Abstract
Abstract. The present work proposes a simulation-based Bayesian method for parameter
estimation and fragility model selection for mutually exclusive and
collectively exhaustive (MECE) damage states. This method uses an adaptive
Markov chain Monte Carlo simulation (MCMC) based on likelihood estimation
using point-wise intensity values. It identifies the simplest model that
fits the data best, among the set of viable fragility models considered. The
proposed methodology is demonstrated for empirical fragility assessments for
two different tsunami events and different classes of buildings with varying
numbers of observed damage and flow depth data pairs. As case studies,
observed pairs of data for flow depth and the corresponding damage level from
the South Pacific tsunami on 29 September 2009 and the Sulawesi–Palu
tsunami on 28 September 2018 are used. Damage data related to a total of five
different building classes are analysed. It is shown that the proposed
methodology is stable and efficient for data sets with a very low number of
damage versus intensity data pairs and cases in which observed data are
missing for some of the damage levels.
Subject
General Earth and Planetary Sciences
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