Dynamic Bayesian network-based system-level evaluation on fatigue reliability of orthotropic steel decks

Abstract Fatigue fractures can be frequently observed in welded joints in orthotropic steel decks (OSDs) after just a few decades of operation, which become the major deterioration mechanism deterring the serviceability of OSDs. In this paper, a novel dynamic Bayesian network (DBN) model has been established for the fatigue reliability analysis of OSDs at system-level. The exact inference algorithm is applied in the DBN model with discrete variables. Special modifications have been made on the existing algorithm to improve the computational efficiency in dealing with the deck system consisting of a considerable number of joints. Using the DBN model, the fatigue reliability of welded joints can be predicted and updated with the inspection and monitoring results at system-level. At the same time, a framework is established for the system-level reliability considering the fatigue fracture of rib-to-deck (RD) joints, the dominant cracking pattern affecting the serviceability of OSDs. For illustration, a typical OSD bridge in China has been selected to carry out a case study. To derive the stress spectrum required by the DBN model, the stochastic traffic model is employed, and the influence-based Monte Carlo simulations have been carried out. As a result, the fatigue reliability can be predicted at both component- and system-levels. Meanwhile, the observation of the traffic and the inspection result has been fused into the DBN model to update the deteriorating state of the deck system. Besides, the effect of enhancement and maintenance has been highlighted, including the enhancement in fatigue strength at the construction stage, and the repair and traffic control during the operation stage.