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contributor authorAli Danandeh Mehr
contributor authorMir Jafar Sadegh Safari
date accessioned2022-01-30T20:03:44Z
date available2022-01-30T20:03:44Z
date issued2020
identifier other%28ASCE%29PS.1949-1204.0000449.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4266448
description abstractSedimentation 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.
publisherASCE
titleApplication of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes
typeJournal Paper
journal volume11
journal issue2
journal titleJournal of Pipeline Systems Engineering and Practice
identifier doi10.1061/(ASCE)PS.1949-1204.0000449
page04020002
treeJournal of Pipeline Systems Engineering and Practice:;2020:;Volume ( 011 ):;issue: 002
contenttypeFulltext


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