Drilling and blasting operations are one of the most effective techniques for rock removal in mines. However, these operations are associated with some environmental issues such as the problem of toe. This issue has a deep impact on blasting cost where there is a need to conduct secondary blasting due to this problem. Therefore, it is beneficial to predict and subsequently optimize this issue. This study attempts to predict toe resulting from blasting by developing artificial neural network (ANN) and decision tree (DT) techniques. To do this, 100 blasting were considered and collected in the Hawzak limestone mine, Iran. Then, the most effective parameters were considered as model inputs and many models of ANN and DT were developed to predict toe induced by blasting. For comparison purposes, multiple regression (REG) was also applied. Three performance indices were used to evaluate the developed models. Coefficients of determination (R ) of 0.76, 0.83 and 0.91 were obtained for testing datasets using the REG, DT and ANN models, respectively, which indicate that ANN can provide higher performance capacity for prediction of toe induced by blasting. In the optimization phase, imperialism competitive algorithm (ICA), which is a new optimization technique in this field, was applied to determine optimal blasting pattern parameters in order to minimize toe induced by blasting. By developing ICA technique, the amount of toe was reduced from 431 to 288 m 2 (49.35%), which shows the excellent ability of ICA in optimizing the minimization of toe that arises from blasting operations.
کلید واژگان :Blasting, Toe problem, Artificial neural network, Imperialism competitive algorithm, Decision tree.
ارزش ریالی : 600000 ریال
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