An adaptive scheme for reliability-based global design optimization: A Markov chain Monte Carlo approach

H. A. Jensen, D. J. Jerez, M. Valdebenito

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

The reliability-based design of structural dynamic systems under stochastic excitation is presented. The design problem is formulated in terms of the global minimization of the system failure probability. The corresponding optimization problem is solved by an effective stochastic simulation scheme based on the transitional Markov chain Monte Carlo method. Although the scheme is quite general, is computationally very demanding due to the large number of reliability analyses required during the design process. To cope with this difficulty, an advanced simulation technique is combined with an adaptive surrogate model for estimating the failure probabilities. In particular, a kriging meta-model is selected in the present formulation. The algorithm generates a set of nearly optimal solutions uniformly distributed over a neighborhood of the optimal solution set. Such set can be used for exploration of the global sensitivity of the system reliability. Several illustrative examples are presented to investigate the applicability and effectiveness of the proposed design scheme.

Original languageEnglish
Article number106836
JournalMechanical Systems and Signal Processing
Volume143
DOIs
StatePublished - Sep 2020

Keywords

  • Dynamical systems
  • Global structural optimization
  • Kriging approximation
  • Markov sampling method
  • Reliability-based design
  • Stochastic excitation

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