Abstract:

This article employs two machine learning techniques, viz., Least Square Support Vector Machine (LSSVM) and Multivariate Adaptive Regression Spline (MARS), for determination of Uniaxial Compressive Strength (?c) of oporto granite. LSSVM uses a quadratic cost function. MARS is a nonparametric regression technique. Free porosity (N48), dry bulk density (d) and ultrasonic velocity (v) have been used as input of the LSSVM and MARS models. The output of LSSVM and MARS is ?c. The developed LSSVM and MARS give equations for prediction of ?c. A comparative study has been carried out between the developed LSSVM, MARS, Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The results show that the developed LSSVM and MARS models are efficient tools for determination of ?c of Oporto granite.