Assessing the stability of stopes is essential in open stope mine design as unstable hangingwalls and footwalls lead to sloughing, unplanned stope dilution, and safety concerns compromising the profitability of the mine. Over the past few decades, numerous empirical tools have been developed to dimension open stope in connection with its stability, using the stability graph method. However, one of the principal limitations of the stability graph method is to objectively determine the boundary of the stability zones, and gain a clear probabilistic interpretation of the graph. To overcome this issue, this paper aims to explore the feasibility of artificial neural network (ANN) based classifiers for the design of open stopes. A stope stability database was compiled and included the stope dimensions, rock mass properties, and the stope stability conditions. The main parameters included the modified stability number (N’), and the stope stability conditions (stable, unstable, and failed), and hydraulic radius (HR). A feed-forward neural network (FFNN) classifier containing two hidden layers (110 neurons each) was employed to identify the stope stability conditions. Overall, the outcome of the analysis showed good agreement with the field data; most stope surfaces were correctly predicted with an average accuracy of 91%. This shows an improvement over using the existing stability graph method. In addition, for a better interpretation of the results, the associated probability of occurrence of stable, unstable, or caved stope was determined and shown in iso-probability contour charts which were compared with the stability graph. The proposed FFNN-based classifier outperformed the conventional stability graph method in terms of accuracy and better prabablistic interpretation. It is suggested that the classifier could be a reliable tool that can complement the conventional stability graph for the design of open stopes.
The consequences of collapsed stopes can be dire in the mining industry. This can lead to the revocation of a mining license in most jurisdictions, especially when the harm costs lives. Therefore, as a mine planning and technical services engineer, it is imperative to estimate the stability status of stopes. This study has attempted to produce a stope stability prediction model adopted from stability graph using ensemble learning techniques. This study was conducted using 472 case histories from 120 stopes of AngloGold Ashanti Ghana, Obuasi Mine. Random Forest, Gradient Boosting, Bootstrap Aggregating and Adaptive Boosting classification algorithms were used to produce the models. A comparative analysis was done using six classification performance metrics namely Accuracy, Precision, Sensitivity, F1-score, Specificity and Mathews Correlation Coefficient (MCC) to determine which ensemble learning technique performed best in predicting the stability of a stope. The Bootstrap Aggregating model obtained the highest MCC score of 96.84% while the Adaptive Boosting model obtained the lowest score. The Specificity scores in decreasing order of performance were 98.95%, 97.89%, 96.32% and 95.26% for Bootstrap Aggregating, Gradient Boosting, Random Forest and Adaptive Boosting respectively. The results showed equal Accuracy, Precision, F1-score and Sensitivity score of 97.89% for the Bootstrap Aggregating model while the same observation was made for Adaptive Boosting, Gradient Boosting and Random Forest with 90.53%, 92.63% and 95.79% scores respectively. At a 95% confidence interval using Wilson Score Interval, the results showed that the Bootstrap Aggregating model produced the minimal error and hence was selected as the alternative stope design tool for predicting the stability status of stopes. Keywords: Stope Stability, Ensemble Learning Techniques, Stability Graph, Machine Learning
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