;
Suhu, SK .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1998, 93 (443) :1045-1054