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2 Journal of Sustainable Mining

2 2022

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Unplanned dilution prediction in open stope mining: developing new design charts using Artificial Neural Network classifier

Korigov Sultan, Adoko Amoussou Coffi, Sengani Fhatuwani

Journal of Sustainable Mining

2022

Vol. 21, iss. 2

157--168

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.

Predicting the stability of open stopes using Machine Learning

Szmigiel Alicja, Apel Derek B.

Journal of Sustainable Mining

2022

Vol. 21, iss. 3

241--248

The Mathews stability graph method was presented for the first time in 1980. This method was developed to assess the stability of open stopes in different underground conditions, and it has an impact on evaluating the safety of underground excavations. With the development of technology and growing experience in applying computer sciences in various research disciplines, mining engineering could significantly benefit by using Machine Learning. Applying those ML algorithms to predict the stability of open stopes in underground excavations is a new approach that could replace the original graph method and should be investigated. In this research, a Potvin database that consisted of 176 historical case studies was passed to the two most popular Machine Learning algorithms: Logistic Regression and Random Forest, to compare their predicting capabilities. The results obtained showed that those algorithms can indicate the stability of underground openings, especially Random Forest, which, in examined data, performed slightly better than Logistic Regression.