Industry Survey on the Current State of Stope Design Methods in the Underground Mining Sector

Stope design is a core discipline within mining engineering. This study analyzes the current state-of-the-art of stope design through a survey addressed to mining industry professionals. In stope design research the dominance of empirical methods has slowly shifted towards numerical methods. Recent advancements have mostly focused on the development of stope optimization algorithms. The survey consisted of 19 questions and was distributed to stope design experts via email, LinkedIn messages, and the Mining Industry Professionals network forum. In total, 36 responses of satisfying quality from 20 countries were received and analyzed. No dominance of a single stope design method was recognized. Empirical methods and personal expertise are still used widely. However, a readiness for change in stope design practice was indicated in 87% of responses. The current needs of the stoping-based underground mining sector are to increase the amount of geotechnical data, automate stope design and implement related software, and integrate these into general mine planning. According to 70% of the participants, acquired geotechnical data should be available within three days to be employed in design practice. The industry is ready to implement more efficient stope design methods if they offer results proven in case studies.

The Consolidated Mathews Stability Graph for Open Stope Design

The stability graph method of stope design is one of the most widely used methods of stability assessments of stopes in underground polymetallic mines. The primary objective of this work is to introduce a new stability chart, which includes all relevant case histories, and to exclude parameters with uncertainties in the determination of stability number. The modified stability number was used to achieve this goal, and the Extended Mathews database was recalculated and compared with the new stability graph. In this study, a new refined Consolidated stability graph was developed by excluding the entry mining methods data from the Extended graph data, and only the non-entry methods data was used. The applicability of the proposed Consolidated stability chart was demonstrated by an open stope example. The stability for each stope surface was evaluated by a probabilistic approach employing a logistic regression model and the developed Consolidated stability chart. Comparing the stability analysis results with that of other published works of the same example shows that the determined Consolidated chart, in which the entry-method data is excluded, produces a more conservative and safer design. In conclusion, the size and quality of the dataset dictate the reliability of this approach.

A Feasibility Study on The Implementation of Neural Network Classifiers for Open Stope Design

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.