Life-cycle management of deteriorating bridge networks with network-level risk bounds and system reliability analysis - Yang, David Y.; Frangopol, Dan M. (Structural safety, 2020)
•A novel method is proposed for life-cycle management of bridge networks.•The method adopts a non-simulation approach based on network-level risk bounds.•Probabilities of failure scenarios are obtained in system reliability analysis.•Optimal maintenance plans are determined by minimizing network-level risk.•The method can account for the impacts of unlikely, high-impact events. Structural deterioration poses a substantial threat to the safety, serviceability, and functionality of bridges. Since bridges are connected in transportation networks, their failure can dramatically alter traffic flow, causing immense social consequences such as large-scale traffic delay and additional vehicle operating cost. In this paper, a novel method is proposed for life-cycle management of deteriorating bridge networks. The proposed method aims at minimizing network-level risks associated with deterioration-induced bridge failure. To combat the deficiencies of existing Monte Carlo simulation-based risk assessment methods, the proposed method adopts a non-simulation approach that relies on risk bounds of deteriorating bridge networks. In particular, this method uses system reliability analysis to determine the occurrence probabilities of various bridge failure scenarios in a network. For each failure scenario considered, traffic assignment is conducted to predict the traffic flow in the damaged network and the network-level consequences. The upper and lower bounds of the network-level risk are then formulated, allowing for efficient and accurate risk assessment with only a few traffic assignment operations. Estimated by the average value of its upper and lower bounds, the network-level risk is used as the optimization objective in a metaheuristic search procedure to obtain optimal life-cycle maintenance plans including the maintenance schedule and the investment on each maintenance action. The proposed method excels in its ability to account for the impacts of unlikely, yet high-impact events, as well as its ability to obtain optimal or near-optimal life-cycle maintenance plans that can more effectively reduce risks than those obtained using simulation-based methods.