Utilization of Statistical Learning Algorithms for Prediction of Elastic Modulus of Jointed Rock Mass

Abstract

This study uses two statistical learning algorithms for the prediction of elastic modulus (Ej) of jointed rock
mass. The first algorithm uses support vector machine (SVM) that is firmly based on the theory of statistical
learning and uses regression technique by introducing -insensitive loss functionhas been adopted. The
second algorithm uses relevance vector machine (RVM). It is based on a Bayesian formulation of a linear
model with an appropriate prior that results in a sparse representation. The RVM model gives variance of
predicted data. The inputs of models are joint frequency (Jn), joint inclination parameter (n), joint roughness
parameter (r), confining pressure (3) and elastic modulus (Ei) of intact rock. Equations have been developed
for the determination of Ej of jointed rock mass based on the SVM and RVM models. The results of SVM
and RVM models are compared with a widely used artificial neural network (ANN) model. This study
shows that the developed SVM and RVM models can be used for the prediction of Ej of jointed rock mass.
Keywords: elastic modulus, jointed rock, support vector machine, relevance vector machine, artificial neural
network