Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Infrastructure owners and operators, or governmental agencies, need rapid screening tools to prioritize detailed risk assessment and retrofit resources allocation. This paper provides one such tool, for use by highway administrations, based on Bayesian belief network (BBN) and aimed at replacing so-called generic or typological seismic fragility functions for reinforced concrete girder bridges. Resources for detailed assessments should be allocated to bridges with highest consequence of damage, for which site hazard, bridge fragility, and traffic data are needed. The proposed BBN based model is used to quantify seismic fragility of bridges based on data that can be obtained by visual inspection and engineering drawings. Results show that the predicted fragilities are of sufficient accuracy for establishing relative ranking and prioritizing. While the actual data and seismic hazard employed to train the network (establishing conditional probability tables) refer to the Italian bridge stock, the network structure and engineering judgment can easily be adopted for bridges in different geographical locations.