Abstract
The failure probability function (FPF) expresses the probability of failure as a function of the distribution parameters associated with the random variables . Knowledge on FPF is of much relevance for reliability sensitivity analysis and reliability-based design optimisation. . this paper presents an efficient approach for estimating the FPF based on strategy and . involves three basic elements: (1) a Weighted Importance Sampling approach local FPF estimates; (2) an adaptive strategy of the distribution parameters local FPF estimation; and (3) an optimal combination algorithm, local FPF estimations together to form a global estimate of the FPF. Test and practical examples are presented to demonstrate the efficiency and feasibility of the proposed approach. • A global Adaptive Weigthed Importance Sampling approach for failure probability function estimation is proposed. • Adaptive Weigthed Importance Sampling utilizes weighted importance sampling to efficiently obtain local estimators. • Adaptive Weigthed Importance Sampling includes an adaptive active strategy to guide the local approximations. • Adaptive Weigthed Importance Sampling adopts an optimal combination algorithm to combine the local FPF estimations.
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