Bounding the first excursion probability of linear structures subjected to imprecise stochastic loading

Matthias G.R. Faes, Marcos A. Valdebenito, David Moens, Michael Beer

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

This paper presents a highly efficient and accurate approach to determine the bounds on the first excursion probability of a linear structure that is subjected to an imprecise stochastic load. Traditionally, determining these bounds involves solving a double loop problem, where the aleatory uncertainty has to be fully propagated for each realization of the epistemic uncertainty or vice versa. When considering realistic structures such as buildings, whose numerical models often contain thousands of degrees of freedom, such approach becomes quickly computationally intractable. In this paper, we introduce an approach to decouple this propagation by applying operator norm theory. In practice, the method determines those epistemic parameter values that yield the bounds on the probability of failure, given the epistemic uncertainty. The probability of failure, conditional on those epistemic parameters, is then computed using the recently introduced framework of Directional Importance Sampling. Two case studies involving a modulated Clough-Penzien spectrum are included to illustrate the efficiency and exactness of the proposed approach.

Original languageEnglish
Article number106320
JournalComputers and Structures
Volume239
DOIs
StatePublished - 15 Oct 2020

Keywords

  • First excursion probability
  • Imprecise probabilities
  • Interval analysis
  • Linear structure
  • Stochastic loading

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