Assessment of backbreak due to blasting operation in open pit mines: a case study

One of the major problems related to blasting operations in open pit mines is the formation of cracks on the benches behind the last row of blast holes (backbreak). Prediction of backbreak plays an important role in the safety of open pit mining and the reduction of the detrimental effects resulting from blasting operations. In this study, we have tried to develop predictive models for anticipating backbreak. For this purpose, two of the most popular techniques, regression tree (RT) analysis and adaptive neuro-fuzzy inference system (ANFIS), were taken into account and a predictive model was constructed based on each. For training and testing of these models, a database including 175 blasting events in the Sungun Copper Mine (SCM), Iran, was used. These models predict backbreak based on the major blast design parameters (i.e., burden, spacing, stemming length, powder factor, and geometric stiffness ratio). It was found that both models can be used to predict the backbreak, but the comparison of two models, in terms of statistical performance indices, shows that the ANFIS model provides better results than the RT.