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    Application of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes

    Source: Journal of Pipeline Systems Engineering and Practice:;2020:;Volume ( 011 ):;issue: 002
    Author:
    Ali Danandeh Mehr
    ,
    Mir Jafar Sadegh Safari
    DOI: 10.1061/(ASCE)PS.1949-1204.0000449
    Publisher: ASCE
    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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      Application of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4266448
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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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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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