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Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques

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Abstract

Air over-pressure (AOp) is one of the products of blasting operations for rock fragmentation in open-pit mines. It can cause structural vibration, smash glass doors, adversely affect the surrounding environment, and even be fatal to humans. To assess its dangerous effects, seven artificial intelligence (AI) methods for predicting specific blast-induced AOp have been applied and compared in this study. The seven methods include random forest, support vector regression, Gaussian process, Bayesian additive regression trees, boosted regression trees, k-nearest neighbors, and artificial neural network (ANN). An empirical technique was also used to compare with AI models. The degree of complexity and the performance of the models were compared with each other to find the optimal model for predicting blast-induced AOp. The Deo Nai open-pit coal mine (Vietnam) was selected as a case study where 113 blasting events have been recorded. Indicators used for evaluating model performances include the root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE). The results indicate that AI techniques provide better performance than the empirical method. Although the relevance of the empirical approach was acceptable (R2 = 0.930) in this study, its error (RMSE = 7.514) is highly significant to guarantee the safety of the surrounding environment. In contrast, the AI models offer much higher accuracies. Of the seven AI models, ANN was the most dominant model based on RMSE, R2, and MAE. This study demonstrated that AI techniques are excellent for predicting blast-induced AOp in open-pit mines. These techniques are useful for blasters and managers in controlling undesirable effects of blasting operations on the surrounding environment.

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Acknowledgments

This research was supported by Hanoi University of Mining and Geology (HUMG) and Ministry of Education and Training of Vietnam (MOET). We also thank the Center for Mining, Electro-Mechanical Research of HUMG.

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Authors and Affiliations

  1. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

    Xuan-Nam Bui, Hoang Nguyen & Ngoc-Hoan Do

  2. Center for Mining, Electro-Mechanical research, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang ward, Bac Tu Liem District, Hanoi, Vietnam

    Xuan-Nam Bui & Hoang Nguyen

  3. Faculty of Oil and Gas, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang ward, Bac Tu Liem District, Hanoi, Vietnam

    Hai-An Le

  4. Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang ward, Bac Tu Liem District, Hanoi, Vietnam

    Hoang-Bac Bui

  5. Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang ward, Bac Tu Liem District, Hanoi, Vietnam

    Hoang-Bac Bui

  6. Faculty of Mining, Saint-Petersburg Mining University, Saint-Petersburg, Russia

    Ngoc-Hoan Do

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Bui, XN., Nguyen, H., Le, HA. et al. Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques. Nat Resour Res 29, 571–591 (2020). https://doi.org/10.1007/s11053-019-09461-0

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  • DOI: https://doi.org/10.1007/s11053-019-09461-0

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