Sewer network design : heuristic algorithm for hydraulic optimisation

Date
2017-09
Journal Title
Journal ISSN
Volume Title
Publisher
South African Institution of Civil Engineering
Abstract
ENGLISH ABSTRACT: For a given sewer network layout and choice of pipe material, the total installed cost of the network is determined mainly by the pipe diameters and slopes. Hydraulic design optimisation is the task of determining suitable pipe diameters and slopes so as to minimise the installed cost of the network. This is a complex problem for which numerous solution approaches have been proposed. Recently the use of metaheuristic algorithms, like Ant Colony Optimisation (ACO) for example, has gained popularity, and they perform well for a given static layout. However, their computational complexity precludes their use in simultaneous layout and hydraulic optimisation, where a complete hydraulic optimisation has to be performed for each layout. This paper proposes a computationally efficient method for near optimal hydraulic design of a gravity sewer network. It makes use of required minimum slope information to heuristically determine optimal pipe sizes and slopes. The method is used to solve two benchmark problems and is shown to obtain good solutions while being computationally extremely efficient. Therefore it is ideally suited to be used in combination with a metaheuristic algorithm aimed at optimising the network layout.
Description
CITATION: De Villiers, N., Van Rooyen, G. C. & Middendorf, M. 2017. Sewer network design : heuristic algorithm for hydraulic optimisation. Journal of the South African Institution of Civil Engineering, 59(3):48-56, doi:10.17159/2309-8775/2017/v59n3a6.
The original publication is available at http://www.scielo.org.za
Keywords
Sewer-pipe, Hydraulic engineering, Sewer-pipe -- Joints, Heuristic algorithms
Citation
De Villiers, N., Van Rooyen, G. C. & Middendorf, M. 2017. Sewer network design : heuristic algorithm for hydraulic optimisation. Journal of the South African Institution of Civil Engineering, 59(3):48-56, doi:10.17159/2309-8775/2017/v59n3a6