TY - JOUR

T1 - Efficient imprecise reliability analysis using the Augmented Space Integral

AU - Yuan, Xiukai

AU - Faes, Matthias G.R.

AU - Liu, Shaolong

AU - Valdebenito, Marcos A.

AU - Beer, Michael

N1 - Funding Information: Xiukai Yuan would like to acknowledge financial support from NSAF (Grant No. U1530122), the Aeronautical Science Foundation of China (Grant No. ASFC-20170968002). Matthias Faes gratefully acknowledges the financial support of the Research Foundation Flanders (FWO) under grant number 12P3519N, as well as the Alexander von Humboldt foundation. Marcos Valdebenito acknowledges the support of ANID (National Agency for Research and Development, Chile ) under its program FONDECYT, grant number 1180271.

PY - 2021/6

Y1 - 2021/6

N2 - 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.

AB - 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.

KW - Augmented space

KW - Imprecise reliability analysis

KW - Interval variable

KW - Simulation-based method

UR - http://www.scopus.com/inward/record.url?scp=85101722587&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2021.107477

DO - 10.1016/j.ress.2021.107477

M3 - Article

AN - SCOPUS:85101722587

VL - 210

JO - Reliability engineering & system safety

JF - Reliability engineering & system safety

SN - 0951-8320

M1 - 107477

ER -