LAUSR

This paper aims to study the mechanical behavior of rocks using the cohesive crack model (CCM) and grain-based model (GBM) within the distinct element method (DEM) simulations. In the GBM-DEM, the Voronoi tessellation scheme is used and the intact material is simulated as assemblies of a number of particles bonded together at their contact areas. Implemented CCM defines the nonlinear behavior of the grain interfaces under various modes of loading. Numerical simulation of a tension–compression and direct shear tests is conducted to verify the correct implementation of CCM. Moreover, in the application of such a numerical framework, the uniaxial and biaxial compression tests as well as the Brazilian disc test are simulated in the universal distinct element code using a calibration process and then the results are compared with the relevant responses of the rock sample. Finally, the capability of soft computing method, i.e., artificial neural network (ANN) for predicting macromechanical parameters of rock, i.e., UCS based on input cohesive contact shear strength parameters, i.e., cohesion and friction angle, is explored. Results indicate that the ANN model can be quite accurate predictor of uniaxial compressive strength (UCS) and therefore used as a potential tool to aid in estimating target values.