  Uncertain information on input parameters of reliability models is usually
modeled by considering these parameters as random, and described by marginal
distributions and a dependence structure of these variables. In numerous
real-world applications, while information is mainly provided by marginal
distributions, typically from samples , little is really known on the
dependence structure itself. Faced with this problem of incomplete or missing
information, risk studies are often conducted by considering independence of
input variables, at the risk of including irrelevant situations. This approach
is especially used when reliability functions are considered as black-box
computational models. Such analyses remain weakened in absence of in-depth
model exploration, at the possible price of a strong risk misestimation.
Considering the frequent case where the reliability output is a quantile, this
article provides a methodology to improve risk assessment, by exploring a set
of pessimistic dependencies using a copula-based strategy. In dimension greater
than two, a greedy algorithm is provided to build input regular vine copulas
reaching a minimum quantile to which a reliability admissible limit value can
be compared, by selecting pairwise components of sensitive influence on the
result. The strategy is tested over toy models and a real industrial
case-study. The results highlight that current approaches can provide
non-conservative results, and that a nontrivial dependence structure can be
exhibited to define a worst-case scenario.
