Abstract
AbstractThe elastic modulus (Ej) 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 Ej of jointed rock masses. The GPR is a Bayesian nonparametric model. The joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ3), and elastic modulus (Ei) of intact rock are considered as inputs of the GPR. The output of the GPR is the Ej of jointed rock masses. The developed GPR has been compared with the artificial neural network (ANN) models. Variance of the predicted Ej 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 Ej of jointed rock masses.
