System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?

Fan Wang, Heng Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


Many reliability problems involve correlated random variables. However, the probabilistic specification of random variables is commonly given in terms of marginals and correlations, which is actually incomplete because the data dependency needed for distribution modeling is not characterized. The implicitly assumed Gaussian dependence structure is not necessarily true and may bias the reliability result. To investigate the effect of correlations on system reliability under non-Gaussian dependence structures, a general approach to the probability distribution model construction based on the pair-copula decomposition is proposed. Numerical examples have highlighted the importance of dependence modeling in system reliability since large deviation in failure probabilities under different dependencies is observed. The method for identifying the best fit data dependency from data is later provided and illustrated with a retaining wall. It is demonstrated that the reliability result can be accurately estimated if the qualitative dependence structure is complemented to the available quantitative statistical information.

Original languageEnglish
Pages (from-to)94-104
Number of pages11
JournalReliability Engineering and System Safety
Publication statusPublished - 1 May 2018


  • Dependence structure
  • Marginals and correlations
  • Pair-copula decomposition
  • Probability distribution model
  • System reliability
  • Transformation method

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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