A Comparative Study Between Least Square Support Vector Machine(Lssvm) and Multivariate Adaptive Regression Spline(Mars) Methods for the Measurement of Load Storing Capacity of Driven Piles in Cohesion Less Soil

2015 ◽  
Author(s):  
Mohit Chugh ◽  
◽  
Shraddha Satish Thumsi ◽  
Vivek Keshri
Keyword(s):  
Support Vector Machine ◽  
Comparative Study ◽  
Least Square ◽  
Multivariate Adaptive Regression Spline ◽  
Support Vector ◽  
Driven Piles ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Cohesion Less Soil

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

2012 ◽  
Vol 3 (2) ◽  
pp. 33-42 ◽  
Author(s):  
Pijush Samui ◽  
Pradeep Kurup
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Undrained Shear Strength ◽  
Least Square ◽  
Support Vector ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Undrained Shear

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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