Assessment of potential mechanical damage to tanks of flammable liquids

Abstract

The Bayesian updating with the new data represented by the set of continuous distributions is carried out by discretizing these distributions. The discretization yields a new sample which is entered into the Bayes theorem through likelihood function. The sample created by the discretization consists of fragility function values which have equal epistemic weights. The proposed scheme of discretization is considered an alternative to a posterior averaging approach. This approach is suitable for Bayesian updating with uncertain data; however, it is applicable to the case where data uncertainty is modeled by discrete distributions of epistemic uncertainty. Several aspects of numerical implementation of the proposed discretization approach and subsequent updating are discussed and illustrated by an example.