Efficient imprecise reliability analysis using the Augmented Space Integral

This paper presents an efficient approach to compute the bounds on the reliability of a structure subjected to uncertain parameters described by means of imprecise probabilities. These imprecise probabilities arise from epistemic uncertainty in the definition of the hyper-parameters of a set of random variables that describe aleatory uncertainty in some of the structure's properties. Typically, such calculation involves the solution of a so-called double-loop problem, where a crisp reliability problem is repeatedly solved to determine which realization of the epistemic uncertainties yields the worst or best case with respect to structural safety. The approach in this paper aims at decoupling this double loop by virtue of the Augmented Space Integral. The core idea of the method is to infer a functional relationship between the epistemically uncertain hyper-parameters and the probability of failure. Then, this functional relationship can be used to determine the best and worst case behavior with respect to the probability of failure. Three case studies are included to illustrate the effectiveness and efficiency of the developed methods.

Automated summary

This paper presents an efficient approach to compute the bounds on the reliability of a structure by decoupling a double-loop problem. The core idea is to infer a functional relationship between epistemic uncertain hyper-parameters and the probability of failure to determine the best and worst case behavior with respect to failure probability. Three case studies illustrate the effectiveness and efficiency of the developed methods.