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The study aims to enhance structural reliability analysis by addressing the effects of correlation among input variables.

A Novel Correlation‐Based Reliability and Nonparametric Sensitivity Evaluation Method for Correlated Systems

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The study aims to enhance structural reliability analysis by addressing the effects of correlation among input variables.
Proposed a novel framework using fourth-order L-moments for reliability analysis.
Utilized polynomial transformation to map correlated variables to independent normal space.
Optimized coefficients for accurate moment matching and identified probable failure points.
Conducted non-parametric sensitivity analysis to assess the impacts of input variables.
Validated the method with numerical examples against Monte Carlo results.
Achieved a relative error reduction of 87.6% compared to traditional methods.
Errors in the cam mechanism case were kept under 1.0%.
Showed strong agreement with Monte Carlo results, indicating improved accuracy.

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A novel framework improves reliability estimation accuracy in correlated systems, suggesting better modeling outcomes.

Chen et al. (2026) studied this question.

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