Application of Bayesian Methods to Probabilistic Seismic Demand Analyses of Concrete Box-Girder Bridges

This paper proposes a set of probabilistic demand models of bridge components using the Bayesian parameter estimation method. To develop probabilistic demand models of individual bridge components, the material, structural, and geometric properties used in the bridge models serve as independent variables and the response data of engineering demand parameters for individual components monitored from the analyses serve as dependent response variables. To illustrate the proposed methodology, a typical reinforced two span, three frame curved concrete box-girder bridge in California is selected as a case study. Probabilistic numerical bridge models are developed, and then nonlinear time history analyses are performed using a set of ground motions representative of the seismic hazard. The significant input parameters are identified through a stepwise removal process in the Bayesian approach. The demand models generated in the Bayesian approach provides a more reliable estimation of the seismic demand compared to the traditional approach in which the ground motion intensity measure is the only input parameter.