Application of SVM Algorithm in Predicting Vertical Pier Scour Depth

Local scour around the foundation of marine and hydraulic structures is one of the most important factors in the instability and destruction of these structures. False prediction of scour depth around bridges has caused financial losses in plasticization and endangered many peoplechr('39')...

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Main Authors: M. Majedi Asl, S. Valizadeh
Format: Article
Language:fas
Published: Isfahan University of Technology 2019-12-01
Series:علوم آب و خاک
Subjects:
Online Access:http://jstnar.iut.ac.ir/article-1-3688-en.html
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spelling doaj-e1073a14facd4192aad0da27c2b4bfa82021-04-20T08:18:21ZfasIsfahan University of Technology علوم آب و خاک2476-35942476-55542019-12-01234165181Application of SVM Algorithm in Predicting Vertical Pier Scour DepthM. Majedi Asl0S. Valizadeh1 1. Department of Civil Engineering, Hydraulic Structures, University of Maragheh, Iran. 1. Department of Civil Engineering, Hydraulic Structures, University of Maragheh, Iran. Local scour around the foundation of marine and hydraulic structures is one of the most important factors in the instability and destruction of these structures. False prediction of scour depth around bridges has caused financial losses in plasticization and endangered many peoplechr('39')s lives. Therefore, an accurate estimation of this complex phenomenon around the bridges is necessary. Also, since the formulas presented by different researchers relate to laboratory conditions, they are less true and less accurate in other situations. Recently, many researchers have tried to introduce new methods and models called soft calculations in predicting this phenomenon. In this research, 146 different laboratory data series (three different laboratory conditions) were analyzed using a backup vector machine to predict scour depth around the bridge head. These data are presented in the form of various combinations of input parameters  which, respectively, represent thickness under the slippery layer, Reynolds number, critical velocity, Shields parameter, velocity Shear, average speed, flow depth, the average diameter of the particles and diameter of the bridge. The parameters in two different scenarios (the mode with dimension and mode) were introduced into the SVM network and the results of this machine were compared with those obtained from the experimental formulas and relations presented in this study. The results showed that in the first scenario, the combination of No. 5 with input parameters () and in the second scenario, the combination No. 5 with input parameters  () for the test stage were selected as the best model. It was also concluded from the results that the scenario two (the state with dimension) in predicting the scour depth around the vertical single-pillar provided a more accurate estimate than the first scenario (barrier state). At the end, the sensitivity analysis was carried out on the parameters and the parameters D, U*, V were selected, respectively, as the most effective parametershttp://jstnar.iut.ac.ir/article-1-3688-en.htmllocal scourinput parameterssupport vector machinesoft computing
collection DOAJ
language fas
format Article
sources DOAJ
author M. Majedi Asl
S. Valizadeh
spellingShingle M. Majedi Asl
S. Valizadeh
Application of SVM Algorithm in Predicting Vertical Pier Scour Depth
علوم آب و خاک
local scour
input parameters
support vector machine
soft computing
author_facet M. Majedi Asl
S. Valizadeh
author_sort M. Majedi Asl
title Application of SVM Algorithm in Predicting Vertical Pier Scour Depth
title_short Application of SVM Algorithm in Predicting Vertical Pier Scour Depth
title_full Application of SVM Algorithm in Predicting Vertical Pier Scour Depth
title_fullStr Application of SVM Algorithm in Predicting Vertical Pier Scour Depth
title_full_unstemmed Application of SVM Algorithm in Predicting Vertical Pier Scour Depth
title_sort application of svm algorithm in predicting vertical pier scour depth
publisher Isfahan University of Technology
series علوم آب و خاک
issn 2476-3594
2476-5554
publishDate 2019-12-01
description Local scour around the foundation of marine and hydraulic structures is one of the most important factors in the instability and destruction of these structures. False prediction of scour depth around bridges has caused financial losses in plasticization and endangered many peoplechr('39')s lives. Therefore, an accurate estimation of this complex phenomenon around the bridges is necessary. Also, since the formulas presented by different researchers relate to laboratory conditions, they are less true and less accurate in other situations. Recently, many researchers have tried to introduce new methods and models called soft calculations in predicting this phenomenon. In this research, 146 different laboratory data series (three different laboratory conditions) were analyzed using a backup vector machine to predict scour depth around the bridge head. These data are presented in the form of various combinations of input parameters  which, respectively, represent thickness under the slippery layer, Reynolds number, critical velocity, Shields parameter, velocity Shear, average speed, flow depth, the average diameter of the particles and diameter of the bridge. The parameters in two different scenarios (the mode with dimension and mode) were introduced into the SVM network and the results of this machine were compared with those obtained from the experimental formulas and relations presented in this study. The results showed that in the first scenario, the combination of No. 5 with input parameters () and in the second scenario, the combination No. 5 with input parameters  () for the test stage were selected as the best model. It was also concluded from the results that the scenario two (the state with dimension) in predicting the scour depth around the vertical single-pillar provided a more accurate estimate than the first scenario (barrier state). At the end, the sensitivity analysis was carried out on the parameters and the parameters D, U*, V were selected, respectively, as the most effective parameters
topic local scour
input parameters
support vector machine
soft computing
url http://jstnar.iut.ac.ir/article-1-3688-en.html
work_keys_str_mv AT mmajediasl applicationofsvmalgorithminpredictingverticalpierscourdepth
AT svalizadeh applicationofsvmalgorithminpredictingverticalpierscourdepth
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