Exploring the structural factors of resilience in urban drainage systems: a large-scale stochastic computational experiment

Abstract

The focus of infrastructure design and management has turned from a reliability-based approach to a resilience-based one. Resilience is a system's ability to maintain its function and minimize failure consequences when faced with exceptional conditions. This study carries out a large-scale computational experiment to study how resilience is affected by system's structure in a combined sewer system. We build a stochastic generation model, involving a random sampling of facility locations and a graph-based random walk sampling algorithm to generate various layouts of pipelines. The performance of these virtual systems are assessed in the Storm Water Management Model. We apply statistical techniques on these samples to study the relation between resilience and system structure. Results show that the number of combined sewer overflow (CSO) outfalls is a more important factor of resilience compared to the number of wastewater treatment plants (WWTPs). Some locations are found more preferable for WWTP or CSO outfall placement, while adding WWTPs or outfalls at other locations might even lower the system's resilience. Size of the sub-catchments of the CSO outfalls also affects resilience. Although this effect is statistically significant, the extent is not remarkable compared to other factors. We further study the structural features of the cost-effective systems. The highest achievable resilience level increases as the number of CSO outfalls decreases and so does system's cost. This results from the difference in CSO quantity, therefore this dilemma can be cut off by end-of-pipe storage or treatment which specifically tackles CSO. The conclusion of this study provides an insight into the structural factors of combined sewer systems’ resilience and can provide guidance for system's planning.