Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network

Abstract

Underground mining becomes more efficient due to the technological advancements of drilling & blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA & NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters respectively. The performance of LMRA, NMRA and ANN models were evaluated by comparing coefficient correlations (R2) and their values are 0.694, 0.704 and 0.945 respectively which means that the relatively high level of accuracy of the ANN in comparison of LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements.