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 ( qt ) , vertical total stress ( σv ) , hydrostatic pore pressure ( u0 ) , pore pressure at the cone tip ( u1 ) , and the pore pressure just above the cone base ( u2 ) . Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt =primary in situ data influenced by OCR followed by σv , u0 , u2 , and u1 . Comparison between SVM and some of the traditional interpretation methods is also presented. The results of...
Published Version
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