Structural Engineering and Mechanics

  • 제25권3호
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  • Pages.347-363
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  • 2007
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Auxiliary domain method for solving multi-objective dynamic reliability problems for nonlinear structures

  • 투고 : 2005.02.21
  • 심사 : 2006.02.17
  • 발행 : 2007.02.20
⟨ 이전 논문 다음 논문 ⟩

초록

A novel methodology, referred to as Auxiliary Domain Method (ADM), allowing for a very efficient solution of nonlinear reliability problems is presented. The target nonlinear failure domain is first populated by samples generated with the help of a Markov Chain. Based on these samples an auxiliary failure domain (AFD), corresponding to an auxiliary reliability problem, is introduced. The criteria for selecting the AFD are discussed. The emphasis in this paper is on the selection of the auxiliary linear failure domain in the case where the original nonlinear reliability problem involves multiple objectives rather than a single objective. Each reliability objective is assumed to correspond to a particular response quantity not exceeding a corresponding threshold. Once the AFD has been specified the method proceeds with a modified subset simulation procedure where the first step involves the direct simulation of samples in the AFD, rather than standard Monte Carlo simulation as required in standard subset simulation. While the method is applicable to general nonlinear reliability problems herein the focus is on the calculation of the probability of failure of nonlinear dynamical systems subjected to Gaussian random excitations. The method is demonstrated through such a numerical example involving two reliability objectives and a very large number of random variables. It is found that ADM is very efficient and offers drastic improvements over standard subset simulation, especially when one deals with low probability failure events.

키워드

참고문헌

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피인용 문헌

  1. Uncertainty analysis of complex structural systems vol.80, pp.6‒7, 2009, https://doi.org/10.1002/nme.2549
  2. Reliability of deterministic non-linear systems subjected to stochastic dynamic excitation vol.85, pp.9, 2011, https://doi.org/10.1002/nme.3017
  3. Feasibility of FORM in finite element reliability analysis vol.32, pp.2, 2010, https://doi.org/10.1016/j.strusafe.2009.10.001
  4. The role of the design point for calculating failure probabilities in view of dimensionality and structural nonlinearities vol.32, pp.2, 2010, https://doi.org/10.1016/j.strusafe.2009.08.004
  5. Bayesian post-processor and other enhancements of Subset Simulation for estimating failure probabilities in high dimensions vol.92-93, 2012, https://doi.org/10.1016/j.compstruc.2011.10.017
  6. General network reliability problem and its efficient solution by Subset Simulation vol.40, 2015, https://doi.org/10.1016/j.probengmech.2015.02.002
  7. Uncertain linear systems in dynamics: Retrospective and recent developments by stochastic approaches vol.31, pp.11, 2009, https://doi.org/10.1016/j.engstruct.2009.07.005
  8. Evaluating small failure probabilities of multiple limit states by parallel subset simulation vol.25, pp.3, 2010, https://doi.org/10.1016/j.probengmech.2010.01.003
  9. Subset simulation for non-Gaussian dependent random variables given incomplete probability information vol.67, 2017, https://doi.org/10.1016/j.strusafe.2017.04.005
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  14. Efficient Monte Carlo simulation procedures in structural uncertainty and reliability analysis - recent advances vol.32, pp.1, 2007, https://doi.org/10.12989/sem.2009.32.1.001