Abstract

In recent years, a significant increase in the number of extreme rains around the world has been observed, which has caused an overpressure of urban drainage networks. The lack of capacity to evacuate this excess water generates the need to rehabilitate drainage systems. There are different rehabilitation methodologies that have proven their validity; one of the most used is the heuristic approach. Within this approach, the use of genetic algorithms has stood out for its robustness and effectiveness. However, the problem to be overcome by this approach is the large space of solutions that algorithms must explore, affecting their efficiency. This work presents a method of search space reduction applied to the rehabilitation of drainage networks. The method is based on reducing the initially large search space to a smaller one that contains the optimal solution. Through iterative processes, the search space is gradually reduced to define the final region. The rehabilitation methodology contemplates the optimization of networks using the joint work of the installation of storm tanks, replacement of pipes, and implementation of hydraulic control elements. The optimization model presented uses a pseudo genetic algorithm connected to the SWMM model through a toolkit. Optimization problems consider a large number of decision variables, and could require a huge computational effort. For this reason, this work focuses on identifying the most promising region of the search space to contain the optimal solution and to improve the efficiency of the process. Finally, this method is applied in real networks to show its validity.

Highlights

  • Drainage systems around the world have experienced an increase in operating pressure in recent years

  • The methodology based on the optimization of the network considering the replacement of pipes, installation of storm tanks, and inclusion of hydraulic controls has proven to be a valid alternative to solve these types of problems

  • In the E-Chico network in particular, the search space (SS) is reduced from a magnitude of 140 to a magnitude of 12, while, in the Ayurá network, much larger than E-Chico, the SS is reduced from a magnitude of 344 to one of 82

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Summary

Introduction

Drainage systems around the world have experienced an increase in operating pressure in recent years. During the last few decades, cities have experienced a constant process of growth, which has reduced the green areas that surround them, replacing them with highly impermeable surfaces. These factors have led many cities to appreciate the increase in surface runoff, and in many cases, the collapse of their drainage systems [4,5,6]. Concern increases as cities are increasingly exposed to flood risk [7,8] To face this problem, different options for optimizing drainage networks have been developed

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