Int J Performability Eng ›› 2014, Vol. 10 ›› Issue (2): 189-195.doi: 10.23940/ijpe.14.2.p189.mag

• Original articles • Previous Articles     Next Articles

Machine Learning Techniques Applied to Uniaxial Compressive Strength of Oporto Granite


  1. 1 National Institute of Rock Mechanics, Kolar Gold Fields-563 117, Karnataka, India
    2 School of Mechanical and Building Science, VIT University,Vellore-632014, Tamilnadu, India
    3 Centre for Disaster Mitigation and Management, VIT University,Vellore-632014, India


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.

Received on March 21, 2013, revised on April 12 and November 07, 2013
References: 21