Non-deposition design criteria for sewers with part-full flow

Vongvisessomjai, Natchapon; Tingsanchali, Tawatchai; Babel, Mukand S.

doi:10.1080/15730620903242824

Cited by 93 publications

(131 citation statements)

References 12 publications

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“…In this study, three datasets are used to predict sediment transport in the non-deposition condition in pipe channels (Ab Ghani 1993;Ota andNalluri 1999 andVongvisessomjai et al 2010). The data employed in this study are all associated with non-deposition in different hydraulic conditions, such as pipe diameter, channel slope, particle size and channel length.…”

Section: Methodsmentioning

confidence: 99%

“…Several studies in this field (Ab Ghani 1993;Vongvisessomjai et al 2010;Azamathulla et al 2012;Ebtehaj and Bonakdari 2013) presented the influential parameters on sediment transport in non-deposition state as follows:…”

Section: Methodsmentioning

confidence: 99%

“…Numerous experimental and analytical studies have been done to identify which parameters influence sediment transport (Mayerle et al 1991;Nalluri and Ab Ghani 1996;May 2003;Banasiak 2008;Vongvisessomjai et al 2010;Almedeij 2012;Bonakdari and Ebtehaj 2014a). These studies yielded various equations for determining the limiting velocity.…”

Section: Introductionmentioning

confidence: 99%

“…(1). It is recognized as the best semi-experimental equation for nondeposition sediment transport (Vongvisessomjai et al 2010;Ebtehaj and Bonakdari 2014a): …”

Section: Introductionmentioning

confidence: 99%

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Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Ebtehaj

Bonakdari

2017

Appl Water Sci

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Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number (Fr) parameters includes the volumetric sediment concentration (C V ), ratio of median particle diameter to hydraulic radius (d/R), ratio of median particle diameter to pipe diameter (d/D) and overall friction factor of sediment (k s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy (R 2 = 0.965) than the ANFIS model and regression-based equations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…(1). It is recognized as the best semi-experimental equation for nondeposition sediment transport (Vongvisessomjai et al 2010;Ebtehaj and Bonakdari 2014a): …”

Section: Introductionmentioning

confidence: 99%

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Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Ebtehaj

Bonakdari

2017

Appl Water Sci

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“…Minimum velocity and shear stress are among the most effective factors in reducing sedimentation, and different criteria for both shear stress and velocity were provided by Vongvisessomjai et al (2010). This method does not refer to sediment and flow characteristics and is thus not reliable, as it leads to under or overestimated critical velocity for incipient motion .…”

Section: Introductionmentioning

confidence: 99%

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

Ebtehaj

Bonakdari

Zaji

et al. 2016

Journal of Hydrology and Hydromechanics

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A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.

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

Safari

Shirzad

2019

Water Environment Research

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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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Non-deposition design criteria for sewers with part-full flow

Cited by 93 publications

References 12 publications

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

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

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