Epistemic Uncertainties in Fragility Functions Derived from Post-Disaster Damage Assessments

Abstract

Fragility functions define the probability of meeting or exceeding some damage measure (DM) for a given level of engineering demand (e.g., base shear) or hazard intensity measure (IM; e.g., wind speed, and peak ground acceleration). Empirical fragility functions specifically refer to fragility functions that are developed from posthazard damage assessments, and, as such, they define the performance of structures or systems as they exist in use and under true natural hazard loading. This paper describes major sources of epistemic uncertainty in empirical fragility functions for building performance under natural hazard loading, and develops and demonstrates methods for quantifying these uncertainties using Monte Carlo simulation methods. Uncertainties are demonstrated using a dataset of 1,241 residential structures damaged in the May 22, 211, Joplin, Missouri, tornado. Uncertainties in the intensity measure (wind speed) estimates were the largest contributors to the overall uncertainty in the empirical fragility functions. With a sufficient number of samples, uncertainties because of potential misclassification of the observed damage levels and sampling error were relatively small. The methods for quantifying uncertainty in empirical fragility functions are demonstrated using tornado damage observations, but are applicable to any other natural hazard as well.