Illustrating a Bayesian approach to seismic collapse risk assessment

Abstract

In this study, we present a Bayesian method for efficient collapse response assessment of structures. The method facilitates integration of prior information on collapse response with data from nonlinear structural analyses in a Bayesian setting to provide a more informed estimate of the collapse risk. The prior information on collapse can be obtained from a variety of sources, including information on the building design criteria and simplified linear dynamic analysis or nonlinear static (pushover) analysis. The proposed method is illustrated on a four-story reinforced concrete moment frame building to assess its seismic collapse risk. The method is observed to significantly improve the statistical and computational efficiency of collapse risk predictions compared to alternative methods.