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

Authors:
N. Pedroni;E. Zio;E. Ferrario;A. Pasanisi;M. Couplet
Affiliations:
Energy Department, Politecnico di Milano, Via Ponzio, 34/3 - 20133 Milano, Italy;Energy Department, Politecnico di Milano, Via Ponzio, 34/3 - 20133 Milano, Italy and Ecole Centrale Paris, Grande Voie des Vignes, 92295, Chatenay Malabry-Cedex, France;Ecole Centrale Paris, Grande Voie des Vignes, 92295, Chatenay Malabry-Cedex, France;Electricité de France, Chatou, France;Electricité de France, Chatou, France
Venue:
Computers and Structures
Year:
2013

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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.