Hierarchical propagation of probabilistic and non-probabilistic uncertainty in the parameters of a risk model

Abstract : We consider a model for the risk-based design of a flood protection dike, and use probability distributions to represent aleatory uncertainty and possibility distributions to describe the epistemic uncertainty associated to the poorly known parameters of such probability distributions. A hybrid method is introduced to hierarchically propagate the two types of uncertainty, and the results are compared with those of a Monte Carlo-based Dempster-Shafer approach employing independent random sets and a purely probabilistic, two-level Monte Carlo approach: the risk estimates produced are similar to those of the Dempster-Shafer method and more conservative than those of the two-level Monte Carlo approach.
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Nicola Pedroni, Enrico Zio, Elisa Ferrario, Alberto Pasanisi, Mathieu Couplet. Hierarchical propagation of probabilistic and non-probabilistic uncertainty in the parameters of a risk model. Computers and Structures, Elsevier, 2013, Online, pp.1-15. ⟨10.1016/j.compstruc.2013.02.003⟩. ⟨hal-00839639⟩

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