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

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

Pijush Samui (Vellore Institute of Technology University, India) and Pradeep Kurup (University of Massachusetts Lowell, USA)
Copyright: © 2012 |Pages: 10
DOI: 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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Details Of Mars

MARS is a flexible modeling method for high-dimensional data (Friedman, 1991). A brief overview of the MARS model will be given here. MARS uses the following equation for prediction of output(y).

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