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

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

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Official URL: https://doi.org/10.1007/s00521-016-2807-5

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Embankment; Settlement prediction; Soft clay; Stone columns; Support vector machines
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 25 Sep 2019 04:46
Last Modified: 25 Sep 2019 04:46
URI: http://eprints.um.edu.my/id/eprint/22544

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