Prediction of Backbreak in Open Pit Blasting By Adaptive Neuro-Fuzzy Inference System (ANFIS) Model

Abstract

Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input–output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations that can be caused instability in mine walls, falling down of machinery, improper fragmentation and reduced efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.