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Structural Reliability Assessment by Local Approximation of Limit State Functions Using Adaptive Markov Chain Simulation and Support Vector Regression
Author(s) -
Dai Hongzhe,
Zhang Hao,
Wang Wei,
Xue Guofeng
Publication year - 2012
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2012.00767.x
Subject(s) - markov chain , limit (mathematics) , reliability (semiconductor) , limit state design , mathematics , state vector , surrogate model , state (computer science) , function (biology) , mathematical optimization , computer science , algorithm , statistics , mathematical analysis , physics , engineering , power (physics) , structural engineering , classical mechanics , quantum mechanics , evolutionary biology , biology
The surrogate model method is widely used in structural reliability analysis to approximate complex limit state functions. Accurate results can only be obtained when the surrogate model for the limit state function is approximated sufficiently close to the failure region. This study develops a novel local approximation method for efficient structural reliability assessment. The adaptive Markov chain simulation is utilized to generate samples in the failure region (the “region of most interest”). The support vector regression technique is then used to obtain an explicit approximation of the original complex limit state function around the region of most interest. Four examples are given to demonstrate the application and efficiencies of the proposed method.