Analyzing Stability Conditions and Ore Dilution in Open Stope Mining

Ore dilution is a fundamental problem for the production process in underground mining operations. Especially in open stoping methods of underground mining, the continuous estimation, monitoring and treatment of instability issues is considered necessary in order to maintain the consistency of the production process. This paper aims to combine empirical nomograms of stability estimation and numerical approaches and thus link the extensive experiences of the empirical design and the quantitative data derived by numerical analyses. To facilitate this, a large number of different geomechanical conditions were modeled and analyzed in the pursuit of obtaining valid and applicable relationships between the empirical stability graphs’ approaches and the numerical simulation models. The parametric analysis was made to express the stability conditions and the dilution with specific design characteristics, using prevalent stability-graph approaches while the numerical models were tested using the RS2 software package. The obtained results include direct and easy-to-use mathematical expressions that can be applied during the initial design of the stoping process, especially for the case of sidewalls (hanging walls and foot walls). Furthermore, through the research, an initial proposal is made for a dilution-based stability graph that could be utilized for the early identification of dilution.

Linking Stability Conditions and Ore Dilution in Open Stope Mining

The estimation of the stability conditions, over-breaks, and spalling failures, which could inflict potential external dilution, is a key parameter so as to ensure the optimal design of the exploitation and its cost effectiveness The research undertaken aims at correlating established empirical approaches for the estimation of the stability condition with numerical analysis that identifies and measures the depth of failure. A number of analyses have been conducted and the results obtained yield promising results that can be transformed to direct mathematical expressions applied for the early estimation of dilution rates. Furthermore, through the research, an initial proposal is made for a dilution-based stability graph that could be utilized for the early identification of dilution.

Intraoperative Assessment of Gap Balancing in Total Knee Arthroplasty Using Navigation with Joint Stability Graphs

The purpose of this study was to assess continuous gaps in the replaced knee throughout the full range of motion (ROM) after total knee arthroplasty (TKA) using a joint stability graph, and to analyze the gap laxity in the mid-flexion range. Ninety-three TKAs were performed using imageless navigation with a joint stability graph. While positioning guides for each respective cut, the surgeon can safely preview the resection's impact for the resulting joint gaps and control the soft tissue balance at the knee flexion of 0° (extension) and 90° (flexion). The gaps between the femoral component and insert were evaluated throughout the full ROM using the joint stability graph. The mechanical axis (MA) and change of joint line height were radiographically evaluated. Posthoc power analyses using a significant α value of 0.05 were performed on the proportion of the mid-flexion instability as a primary outcome to determine whether the sample had sufficient power. The power was determined to be sufficient (100%). The flexion–extension gap differences in each medial and lateral compartment and the mediolateral gap differences in flexion and extension were all ≤3 mm. None of the knees had mid-flexion instability, which is defined by a peak mid-flexion gap that is 3 mm greater than the smaller value of flexion or extension gap. The average MA was well corrected from varus 11.4° to varus 1.0° postoperatively. The proportion of postoperative well-aligned knees (MA ≤ 3°) was 87.1%. The joint line height was well preserved (14.7 vs. 14.8 mm, p = 0.751). The joint stability graph in TKA using the navigation can effectively evaluate the continuous gap throughout the ROM, including the mid-flexion range. Mid-flexion instability was uncommon in primary TKAs with appropriate alignment and proper preservation of the joint line. The Level of evidence for the study is IV.

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.

Development of a Stope Stability Prediction Model Using Ensemble Learning Techniques - A Case Study

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

Stability analysis of underground water-sealed oil storage caverns in China：A case study

The stability of underground water-sealed oil storage caverns is of great importance for safe excavation and operation. To analyze the scope of the failure zone and underground cavern stability accurately, a new method was developed that integrates the rock tunneling quality index Q-system and stability graph method with 3D laser scanning and numerical simulation. The point cloud data were obtained by 3D laser scanning, and the cavern model was built by using DIMINE software, which directly shows the 3D shape of the cavern. The rock mass physical and mechanical parameters and the corresponding stability coefficients were obtained based on Q-system and stability graph method. The plastic zone distribution and deformation characteristics of surrounding rock were analyzed through numerical simulation. Then, the corresponding relationship between caving zone and plastic zone was determined by comparing the numerical simulation results with the 3D laser scanning contour. The new method provides a reliable way to analyze the stability of the underground water-sealed oil storage cavern and also will helpful to design or optimize the subsequent support.