Structural Reliability Assessment by Local Approximation of Limit State Functions Using Adaptive Markov Chain Simulation and Support Vector Regression

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.