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The risk associated with extreme traffic loads on bridges has seldom been explored, with State-of-the-art evaluation methods being time-consuming and unsuitable for fast risk management. Traditional risk management advocates optimizing offline bridge maintenance plans. In contrast, novel approaches that can assess and manage this risk live through Intelligent Transportation Systems (ITSs) are lacking. This study addresses these gaps with a three-block framework. It utilizes Weigh-In-Motion (WIM) systems for collecting bridge-specific traffic load data, develops a probabilistic Risk Prediction Model for estimating the frequency and severity of overloading events drawing on current Structural Design Codes (SDCs), and simulates an ITS-based architecture for implementing management actions. The framework was tested on 2. 5M + WIM raw data records gathered from the ring road of Brescia, Italy. Results showed that bridge design loads were overcome more frequently than SDCs prescriptions, and violations of the Traffic Code mass limit significantly affected risk predictions. These findings underscore the need for increased attention when issuing permits for extremely overweighted vehicles and encourage enforcement strategies implemented by ITS-based architectures for real-time risk management.
Ventura et al. (Tue,) studied this question.