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Abstract Accurate prediction of underground pillar stability statuses (i.e., stable, unstable or failed) is of great significance for mining safety, but the limited prior data for pillar failure makes it a critical challenge. The present study proposes a novel feature fusion learning (FFL) framework to address this issue by integrating feature fusion, machine learning (ML) models, and category voting. The FFL strategy expands limited datasets through cross-reconstruction of feature vectors, converting multi-class stability prediction into a binary classification task while preserving original feature attributes. Comparative analysis of the proposed FFL strategy with two widely used data augmentation techniques (i.e., SMOTE and GAN), combined with three ML models Random Forests (RF), LightGBM and XGBoost demonstrates the superiority of FFL. Results reveal that FFL achieves higher accuracy (0.89), precision (0.88), recall (0.86), and F1-score (0.87) when paired with the XGBoost model, outperforming conventional methods. SHAP analysis underscores pillar stress, uniaxial compressive strength (UCS), and width-to-height ratio as dominant predictors, while feature fusion enriches dimensionality without compromising interpretability. The framework exhibits robustness across datasets with varying feature vectors and sample sizes, validating its adaptability to real-world scenarios with sparse data. This work provides a reliable and interpretable tool for pillar stability assessment, enhancing the safety of deep mining operations.
Cai et al. (Tue,) studied this question.