Development of a local scour prediction model clustered by soil class

Several studies have been conducted to assess local scour formulas in order to select the most appropriate one. Confronted with the limits of the previous formulas, further studies have been performed to propose new local scour formulas. Generalizing a single scour formula, for all soil classes, see...

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Autores principales:	M. Annad, A. Lefkir, M. Mammar-kouadri, I. Bettahar
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	bridge local scour new scour formulas scour estimate soil classification Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/9e679a51063846a1a2a55df8892d6856
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spelling	oai:doaj.org-article:9e679a51063846a1a2a55df8892d68562021-11-05T21:16:45ZDevelopment of a local scour prediction model clustered by soil class1751-231X10.2166/wpt.2021.065https://doaj.org/article/9e679a51063846a1a2a55df8892d68562021-10-01T00:00:00Zhttp://wpt.iwaponline.com/content/16/4/1159https://doaj.org/toc/1751-231XSeveral studies have been conducted to assess local scour formulas in order to select the most appropriate one. Confronted with the limits of the previous formulas, further studies have been performed to propose new local scour formulas. Generalizing a single scour formula, for all soil classes, seems approximate for such a complex phenomenon depending on several parameters and may eventually lead to considerable uncertainties in scour estimation. This study aims to propose several new scour formulas for different granulometric classes of the streambed by exploiting a large field database. The new scour formulas are based on multiple non-linear regression (MNLR) models. Supervised learning is used as an optimization tool to solve the hyper-parameters of each new equation by using the ‘Gradient Descent Algorithm’. The results show that the new formulas proposed in this study perform better than some other empirical formulas chosen for comparison. The results are presented as seven new formulas, as well as abacuses for the calculation of local scour by soil classes. HIGHLIGHTS Modeling of local scour with clustering the data by soil type.; Using a large database of local scour.; Statistical analysis of the scour phenomenon followed by a regression model optimized by the gradient descent algorithm.; Reduction scour parameters.; Proposal of abacuses for the scour calculation.;M. AnnadA. LefkirM. Mammar-kouadriI. BettaharIWA Publishingarticlebridge local scournew scour formulasscour estimatesoil classificationEnvironmental technology. Sanitary engineeringTD1-1066ENWater Practice and Technology, Vol 16, Iss 4, Pp 1159-1172 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bridge local scour new scour formulas scour estimate soil classification Environmental technology. Sanitary engineering TD1-1066
spellingShingle	bridge local scour new scour formulas scour estimate soil classification Environmental technology. Sanitary engineering TD1-1066 M. Annad A. Lefkir M. Mammar-kouadri I. Bettahar Development of a local scour prediction model clustered by soil class
description	Several studies have been conducted to assess local scour formulas in order to select the most appropriate one. Confronted with the limits of the previous formulas, further studies have been performed to propose new local scour formulas. Generalizing a single scour formula, for all soil classes, seems approximate for such a complex phenomenon depending on several parameters and may eventually lead to considerable uncertainties in scour estimation. This study aims to propose several new scour formulas for different granulometric classes of the streambed by exploiting a large field database. The new scour formulas are based on multiple non-linear regression (MNLR) models. Supervised learning is used as an optimization tool to solve the hyper-parameters of each new equation by using the ‘Gradient Descent Algorithm’. The results show that the new formulas proposed in this study perform better than some other empirical formulas chosen for comparison. The results are presented as seven new formulas, as well as abacuses for the calculation of local scour by soil classes. HIGHLIGHTS Modeling of local scour with clustering the data by soil type.; Using a large database of local scour.; Statistical analysis of the scour phenomenon followed by a regression model optimized by the gradient descent algorithm.; Reduction scour parameters.; Proposal of abacuses for the scour calculation.;
format	article
author	M. Annad A. Lefkir M. Mammar-kouadri I. Bettahar
author_facet	M. Annad A. Lefkir M. Mammar-kouadri I. Bettahar
author_sort	M. Annad
title	Development of a local scour prediction model clustered by soil class
title_short	Development of a local scour prediction model clustered by soil class
title_full	Development of a local scour prediction model clustered by soil class
title_fullStr	Development of a local scour prediction model clustered by soil class
title_full_unstemmed	Development of a local scour prediction model clustered by soil class
title_sort	development of a local scour prediction model clustered by soil class
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/9e679a51063846a1a2a55df8892d6856
work_keys_str_mv	AT mannad developmentofalocalscourpredictionmodelclusteredbysoilclass AT alefkir developmentofalocalscourpredictionmodelclusteredbysoilclass AT mmammarkouadri developmentofalocalscourpredictionmodelclusteredbysoilclass AT ibettahar developmentofalocalscourpredictionmodelclusteredbysoilclass
_version_	1718443996815032320

Development of a local scour prediction model clustered by soil class

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