Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> 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 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. As a case-study, observed pairs of data for tsunami intensity and corresponding damage level from the central South Pacific tsunami on September 29, 2009, are used. The tsunami was triggered by an unprecedented earthquake doublet (Mw 8.1 and Mw 8.0) and seriously impacted numerous locations in the central South Pacific. Damage data related to 120 brick masonry residential buildings in American Samoa and Samoa islands were utilized. A six-tier damage scale was considered, using tsunami flow depth as the intensity measure.
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