Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment
Clay; Embankments; Forecasting; Highway engineering; Mean square error; Settlement of structures; Vectors; Mathematical procedures; Root mean square errors; Settlement behaviors; Settlement prediction; Settlement prediction models; Soft clays; Stone column; Support vector regression (SVR); Support v...
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2023
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my.uniten.dspace-236882023-05-29T14:51:02Z Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment Aljanabi Q.A. Chik Z. Allawi M.F. El-Shafie A.H. Ahmed A.N. El-Shafie A. 55786638200 55522804700 57057678400 57207789882 57214837520 16068189400 Clay; Embankments; Forecasting; Highway engineering; Mean square error; Settlement of structures; Vectors; Mathematical procedures; Root mean square errors; Settlement behaviors; Settlement prediction; Settlement prediction models; Soft clays; Stone column; Support vector regression (SVR); Support vector machines 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. � 2017, The Natural Computing Applications Forum. Final 2023-05-29T06:51:02Z 2023-05-29T06:51:02Z 2018 Article 10.1007/s00521-016-2807-5 2-s2.0-85008620710 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008620710&doi=10.1007%2fs00521-016-2807-5&partnerID=40&md5=9d1d57a50ab2daedac3c8d7248e26bd1 https://irepository.uniten.edu.my/handle/123456789/23688 30 8 2459 2469 Springer London Scopus |
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| description |
Clay; Embankments; Forecasting; Highway engineering; Mean square error; Settlement of structures; Vectors; Mathematical procedures; Root mean square errors; Settlement behaviors; Settlement prediction; Settlement prediction models; Soft clays; Stone column; Support vector regression (SVR); Support vector machines |
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55786638200 |
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55786638200 Aljanabi Q.A. Chik Z. Allawi M.F. El-Shafie A.H. Ahmed A.N. El-Shafie A. |
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Article |
| author |
Aljanabi Q.A. Chik Z. Allawi M.F. El-Shafie A.H. Ahmed A.N. El-Shafie A. |
| spellingShingle |
Aljanabi Q.A. Chik Z. Allawi M.F. El-Shafie A.H. Ahmed A.N. El-Shafie A. Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment |
| author_sort |
Aljanabi Q.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 London |
| publishDate |
2023 |
| _version_ |
1806428075197464576 |
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13.214268 |