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Risk-based renewal planning is essential for the reliable and continuous functioning of infrastructure systems. In this paper, we propose a risk assessment framework for storm sewer networks considering both hydraulic capacity and asset deterioration. The method is designed for a city-scale analysis and can provide an insightful result even when there is incomplete information. The objective is to assign a relative risk score to each pipe to inform replacement priorities. Moreover, we adopt a dynamic framework to proactively assess risk in different time horizons to investigate the impact of climate change and urbanization. A Dynamic Bayesian Network (DBN) model is used to capture dependencies among indicators, quantify uncertainty, and update belief when new information becomes available. Geographic information system (GIS) applications are used to collect and process model input data as well as visualize analysis results. Finally, the method is demonstrated on a storm sewer network in the city of Vernon, Canada.

Storm sewer pipe renewal planning considering deterioration, climate change, and urbanization: a dynamic Bayesian network and GIS framework