Real-time evaluation and management of extreme traffic load risk on main road’s bridges

The risk induced by extreme traffic loads on bridges was rarely investigated and the existing methods require computationally expensive elaborations that are not compatible with a real time risk management. Traditional approaches to reduce risk suggested the optimisation of bridge maintenance plans. Conversely, approaches that real-time evaluate and manage the risk are missing. Moreover, the integration of emerging prediction models, such as Artificial Neural Networks, was never explored. This study fills the previous gaps by proposing a three-block methodology. It adopts Weight-In-Motion systems to collect site-traffic load data, formulates a probabilistic Risk Prediction Model to estimate frequency and severity of bridge failure events according to Eurocodes, and simulates an Intelligent Transportation System (ITS) architecture to apply real time management actions. The methodology was tested on 2.5M+ vehicles raw WIM data gathered along the ring road of Brescia (Italy). Bridge failure events resulted significantly more frequent than that prescribed by Eurocode, and factors of compliance with Traffic Code mass limits prescriptions had the more significant effect on risk predictions. The findings suggest a greater attention when permits for extremely overweighed vehicles are issued, as well as the implementation of enforcement strategies and ITS-based architectures for the real time risk management.