OCR Prediction Using Support Vector Machine Based on Piezocone Data
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 134, Issue 6
Abstract
The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance , vertical total stress , hydrostatic pore pressure , pore pressure at the cone tip , and the pore pressure just above the cone base . Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that =primary in situ data influenced by OCR followed by , , , and . Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.
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Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 134 • Issue 6 • June 2008
Pages: 894 - 898
Copyright
© 2008 ASCE.
History
Received: May 1, 2007
Accepted: Sep 26, 2007
Published online: Jun 1, 2008
Published in print: Jun 2008
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