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

Nicola Pedroni (1) , Enrico Zio (1) , Elisa Ferrario (1) , Alberto Pasanisi (2) , Mathieu Couplet (2)

1 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
2 EDF R&D STEP - Simulation et Traitement de l'information pour l'Exploitation des systèmes de Production

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.

Mots clés: hierarchical uncertainty; possibility distributions; fuzzy interval analysis; two-level Monte Carlo method; dependences; flood protection dike

Domaines: Sciences de l'ingénieur [physics]

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 & Structures, 2013, Online, pp.1-15. ⟨10.1016/j.compstruc.2013.02.003⟩. ⟨hal-00839639⟩