Prediction of rock brittleness using nondestructive methods for hard rock tunneling

AbstractThe material and elastic properties of rocks are utilized for predicting and evaluating hard rock brittleness using artificial neural networks (ANN). Herein hard rock brittleness is defined using Yagiz' method. A predictive model is developed using a comprehensive database compiled from 30 years' worth of rock tests at the Earth Mechanics Institute (EMI), Colorado School of Mines. The model is sensitive to density, elastic properties, and P- and S-wave velocities. The results show that the model is a better predictor of rock brittleness than conventional destructive strength-test based models and multiple regression techniques. While the findings have direct implications on intact rock, the methodology can be extrapolated to rock mass problems in both tunneling and underground mining where rock brittleness is an important control