Next Article in Journal
Fiber Optics Sensors in Asphalt Pavement: State-of-the-Art Review
Next Article in Special Issue
A Failure Risk-Based Culvert Renewal Prioritization Framework
Previous Article in Journal
Sensitivity of the Flow Number to Mix Factors of Hot-Mix Asphalt
Previous Article in Special Issue
Fit-for-Purpose Infrastructure Asset Management Framework for Water Utilities Facing High Uncertainties
Open AccessArticle

Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

Department of Hydraulic Engineering and Environment, Universitat Politècnica de València, Camino de Vera s/n., 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Infrastructures 2019, 4(2), 35; https://doi.org/10.3390/infrastructures4020035
Received: 10 April 2019 / Revised: 5 June 2019 / Accepted: 6 June 2019 / Published: 8 June 2019
(This article belongs to the Special Issue Water Infrastructure Asset Management)
The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions. View Full-Text
Keywords: drainage networks; extreme rainfalls; problem size reduction; rehabilitation; multi-objective optimization; SWMM drainage networks; extreme rainfalls; problem size reduction; rehabilitation; multi-objective optimization; SWMM
Show Figures

Graphical abstract

MDPI and ACS Style

Ngamalieu-Nengoue, U.A.; Iglesias-Rey, P.L.; Martínez-Solano, F.J. Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology. Infrastructures 2019, 4, 35.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop