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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Vydáno v:	Rock mechanics and rock engineering Ročník 58; číslo 11; s. 12523 - 12550
Hlavní autoři:	Zhang, Yulin, Qiu, Yingui, Du, Kun, Nguyen, Hoang, Armaghani, Danial Jahed, Zhou, Jian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vienna Springer Vienna 01.11.2025 Springer Nature B.V
Témata:	Algorithms Artificial intelligence Blasting Civil Engineering Datasets Dimensional analysis Earth and Environmental Science Earth Sciences Fuzzy logic Geophysics/Geodesy Learning algorithms Machine learning Mines Monte Carlo simulation Neural networks Optimization Original Paper Parameter identification Parameter sensitivity Quarries Real time Reliability Risk reduction Root-mean-square errors Sensitivity analysis Software reliability Statistical analysis Training Open-pit blasting optimization Puma optimizer algorithm Intelligent feedback system Flyrock distance prediction Light spectrum optimizer Random forest Interpretability analysis
ISSN:	0723-2632, 1434-453X
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Abstract	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.
AbstractList	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 (R2), 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 R2 of 0.9661/0.9597, MAPE of 4.445/6.711, and RMSE of 12.081/16.091; POA-RF yields training/testing R2 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 R2 of 0.9706/0.9698, MAPE of 5.369/6.512, and RMSE of 6.128/6.537; POA-RF yields training/testing R2 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 R2 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.HighlightsOptimized 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. 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.
Author	Zhou, Jian Armaghani, Danial Jahed Nguyen, Hoang Du, Kun Qiu, Yingui Zhang, Yulin
Author_xml	– sequence: 1 givenname: Yulin surname: Zhang fullname: Zhang, Yulin organization: School of Resources and Safety Engineering, Central South University – sequence: 2 givenname: Yingui surname: Qiu fullname: Qiu, Yingui organization: School of Resources and Safety Engineering, Central South University – sequence: 3 givenname: Kun surname: Du fullname: Du, Kun organization: School of Resources and Safety Engineering, Central South University – sequence: 4 givenname: Hoang surname: Nguyen fullname: Nguyen, Hoang organization: Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology – sequence: 5 givenname: Danial Jahed surname: Armaghani fullname: Armaghani, Danial Jahed organization: School of Civil and Environmental Engineering, University of Technology Sydney – sequence: 6 givenname: Jian surname: Zhou fullname: Zhou, Jian email: j.zhou@csu.edu.cn organization: School of Resources and Safety Engineering, Central South University
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Keywords	Open-pit blasting optimization Puma optimizer algorithm Intelligent feedback system Flyrock distance prediction Light spectrum optimizer Random forest Interpretability analysis
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Snippet	Flyrock occurrences pose significant environmental and safety risks in open-pit blasting, threatening equipment, personnel, and potentially causing fatal...
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SubjectTerms	Algorithms Artificial intelligence Blasting Civil Engineering Datasets Dimensional analysis Earth and Environmental Science Earth Sciences Fuzzy logic Geophysics/Geodesy Learning algorithms Machine learning Mines Monte Carlo simulation Neural networks Optimization Original Paper Parameter identification Parameter sensitivity Quarries Real time Reliability Risk reduction Root-mean-square errors Sensitivity analysis Software reliability Statistical analysis Training
Title	Optimizing Flyrock Forecasting in Open-Pit Blasting Using Hybrid Machine Learning Models
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