Аңдатпа
Minimizing dilution is essential in open stope mine design as excessive unplanned dilution can compromise the operation's profitability. One of the main challenges associated with the empirical dilution graph method used to design open stopes is how to determine the boundary of the dilution zones objectively. Hence, this paper explores the implementation of machine learning classifiers to bridge this gap in the conventional dilution graph method. Stope performance data consisting of the stope dilution (unplanned dilution), the modified stability number, and the hydraulic radius were compiled from a mine located in Kazakhstan. First, the conventional dilution graph methods were used to assess the dilution. Next, a Feed-Forward Neural Network (FFNN) classifier was implemented to predict each level of dilution. Overall, the FFNN results indicated that 97% of the stope surfaces were correctly classified, indicating an excellent classification performance, while the conventional dilution graph method did not show a good performance. In addition, the outputs of the FFNN were used to plot new dilution graphs with a probabilistic interpretation illustrating its practicability. It was concluded that the FFNN-based classifier could be a useful tool for open stope design in underground mines.