An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load

Xiukai Yuan, Shaolong Liu, Matthias Faes, Marcos A. Valdebenito, Michael Beer

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Structural performance is affected by deterioration processes and external loads. Both effects may change over time, posing a challenge for conducting reliability analysis. In such context, this contribution aims at assessing the reliability of structures where some of its parameters are modeled as random variables, possibly including deterioration processes, and which are subjected to stochastic load processes. The approach is developed within the framework of importance sampling and it is based on the concept of composite limit states, where the time-dependent reliability problem is transformed into a series system with multiple performance functions. Then, an efficient two-step importance sampling density function is proposed, which splits time-invariant parameters (random variables) from the time-variant ones (stochastic processes). This importance sampling scheme is geared towards a particular class of problems, where the performance of the structural system exhibits a linear dependency with respect to the stochastic load for fixed time. This allows calculating the reliability associated with the series system most efficiently. Practical examples illustrate the performance of the proposed approach.

Original languageEnglish
Article number107699
JournalMechanical Systems and Signal Processing
Volume159
DOIs
StatePublished - Oct 2021

Keywords

  • Composite limit state functions
  • Cumulative failure probability
  • Importance sampling
  • Simulation-based method
  • Stochastic load
  • Time-variant structure

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