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Uncertain information on input parameters of reliability models is usuallymodeled by considering these parameters as random, and described by marginaldistributions and a dependence structure of these variables. In numerousreal-world applications, while information is mainly provided by marginaldistributions, typically from samples , little is really known on thedependence structure itself. Faced with this problem of incomplete or missinginformation, risk studies are often conducted by considering independence ofinput variables, at the risk of including irrelevant situations. This approachis especially used when reliability functions are considered as black-boxcomputational models. Such analyses remain weakened in absence of in-depthmodel exploration, at the possible price of a strong risk misestimation.Considering the frequent case where the reliability output is a quantile, thisarticle provides a methodology to improve risk assessment, by exploring a setof pessimistic dependencies using a copula-based strategy. In dimension greaterthan two, a greedy algorithm is provided to build input regular vine copulasreaching a minimum quantile to which a reliability admissible limit value canbe compared, by selecting pairwise components of sensitive influence on theresult. The strategy is tested over toy models and a real industrialcase-study. The results highlight that current approaches can providenon-conservative results, and that a nontrivial dependence structure can beexhibited to define a worst-case scenario.

Detecting and modeling worst-case dependence structures between random inputs of computational reliability models