Application of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 11, Issue 2
Abstract
Sedimentation in sewer networks is a major problem in urban hydrology. In comparison to the well-known classic sediment transport models, this study investigates the capabilities of soft computing methods, including multigene genetic programming (MGGP), gene expression programming, and multilayer perceptron to derive accurate sewer design models. A wide range of experimental data sets comprising fluid, flow, sediment, and pipe features was used to develop new models under the nondeposition with a deposited bed self-cleansing condition. The results showed better performances of the new models compared to the conventional ones in terms of statistical performance indices. The proposed MGGP model was found superior to its counterparts. It is an explicit model motivated to be used for self-cleansing sewer pipes design in practice.
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Data Availability Statement
All data used during the study are available in a repository online in accordance with funder data retention policies at https://doi.org/10.1061/(ASCE)PS.1949-1204.0000335. Code generated or used during the study are available from the corresponding author by request.
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 11 • Issue 2 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Oct 9, 2018
Accepted: Sep 4, 2019
Published online: Jan 14, 2020
Published in print: May 1, 2020
Discussion open until: Jun 14, 2020
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