| Titre : | OCR prediction using support vector machine based on piezocone data (2010) |
| Auteurs : | Pijush Samui, Auteur ; T. G. Sitharam, Auteur ; Pradeep U. Kurup, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Journal of geotechnical and geoenvironmental engineering (Vol. 134 N° 6, Juin 2008) |
| Article en page(s) : | pp. 894–898 |
| Note générale : | Geotechnical and geoenvironmental engineering |
| Langues : | Anglais |
| Tags : | Predictions Vector analysis Data |
| Résumé : | 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 this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR. |
| En ligne : | http://ascelibrary.org/doi/abs/10.1061/%28ASCE%291090-0241%282008%29134%3A6%28894%29 |
