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.

Stress relaxation plays an important role in the design of underground stopes. The aim of this paper is to assess the stope stability in connection with the stress relaxation using a classification approach. Three types of stress relaxation were clearly defined, namely partial relaxation, tangential relaxation and full relaxation. A neural network classifier was implemented to assess the stability of the stopes on the basis of case histories of stope performances. The results of the classification were compared to existing empirical methods of quantifying the stress relaxation. Overall, the present study shows higher classification accuracies, especially when the stress relaxation was considered. The results suggested that the relaxation type can be a good predictor of stability. Relaxed stope (full and tangential stress relaxation) cases are the most critical in the sense that lower accuracies were obtained and the probability of correct classification is rather erratic.

The rock quality designation is an important input for the analysis and design of rock structures as reliable spatial modeling of the rock quality designation (RQD) can assist in designing and planning mines more efficiently. The aim of this paper is to model the spatial distribution of the RQD using the multi-Gaussian kriging approach as an alternative to the non-linear geostatistical technique which has shown some limitations. To this end, 470 RQD datasets were collected from 9 boreholes pertaining to the Gazestan ore deposit in Iran. The datasets were declustered then transformed into Gaussian distribution. To ensure the model spatial continuity, variogram analysis was first performed. The elevation 150 m with a grid of 5 m × 5 m × 5 m was selected to illustrate the methodology. Surface maps showing the RQD classes (very poor, poor, fair, good, and very good) with their associated probability were established. A cross-validation method was used to check the obtained model. The validation results indicated good prediction of the local variability. In addition, the associated uncertainty was quantified on the basis of the conditional distributions and the accuracy plot agreed with the overall results. It is concluded that the proposed model could be used to produce a reliable RQD map.

When excavating tunnels and underground openings with the drill-and-blast method, minimizing excavation overbreak is vital, as it reduces the costs associated with excavation stability, mucking, and rock support. Overbreak in excavations not only causes safety concerns but also increases the cost of construction and completion time. This paper proposes a contour blasting design in which the parameters are optimized based on the rock’s structural properties. Numerical modeling was used to identify the possible damage zones of rock with a change of the seismic load due to blasting. The results were used as input for the design of the proposed contour blasting with a low-brisance explosive. Experimental blasting tests were conducted at the Akbakai mine, located in Kazakhstan, and the performance of the design was examined. The proposed contour blasting was compared with the standard blasting method. The results indicated that the cross-section of excavation with the conventional blasting method varied between 10.5 and 12.1 m², indicating an overbreak between 17 and 34%. However, with the suggested contour blasting, the overbreak was less than 13%. It was concluded that charging contour boreholes with low-brisance explosives is an effective method of reducing overbreak due to excessive explosive loads on the contour massif, which can improve the safety and profitability of mining operations.

Purpose.The research purpose is to assess the stability of mine workings driven in a stratified rock mass by studying the influence of the stratified rock bedding angle on the rock mass stress-strain state (SSS). Methods. The research uses both experimental and numerical methods. Experimental studies are carried out using rock samples with different angles of rock layer occurrence, while numerical modeling is performed using the RS2 (Geotechnical Finite Element Analysis) software based on the generalized Hoek-Brown failure criterion. The studies are carried out on models covering the border area of the mine workings driven in the mass with the angles of rock occurrence from 0 to 75°. Findings. Experimental and numerical studies have shown that when the rock layer inclination angle changes, significant changes occur in the stress concentration zones around the mine workings. An increased rock layer inclination angle is accompanied by a change in stress distribution, which is important for assessing the stability of mine workings. A particularly strong influence is observed at the angles of rock occurrence 30° and above. Originality. The research novelty is in revealing the patterns in the stress distribution in the stratified rock masses depen-ding on the rock layer inclination angle. Research results provide new data on the rock interaction mechanisms in difficult geological conditions. Practical implications. The results obtained can be used in the planning and operation of mine workings in difficult geological conditions. By taking into account the changes in stress zones caused by the rock layer inclination angle, it is possible to improve the safety and efficiency of mining operations.

The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R²) and lower Mean square error (MSE).