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Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay

Pijush Samui, Pradeep Kurup

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)3(2)

ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466610842|DOI: 10.4018/jamc.2012040103

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MLA

Samui, Pijush, and Pradeep Kurup. "Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay." IJAMC vol.3, no.2 2012: pp.33-42. http://doi.org/10.4018/jamc.2012040103

APA

Samui, P. & Kurup, P. (2012). Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay. International Journal of Applied Metaheuristic Computing (IJAMC), 3(2), 33-42. http://doi.org/10.4018/jamc.2012040103

Chicago

Samui, Pijush, and Pradeep Kurup. "Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay," International Journal of Applied Metaheuristic Computing (IJAMC) 3, no.2: 33-42. http://doi.org/10.4018/jamc.2012040103

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Abstract

This study adopts Multivariate Adaptive Regression Spline (MARS) and Least Square Support Vector Machine (LSSVM) for prediction of undrained shear strength (su) of clay, based Cone Penetration Test (CPT) data. Corrected cone resistance (qt), vertical total stress (sv), hydrostatic pore pressure (u0), pore water pressure at the cone tip (u1), and pore water pressure just above the cone base (u2) are used as input parameters for building the MARS and LSSVM models. The developed MARS and LSSVM models give simple equations for prediction of su. A comparative study between MARS and LSSSM is presented. The results confirm that the developed MARS and LSSVM models are robust for prediction of su.

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Multivariate Adaptive Regression Spline and Least Square Support Vector Machine for Prediction of Undrained Shear Strength of Clay

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