ABSTRACT
P. Baraldi & E. Zio Energy Department, Politecnico di Milano, Milano, Italy
ABSTRACT: In event tree analysis, probabilistic distributions have been traditionally used to characterize the epistemic uncertainty associated to the probability of occurrence of an event. However, it has been argued that in certain instances such uncertainty may be best accounted for by fuzzy or possibilistic distributions. This seems the case in particular for events for which the information available is scarce and of qualitative nature. In this work, an hybrid method which jointly propagates probabilistic and possibilistic uncertainties is considered. The joint propagation of the uncertainties is achieved by resorting to an integration of the Monte Carlo sampling and possibility theory. The method is applied to a case study concerning the uncertainties in the probability of occurrence of a severe consequence accident in an event tree analysis of a nuclear power plant.
