A BSMOTE-OOA-SuperLearner Hybrid Framework for Interpretable Prediction of Pillar Stability
Bibliographic record
Abstract
Pillar stability prediction is essential for underground mining safety, yet it remains challenging due to limited data, class imbalance, and insufficient interpretability. This study proposes an integrated Borderline-SMOTE-Osprey Optimization Algorithm-Super Learner framework (BSMOTE-OOA-SL) for hard-rock pillar stability prediction. The framework combines five heterogeneous base learners (ANN, GBDT, KNN, RF, and SVM), applies Borderline-SMOTE within training folds to alleviate class imbalance, and employs the Osprey Optimization Algorithm (OOA) for systematic hyperparameter optimization. The model is evaluated using a dataset of 241 pillar cases from seven underground mines. Statistical experiments based on multiple random train–test splits show that the proposed framework consistently outperforms individual base learners in terms of Accuracy, Macro-Precision, Macro-Recall, and Macro-F1, demonstrating improved robustness and generalization. Ablation results indicate that the joint use of Borderline-SMOTE and OOA leads to quantitative performance gains of 10.21%, 12.25%, 12.61%, and 12.86% in Accuracy, Macro-Precision, Macro-Recall, and Macro-F1, respectively. Under a representative data split, the model achieves an overall accuracy of 95.92%, with strong class-wise Precision, Recall, and F1-score across all stability categories, and AUC values exceeding 0.9 for all classes (reaching 1.0 for the Failed category). SHAP-based interpretability analysis identifies stress-related indicators—particularly average pillar stress, Stress/UCS ratio, and UCS—as the dominant factors governing pillar stability. Overall, the proposed BSMOTE-OOA-SL framework provides a robust, interpretable, and statistically reliable solution for hard-rock pillar stability prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".