Predicting the shear strength of saw-tooth jointed rocks using genetic programming

The geometrical and mechanical properties of joints play an important role in the determination of shear strength of jointed rocks. In this research, we propose a nonlinear model to estimate the shear strength of jointed rocks with unfilled saw-tooth triangular asperities using the basic friction angle φb, the tensile strength of intact rock σt, and the joint inclination angle i under various normal stress σn conditions. In order to propose the new shear strength model, firstly, a rock database of saw-tooth jointed rocks containing experimental and numerical data is established. The experimental data are collected from publications. The numerical data are generated by the particle flow code (PFC) model. Then, the genetic programming (GP) is used to analyze the database to propose the GP shear strength model. The prediction performance of the proposed GP model is tested and compared with that of the Patton model and the Ladanyi and Archambault (LA) model. Comparison results show that the proposed GP model has the best prediction performance compared with the Patton model and the LA model. The proposed GP model can capture the nonlinear shear strength behavior of saw-tooth jointed rocks and its input parameters can be easily estimated.