Modeling of Elastic Modulus of Jointed Rock Mass: Gaussian Process Regression Approach

The elastic modulus (E-j) of a jointed rock mass is an important parameter for rock mechanics. This study examines the capability of Gaussian process regression (GPR) for determination of the E-j of jointed rock masses. The GPR is a Bayesian nonparametric model. The joint frequency (J(n)), joint inclination parameter (n), joint roughness parameter (r), confining pressure (sigma(3)), and elastic modulus (E-i) of intact rock are considered as inputs of the GPR. The output of the GPR is the E-j of jointed rock masses. The developed GPR has been compared with the artificial neural network (ANN) models. Variance of the predicted E-j of jointed rock masses is obtained from the GPR. The results show that the developed GPR is a promising tool for the prediction of the E-j of jointed rock masses. (C) 2014 American Society of Civil Engineers.