Efficient imprecise reliability analysis using the Augmented Space Integral
| Autor: | Marcos A. Valdebenito, Michael Beer, Shaolong Liu, Xiukai Yuan, Matthias Faes |
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| Rok vydání: | 2021 |
| Předmět: |
021110 strategic
defence & security studies Mathematical optimization 021103 operations research Computer science 0211 other engineering and technologies Structure (category theory) 02 engineering and technology Decoupling (cosmology) Space (mathematics) Industrial and Manufacturing Engineering Set (abstract data type) Uncertainty quantification Safety Risk Reliability and Quality Random variable Realization (probability) Reliability (statistics) |
| Zdroj: | RELIABILITY ENGINEERING & SYSTEM SAFETY |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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