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Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment

In order to have a proper design and analysis for the column of stone in the soft clay soil, it is essential to develop an accurate prediction model for the settlement behavior of the stone column. In the current research, to predict the behavior in the settlement of stone column a support vector ma...

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Main Authors: Aljanabi, Qasim A., Chik, Zamri, Allawi, Mohammed Falah, El-Shafie, Amr H., Ahmed, Ali N., El-Shafie, Ahmed
Format: Article
Published: Springer Verlag (Germany) 2018
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/22544/
https://doi.org/10.1007/s00521-016-2807-5
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spelling my.um.eprints.225442019-09-25T04:46:14Z http://eprints.um.edu.my/22544/ Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment Aljanabi, Qasim A. Chik, Zamri Allawi, Mohammed Falah El-Shafie, Amr H. Ahmed, Ali N. El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) In order to have a proper design and analysis for the column of stone in the soft clay soil, it is essential to develop an accurate prediction model for the settlement behavior of the stone column. In the current research, to predict the behavior in the settlement of stone column a support vector machine (SVM) method is developed and examined. In addition, the proposed model has been compared with the existing reference settlement prediction model that using the monitored field data. As SVM mathematical procedure has resilient and robust generalization aptitude and ensures searching for global minima for particular training data as well. Therefore, the potential that support vector regression might perform efficiently to predict the ground soft clay settlement is relatively valuable. As a result, in this study, comparison of two different developed types of SVM method is carried out. Generally, significant reduction in the relative error (RE%) and root mean square error has been achieved. Utilizing nu-SVM-type model through tenfold cross-validation procedure could achieve outstanding performance accuracy level with RE% less than 2% and CR = 0.9987. The study demonstrates high potential for applying SVM in detecting the settlement behavior of SC prediction and ascertains that SVM could be effectively used for settlement stone columns analysis. Springer Verlag (Germany) 2018 Article PeerReviewed Aljanabi, Qasim A. and Chik, Zamri and Allawi, Mohammed Falah and El-Shafie, Amr H. and Ahmed, Ali N. and El-Shafie, Ahmed (2018) Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment. Neural Computing and Applications, 30 (8). pp. 2459-2469. ISSN 0941-0643 https://doi.org/10.1007/s00521-016-2807-5 doi:10.1007/s00521-016-2807-5
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Aljanabi, Qasim A.
Chik, Zamri
Allawi, Mohammed Falah
El-Shafie, Amr H.
Ahmed, Ali N.
El-Shafie, Ahmed
Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment
description In order to have a proper design and analysis for the column of stone in the soft clay soil, it is essential to develop an accurate prediction model for the settlement behavior of the stone column. In the current research, to predict the behavior in the settlement of stone column a support vector machine (SVM) method is developed and examined. In addition, the proposed model has been compared with the existing reference settlement prediction model that using the monitored field data. As SVM mathematical procedure has resilient and robust generalization aptitude and ensures searching for global minima for particular training data as well. Therefore, the potential that support vector regression might perform efficiently to predict the ground soft clay settlement is relatively valuable. As a result, in this study, comparison of two different developed types of SVM method is carried out. Generally, significant reduction in the relative error (RE%) and root mean square error has been achieved. Utilizing nu-SVM-type model through tenfold cross-validation procedure could achieve outstanding performance accuracy level with RE% less than 2% and CR = 0.9987. The study demonstrates high potential for applying SVM in detecting the settlement behavior of SC prediction and ascertains that SVM could be effectively used for settlement stone columns analysis.
format Article
author Aljanabi, Qasim A.
Chik, Zamri
Allawi, Mohammed Falah
El-Shafie, Amr H.
Ahmed, Ali N.
El-Shafie, Ahmed
author_facet Aljanabi, Qasim A.
Chik, Zamri
Allawi, Mohammed Falah
El-Shafie, Amr H.
Ahmed, Ali N.
El-Shafie, Ahmed
author_sort Aljanabi, Qasim A.
title Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment
title_short Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment
title_full Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment
title_fullStr Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment
title_full_unstemmed Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment
title_sort support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment
publisher Springer Verlag (Germany)
publishDate 2018
url http://eprints.um.edu.my/22544/
https://doi.org/10.1007/s00521-016-2807-5
_version_ 1646210271565316096
score 13.211869

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