Augmented line sampling and combination algorithm for imprecise time-variant reliability analysis

Abstract

Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters simultaneously. To estimate the Imprecise Time-variant Failure Probability Function (ITFPF) and derive the imprecise reliability results as a byproduct, Adaptive Combination Augmented Line Sampling (ACALS) is proposed. It consists of three integrated features: Augmented Line Sampling (ALS), adaptive strategy, and the optimal combination. ALS is adopted as an efficient analysis tool to obtain the failure probability function w.r.t. imprecise parameters. Then, the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time simultaneously. Finally, the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance (C.o.V.) of the ITFPF estimate. Overall, the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal C.o.V. Thus, the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering applications. Several examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF.