Disaggregated Empirical Fragility Modeling and Bayesian Parameter Updating for Buildings in Haiti

Quantifying the vulnerability of buildings is fundamental to seismic risk mitigation, and fragility curves are among the most widely used tools for this purpose. With the growing availability and access to post-earthquake damage data from reconnaissance campaigns and measured ground motion parameters, new opportunities have emerged to derive and refine fragility models, improving the seismic damage prediction models and the quantification of seismic risk. Following the 2010 Haiti earthquake, extensive datasets were compiled that include both structural and geotechnical characteristics, as well as observed damage states for a wide range of building classes. In this study, two such datasets are employed in a complementary manner to develop disaggregated fragility models to probabilistically quantify structural damage conditioned on specific building and site attributes, rather than parameters averaged over an entire building stock. The first dataset comprises approximately 335,000 building assessment tags collected, and it is used to develop a set of baseline fragility curves conditioned on parameters such as number of stories, soil type, wall system, topography, roof type, and building age. The second dataset comprises 170 reinforced concrete buildings with more detailed and reliable information, and it is used to update the baseline fragility models using Bayesian estimation. The Bayesian updating introduces fragilities specific to the presence of captive columns and priority index, a metric representing the ratio of wall and column area to floor area. Bayesian updating is performed within a Markov Chain Monte Carlo (MCMC) framework using the Metropolis–Hastings algorithm. The resulting fragility functions reveal the high vulnerability of the Haitian building stock and demonstrate how multiple site and structural attributes influence seismic fragility.