Exploring machine learning techniques for open stope stability prediction: A comparative study and feature importance analysis
Bibliographic record
Abstract
The stability of underground excavations is essential for ensuring the safety of mining operations. Classical stability assessment methods, established in empirical formulas and rock mass classification systems, have long been employed for evaluating stope stability in underground mining. Stability graphs, a popular empirical approach, utilize factors like rock stress, joint orientation, and surface orientation to calculate stability numbers critical for stope design. However, modern advancements in machine learning present new opportunities for enhancing predictive capabilities and understanding complex relationships influencing stope stability. Building upon research demonstrating the feasibility of using machine learning for stability prediction, our study investigates and compares several machine learning algorithms. By analyzing a dataset comprising stope dimensions and geomechanical properties, we explore the potential of machine learning models such as Random Forest, Support Vector Machine, AdaBoost, XGBoost, LightGBM, and Artificial Neural Network in predicting stope stability. Evaluation metrics including accuracy, precision, recall, and F1 score are employed to assess model performance, with the Artificial Neural Network emerging as the most effective. Furthermore, SHapley Additive exPlanations (SHAP) analysis enhances interpretability by explaining the contribution of individual features to model predictions. • Comparing several machine learning models revealed that the Artificial Neural Network (ANN) generated the best predictions for stope stability assessment, with an accuracy score of 91% • Some models demonstrated good overall accuracy in their predictions; however, other classification metrics, such as precision and recall, revealed their limitations. • The SHapley Additive exPlanations (SHAP) analysis identified key factors influencing stability predictions, such as rock mass quality (Q’ value) and shape parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".