A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems - Yu, Jin-Zhu; Whitman, Mackenzie; Kermanshah, Amirhassan; Baroud, Hiba (Reliability engineering & system safety, 2021)
Measuring the performance of infrastructure networks is critical to the allocation of resources before, during, and after a system’s disruption. However, the lack of data often hinders the ability to accurately estimate infrastructure performance, resulting in uncertainty in its evaluation which can lead to biased estimates. To address this challenge, this study develops a Bayesian approach to measure the performance of the infrastructure network at the component level and incorporate it in the evaluation of the system-level serviceability. Component fragility metrics are estimated using a hierarchical Bayesian model and then integrated into the system serviceability assessment using Monte Carlo simulation and a shortest-path algorithm. These performance measures can be dynamically updated as more data becomes available. A case study of the water distribution system of Shelby County in Tennessee subject to earthquake and flood hazards is presented to illustrate the proposed approach. Results show that system topology is more important in determining component functionality under seismic hazard while vulnerability is the dominant factor in the case of flood hazard. •A model combining infrastructure component vulnerability and system serviceability.•Epistemic and aleatory uncertainty incorporated in component failure models.•Hierarchical Bayesian approach to update component fragility formulations.•Integration of network, simulation, and Bayesian models in system serviceability.•Evaluation of water distribution serviceability under earthquake and flood risk.