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Détail de l'auteur
Auteur Pijush Samui
Documents disponibles écrits par cet auteur
Affiner la rechercheOCR prediction using support vector machine based on piezocone data / Pijush Samui in Journal of geotechnical and geoenvironmental engineering, Vol. 134 N° 6 (Juin 2008)
in Journal of geotechnical and geoenvironmental engineering > Vol. 134 N° 6 (Juin 2008) . - pp. 894–898
Titre : OCR prediction using support vector machine based on piezocone data Type de document : texte imprimé Auteurs : Pijush Samui, Auteur ; T. G. Sitharam, Auteur ; Pradeep U. Kurup, Auteur Année de publication : 2010 Article en page(s) : pp. 894–898 Note générale : Geotechnical and geoenvironmental engineering Langues : Anglais (eng) Mots-clés : Predictions Vector analysis Data analysis 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%2889 [...] [article] OCR prediction using support vector machine based on piezocone data [texte imprimé] / Pijush Samui, Auteur ; T. G. Sitharam, Auteur ; Pradeep U. Kurup, Auteur . - 2010 . - pp. 894–898.
Geotechnical and geoenvironmental engineering
Langues : Anglais (eng)
in Journal of geotechnical and geoenvironmental engineering > Vol. 134 N° 6 (Juin 2008) . - pp. 894–898
Mots-clés : Predictions Vector analysis Data analysis 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%2889 [...]
in Journal of geotechnical and geoenvironmental engineering > Vol. 134 N° 6 (Juin 2008) . - pp. 894–898
Titre : OCR prediction using support vector machine based on piezocone data Type de document : texte imprimé Auteurs : Pijush Samui, Auteur ; T. G. Sitharam, Auteur ; Pradeep U. Kurup, Auteur Année de publication : 2010 Article en page(s) : pp. 894–898 Note générale : Geotechnical and geoenvironmental engineering Langues : Anglais (eng) Mots-clés : Predictions Vector analysis Data analysis 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%2889 [...] [article] OCR prediction using support vector machine based on piezocone data [texte imprimé] / Pijush Samui, Auteur ; T. G. Sitharam, Auteur ; Pradeep U. Kurup, Auteur . - 2010 . - pp. 894–898.
Geotechnical and geoenvironmental engineering
Langues : Anglais (eng)
in Journal of geotechnical and geoenvironmental engineering > Vol. 134 N° 6 (Juin 2008) . - pp. 894–898
Mots-clés : Predictions Vector analysis Data analysis 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%2889 [...]
