A Bayesian network model to assess seismic risk of reinforced concrete girder bridges Franchin, Paolo; Lupoi, Alessio; Noto, Fabrizio; Tesfamariam, Solomon
Infrastructure owners or governmental agencies need tools for rapid screening of assets in order to prioritize resources allocation for detailed risk assessment. This paper provides one such tool based on Bayesian Networks and aimed at replacing so-called generic/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 presented Bayesian Network predicts the seismic fragility of a bridge at a given site based on data that can be obtained by visual inspection at low cost. Results show that the predicted fragilities are of sufficient accuracy for establishing relative ranking based on risk and assign priorities. While the actual data employed to train the network (establishing conditional probability tables) refer to the Italian bridge stock, the network structure and engineering judgment behind it can be easily transferred to other situations.
Item Citations and Data
Attribution-NonCommercial-NoDerivs 2.5 Canada