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Prediction of Rock Size Distribution in Mine Bench Blasting Using a Novel Ant Colony Optimization-Based Boosted Regression Tree Technique

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Abstract

In this paper, we used artificial intelligence (AI) techniques to investigate the relation between the rock size distribution (RSD) and blasting parameters for rock fragmentation in quarries. Moreover, the ant colony optimization (ACO)–boosted regression tree (BRT) model, which is a novel AI technique for predicting RSD using the blasting parameters, is proposed based on the ACO and BRT algorithms. For predicting RSD, three well-developed models, namely the particle swarm optimization–adaptive neuro-fuzzy inference system (PSO–ANFIS), firefly algorithm (FFA)–ANFIS, and FFA–artificial neural network, were applied to the same dataset. Additionally, four benchmark AI techniques, i.e., support vector machine, k-nearest neighbors, principal component regression, and Gaussian process, and a conventional approach, i.e., the Kuz–Ram model, were employed for considering and predicting RSD. Using an image processing technique, the Split-Desktop software package was used to analyze the RSD of 136 blasting events at a quarry in Vietnam. Results were used as inputs, such as powder factor, explosive charge per delay, bench height, stemming length, and burden, and outputs, i.e., RSD, in this study. The novel scoring and color-intensity methods were used for visualizing several statistical criteria, including the correlation coefficient, mean absolute error, and root-mean-square error, to evaluate the model performance. Results indicate that the proposed ACO–BRT hybrid model yields higher RSD predictive accuracy than that obtained using any other model. The proposed model seems to be promising for optimizing the blasting parameters to increase the production efficiency while reducing the production costs.

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Acknowledgments

This research is partially supported by the Training Project for Young Scholar of Institutions of High Education of Henan Province (2018GGJS122), Natural Science Foundation of Henan Province (182300410160), Henan Science and Technology Research Planning Project (182102310804), and Anyang Science and Technology Research Planning Project (AK[2018]66). The authors also would like to thank Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam, and the Center for Mining, Electro-Mechanical Research of HUMG; Duy Tan University, Da Nang, Vietnam, and Ton Duc Thang University, Ho Chi Minh City, Vietnam, for supporting this research.

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Authors and Affiliations

  1. School of Civil Engineering and Architecture, Anyang Normal University, Anyang, 450000, Henan, China

    Shike Zhang

  2. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Xuan-Nam Bui

  3. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Xuan-Nam Bui

  4. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Nguyen-Thoi Trung

  5. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Nguyen-Thoi Trung

  6. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Hoang Nguyen

  7. Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Hoang-Bac Bui

  8. Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Hoang-Bac Bui

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  1. Shike Zhang
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Zhang, S., Bui, XN., Trung, NT. et al. Prediction of Rock Size Distribution in Mine Bench Blasting Using a Novel Ant Colony Optimization-Based Boosted Regression Tree Technique. Nat Resour Res 29, 867–886 (2020). https://doi.org/10.1007/s11053-019-09603-4

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