Experimental Studies of Self-Cleansing Drainage System Design: A Review

Safari, Mir Jafar Sadegh; Mohammadi, Mirali; Ghani, Aminuddin Ab.

doi:10.1061/(asce)ps.1949-1204.0000335

Cited by 50 publications

(15 citation statements)

References 71 publications

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“…mean particle diameter, volumetric sediment concentration and hydraulic radius, amongst others parameters. According to Safari, Mohammadi, and Ghani (2018), these self-cleansing criteria studies can be classified into two major groups: bed sediment motion and non-deposition.…”

Section: Introductionmentioning

confidence: 99%

“…incipient motion criterion) or scouring of existing sediment bed (i.e. scouring criterion) (Vongvisessomjai, Tingsanchali, and Babel 2010;Safari et al 2017;Safari, Mohammadi, and Ghani 2018). Several studies in this group can be found in the literature of incipient motion Nalluri 1975, 1984;Ab Ghani et al 1999) and scouring (Camp 1946).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies in this group can be found in the literature of incipient motion Nalluri 1975, 1984;Ab Ghani et al 1999) and scouring (Camp 1946). A full review of bed sediment motion studies has been prepared by Safari, Mohammadi, and Ghani (2018).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the non-deposition without deposited bed criterion is studied, which is a conservative criterion useful to the design of self-cleansing sewer pipes, according to Butler, May, and Ackers (2003), Vongvisessomjai, Tingsanchali, and Babel (2010) and Safari, Mohammadi, and Ghani (2018). To apply this criterion, it is necessary to identify several parameters such as size, concentration and density of the sediments (Vongvisessomjai, Tingsanchali, and Babel 2010) and the mode of transport of the particles inside the pipes, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting bedload sediment transport of non-cohesive material in sewer pipes using evolutionary polynomial regression – multi-objective genetic algorithm strategy

Montes

Berardi

Kapelan

et al. 2020

Urban Water Journal

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2020)Predicting bedload sediment transport of non-cohesive material in sewer pipes using evolutionary polynomial regression -multi-objective genetic algorithm strategy, Urban Water Journal, 17:2, 154-162, ABSTRACT Sediment transport in sewer systems is an important issue of interest to engineering practice. Several models have been developed in the past to predict a threshold velocity or shear stress resulting in selfcleansing flow conditions in a sewer pipe. These models, however, could still be improved. This paper develops three new self-cleansing models using the Evolutionary Polynomial Regression-Multi-Objective Genetic Algorithm (EPR-MOGA) methodology applied to new experimental data collected on a 242 mm diameter acrylic pipe. The three new models are validated and compared to the literature models using both new and previously published data sets. The results obtained demonstrate that three new models have improved prediction accuracy when compared to the literature ones. The key feature of the new models is the inclusion of pipe slope as a significant explanatory factor in estimating the threshold selfcleansing velocity. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting bedload sediment transport of non-cohesive material in sewer pipes using evolutionary polynomial regression – multi-objective genetic algorithm strategy

Montes

Berardi

Kapelan

et al. 2020

Urban Water Journal

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“…The former covers the "incipient motion" and "scouring" criteria (Bong, Lau, & Ab Ghani, 2013;Bong, Lau, Ab Ghani, & Chan, 2016;Novak & Nalluri, 1984;Safari, Aksoy, Unal, & Mohammadi, 2017b) while the latter includes "nondeposition with and deposited bed, " "nondeposition without deposited bed, " and "incipient deposition" conditions (Aksoy, Safari, Unal, & Mohammadi, 2017;Ota & Nalluri, 2003;Ota & Perrusquía, 2013;Safari, Aksoy, & Mohammadi, 2015Safari & Danandeh Mehr, 2018;Safari, Shirzad, & Mohammadi, 2017;Vongvisessomjai, Tingsanchali, & Babel, 2010). Detailed comparison of the different self-cleansing criteria is reported in Safari, Mohammadi, and Ab Ghani (2018).…”

Section: Introductionmentioning

confidence: 99%

Self‐cleansing design of sewers: Definition of the optimum deposited bed thickness

Safari

Shirzad

2019

Water Environment Research

Self Cite

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Sediment deposits may influence the performance of the sewer systems. Sediments are the main store of pollutants which causes sewer systems overflows. In order to prevent the deposition of sediment in sewer systems, self‐cleansing design criteria are used. Among various criteria, “self‐cleansing with deposited bed” criterion is found appropriate for designing of large sewers. Allowing small thickness of deposited bed in large sewers decreases the required channel bed slope and construction costs. This study presents a bed load self‐cleansing model established on wide ranges of experimental data. Comparison of developed model with the existing models in the literature shows its higher performance on variety of data sources. Examples for the application of the model are presented, and design graphs and charts for different pipe sizes are proposed. As a result, the optimum deposited bed thickness is found 1%–5% of the pipe diameter; however, 1% is recommended for sewer pipes design. Practitioner points Nondeposition sediment transport condition with a bed deposit criterion is found appropriate for large sewer pipe design. The precision of a model is linked to the ranges of experimental data and parameters used for the models development. The optimum range of deposited bed thickness can be adopted in the range of 1% to 5% of the pipe diameter.

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Data-Driven Approaches for Estimation of Particle Froude Number in a Sewer System

Shakya

Agarwal

Deshpande

et al. 2023

Lecture Notes in Civil Engineering

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Experimental Studies of Self-Cleansing Drainage System Design: A Review

Cited by 50 publications

References 71 publications

Predicting bedload sediment transport of non-cohesive material in sewer pipes using evolutionary polynomial regression – multi-objective genetic algorithm strategy

Predicting bedload sediment transport of non-cohesive material in sewer pipes using evolutionary polynomial regression – multi-objective genetic algorithm strategy

Self‐cleansing design of sewers: Definition of the optimum deposited bed thickness

Data-Driven Approaches for Estimation of Particle Froude Number in a Sewer System

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