Abstract
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.
Highlights
The stability of the stopes influence the productivity of the mine as instabilities in the stope walls including blasting overbreak, sloughing, caving or failure of the hangingwalls may lead to delays in production, a high cost of maintenance, high dilution, destruction to machinery, and compromise the safety of the personnel
The stope stability dataset (225 cases) that have been compiled for the research was randomly split into three datasets; the training, validation, and testing datasets consisting of 70%, 15%, and 15% of the data, respectively, as commonly suggested (Demuth and Beale 2002; Rafiai and Moosavi 2012)
The overall confusion value was 9.3%. These values are relatively low in comparison with some existing results. This means if 100 new cases of stope surface conditions were to be presented to the feed-forward neural network (FFNN)-classifer, 10 cases would likely be misclassified
Summary
The stability of the stopes influence the productivity of the mine as instabilities in the stope walls including blasting overbreak, sloughing, caving or failure of the hangingwalls may lead to delays in production, a high cost of maintenance, high dilution, destruction to machinery, and compromise the safety of the personnel. One of the charateristics of the open stope mining methods is the high productivity where large stopes are developed with a high level of mechanization. While dimensioning the open stopes, it is necessary to account for influencing factors such as induced stress, rock mass mechanical properties, stope geometries, operational constraints, and geological features. It may not be always practical to consider each of these factors when designing the stopes This has obviously contributed to the development of many design tools
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