Backbreak in the mining industry presents a considerable challenge, impacting both safety and operational efficiency. Accurate prediction of backbreak is therefore a critical endeavour. This study rigorously evaluates four advanced machine learning (ML) techniques—Lagrangian Support Vector Machine (LSVM), Radial Basis Function Neural Network (RBFNN), Gaussian Process Regression (GPR), and Extreme Gradient Boosting (XGBoost)—to ascertain the most effective method for backbreak prediction. Utilising a comprehensive dataset of 60 blasting rounds from the Damang Goldfields Open Pit Mine and prior to the analysis, this dataset underwent a thorough preprocessing phase. The efficacy of each model is assessed using a suite of metrics, including correlation coefficient (r), coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The performance of the models is quantitatively compared, revealing XGBoost as the superior predictor in this context, characterised by an r of 0.9788, an R2 of 0.9565, an MSE of 0.1714, an RMSE of 0.4139, and an MAE of 0.2819. The findings of this study underscore the potential of XGBoost as a robust tool for backbreak prediction, offering mining companies a viable solution to enhance safety protocols and mitigate financial losses related to backbreak incidents. This research contributes significantly to the field of predictive analytics in mining, providing a comprehensive comparative analysis of various ML techniques for backbreak prediction.
| Published in | American Journal of Science, Engineering and Technology (Volume 9, Issue 1) |
| DOI | 10.11648/j.ajset.20240901.11 |
| Page(s) | 1-13 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Backbreak, Blasting, Machine Learning (ML), Cosine Amplitude Method (CAM), Simple Linear Regression (SLR)
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APA Style
Kunkyin-Saadaari, F., Agadzie, V. K., Gyebuni, R. (2024). A Comparative Study on the Application of Intelligent Models in the Estimation of Backbreak in Mine Blasting Operations. American Journal of Science, Engineering and Technology, 9(1), 1-13. https://doi.org/10.11648/j.ajset.20240901.11
ACS Style
Kunkyin-Saadaari, F.; Agadzie, V. K.; Gyebuni, R. A Comparative Study on the Application of Intelligent Models in the Estimation of Backbreak in Mine Blasting Operations. Am. J. Sci. Eng. Technol. 2024, 9(1), 1-13. doi: 10.11648/j.ajset.20240901.11
AMA Style
Kunkyin-Saadaari F, Agadzie VK, Gyebuni R. A Comparative Study on the Application of Intelligent Models in the Estimation of Backbreak in Mine Blasting Operations. Am J Sci Eng Technol. 2024;9(1):1-13. doi: 10.11648/j.ajset.20240901.11
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Download@article{10.11648/j.ajset.20240901.11,
author = {Festus Kunkyin-Saadaari and Victor Kwaku Agadzie and Richard Gyebuni},
title = {A Comparative Study on the Application of Intelligent Models in the Estimation of Backbreak in Mine Blasting Operations},
journal = {American Journal of Science, Engineering and Technology},
volume = {9},
number = {1},
pages = {1-13},
doi = {10.11648/j.ajset.20240901.11},
url = {https://doi.org/10.11648/j.ajset.20240901.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20240901.11},
abstract = {Backbreak in the mining industry presents a considerable challenge, impacting both safety and operational efficiency. Accurate prediction of backbreak is therefore a critical endeavour. This study rigorously evaluates four advanced machine learning (ML) techniques—Lagrangian Support Vector Machine (LSVM), Radial Basis Function Neural Network (RBFNN), Gaussian Process Regression (GPR), and Extreme Gradient Boosting (XGBoost)—to ascertain the most effective method for backbreak prediction. Utilising a comprehensive dataset of 60 blasting rounds from the Damang Goldfields Open Pit Mine and prior to the analysis, this dataset underwent a thorough preprocessing phase. The efficacy of each model is assessed using a suite of metrics, including correlation coefficient (r), coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The performance of the models is quantitatively compared, revealing XGBoost as the superior predictor in this context, characterised by an r of 0.9788, an R2 of 0.9565, an MSE of 0.1714, an RMSE of 0.4139, and an MAE of 0.2819. The findings of this study underscore the potential of XGBoost as a robust tool for backbreak prediction, offering mining companies a viable solution to enhance safety protocols and mitigate financial losses related to backbreak incidents. This research contributes significantly to the field of predictive analytics in mining, providing a comprehensive comparative analysis of various ML techniques for backbreak prediction.
},
year = {2024}
}
TY - JOUR T1 - A Comparative Study on the Application of Intelligent Models in the Estimation of Backbreak in Mine Blasting Operations AU - Festus Kunkyin-Saadaari AU - Victor Kwaku Agadzie AU - Richard Gyebuni Y1 - 2024/01/18 PY - 2024 N1 - https://doi.org/10.11648/j.ajset.20240901.11 DO - 10.11648/j.ajset.20240901.11 T2 - American Journal of Science, Engineering and Technology JF - American Journal of Science, Engineering and Technology JO - American Journal of Science, Engineering and Technology SP - 1 EP - 13 PB - Science Publishing Group SN - 2578-8353 UR - https://doi.org/10.11648/j.ajset.20240901.11 AB - Backbreak in the mining industry presents a considerable challenge, impacting both safety and operational efficiency. Accurate prediction of backbreak is therefore a critical endeavour. This study rigorously evaluates four advanced machine learning (ML) techniques—Lagrangian Support Vector Machine (LSVM), Radial Basis Function Neural Network (RBFNN), Gaussian Process Regression (GPR), and Extreme Gradient Boosting (XGBoost)—to ascertain the most effective method for backbreak prediction. Utilising a comprehensive dataset of 60 blasting rounds from the Damang Goldfields Open Pit Mine and prior to the analysis, this dataset underwent a thorough preprocessing phase. The efficacy of each model is assessed using a suite of metrics, including correlation coefficient (r), coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The performance of the models is quantitatively compared, revealing XGBoost as the superior predictor in this context, characterised by an r of 0.9788, an R2 of 0.9565, an MSE of 0.1714, an RMSE of 0.4139, and an MAE of 0.2819. The findings of this study underscore the potential of XGBoost as a robust tool for backbreak prediction, offering mining companies a viable solution to enhance safety protocols and mitigate financial losses related to backbreak incidents. This research contributes significantly to the field of predictive analytics in mining, providing a comprehensive comparative analysis of various ML techniques for backbreak prediction. VL - 9 IS - 1 ER -
Department of Mining Engineering, University of Mines and Technology, Tarkwa, Ghana
Department of Mining Engineering, University of Mines and Technology, Tarkwa, Ghana
Department of Mining Engineering, University of Mines and Technology, Tarkwa, Ghana
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