Evaluation of effect of blasting pattern parameters on back break using neural networks

Back break is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. To solve this problem, parameters such as the physico-mechanical properties of rock mass, explosives specifications and geometrical particulars of blast design should be considered to obtain optimum design. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for blasting pattern design. In this paper, the artificial neural network (ANN) technique was used to determine the near-optimum blasting pattern so that back break is reduced. The Gol-E-Gohar iron mine in Iran was considered as a case study. A four-layer ANN was found to be optimum with architecture of seven neurons in input layer, 15 and 25 neurons in first and second hidden layer, respectively, and one neuron in output layer. Applying the results obtained from this study, back break was reduced from 20 to 4 m.