Optimizing Flyrock Forecasting in Open-Pit Blasting Using Hybrid Machine Learning Models
Flyrock occurrences pose significant environmental and safety risks in open-pit blasting, threatening equipment, personnel, and potentially causing fatal accidents. This study intends to reduce the risk of flyrock by developing multiple machine learning approaches to estimate its throw range, utiliz...
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| Veröffentlicht in: | Rock mechanics and rock engineering Jg. 58; H. 11; S. 12523 - 12550 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Vienna
Springer Vienna
01.11.2025
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0723-2632, 1434-453X |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Flyrock occurrences pose significant environmental and safety risks in open-pit blasting, threatening equipment, personnel, and potentially causing fatal accidents. This study intends to reduce the risk of flyrock by developing multiple machine learning approaches to estimate its throw range, utilizing Random Forest (RF) as the primary model. Two optimization algorithms, the Light Spectrum Optimizer (LSO) and the Puma Optimizer Algorithm (POA), were employed to fine-tune the hyperparameters of the RF model. Researchers conducted field surveys of blasting events in quarries in Malaysia and the Ayazaga area of Istanbul to compile two databases. Based on these datasets, the reliability and practicality of the LSO-RF and POA-RF models were examined. For comparison, an unoptimized RF model was also created to estimate the flyrock throw distance. The performance of the models was assessed using four metrics: root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R
2
), and variance accounted for (VAF). Results show that LSO-RF and POA-RF outperform the unoptimized RF model across all metrics. For the Malaysian dataset, LSO-RF achieves training/testing R
2
of 0.9661/0.9597, MAPE of 4.445/6.711, and RMSE of 12.081/16.091; POA-RF yields training/testing R
2
of 0.9662/0.9596, MAPE of 4.443/6.717, and RMSE of 12.075/16.094. For the Istanbul dataset, LSO-RF achieves training/testing R
2
of 0.9706/0.9698, MAPE of 5.369/6.512, and RMSE of 6.128/6.537; POA-RF yields training/testing R
2
of 0.9725/0.9680, MAPE of 6.006/7.554, and RMSE of 6.087/7.335. Performance differences between hybrid models are within 0.2% across datasets, indicating consistent reliability. Results from the engineering case validation demonstrate that the developed hybrid model exhibits strong generalization performance, with R
2
of 0.8778. SHAP-based sensitivity analysis identifies key parameters: hole diameter (HD) and maximum charge per delay (CD) for Malaysia, specific charge (q) and spacing-to-burden ratio (S/B) for Istanbul, all positively correlated with FD. An intelligent software tool integrating these models supports data-driven blasting parameter optimization, enhancing safety and efficiency by minimizing flyrock risks.
Highlights
Optimized machine learning models predict flyrock throw range in open-pit blasting.
Light Spectrum Optimizer and Puma Optimizer Algorithm enhance Random Forest performance.
Field data from Malaysia and Istanbul validate model accuracy and reliability.
Intelligent software tool developed for real-time flyrock prediction and blasting optimization. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0723-2632 1434-453X |
| DOI: | 10.1007/s00603-025-04730-2 |