Development of a stochastic framework to design/rehabilitate urban stormwater drainage systems based on a resilient approach

Abstract

Urban floods may cause a wide range of undesirable consequences in populated areas when drainage networks do not have enough capacity or encounter unexpected loads such as blockage. Although both external loads (like heavy rainfalls) and internal factors such as structural failure/blockage of different parts of networks play an important role on urban inundation, most of the implemented design approaches only consider effects of external loads on Urban Stormwater Drainage Systems (USDSs). In this study, a new resilient and stochastic approach is developed by connecting a multi objective optimization algorithm to a numerical solver/meta-model within a Copula-based Monte-Carlo framework in order to design/rehabilitate of USDSs considering the related probabilities of external and internal unexpected conditions of loading. Results indicate the importance of considering the resilience in the rehabilitation of Tehran Stormwater Drainage System (TSDS), Tehran, Iran. Optimal strategies including a set of bypass lines, relief tunnels, and storage units, exhibited satisfactory performance in terms of enhancing the network resiliency.