Abstract
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.
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