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Computational Intelligence Model for Estimating Intensity of Blast-Induced Ground Vibration in a Mine Based on Imperialist Competitive and Extreme Gradient Boosting Algorithms

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Abstract

In this paper, we developed a novel hybrid model ICA–XGBoost for estimating blast-produced ground vibration in a mine based on extreme gradient boosting (XGBoost) and imperialist competitive algorithm (ICA). For comparison, we used another hybrid model combining particle swarm optimization and XGBoost [i.e., particle swarm optimization (PSO)–XGBoost] as well as other models, namely classical XGBoost, artificial neural network (ANN), gradient boosting machine (GBM), and support vector regression (SVR). We compared these techniques using 136 blasting events data gathered at an open-pit coal mine in Vietnam. The models’ performance evaluation criteria were the determination coefficient (R2), root-mean-square error, mean absolute error, ranking, and color intensity. Based on the results, our ICA–XGBoost model is the most robust in predicting blast-produced ground vibration. The PSO–XGBoost model provided a slightly poorer performance. The classical XGBoost model showed a lower performance than the hybrid models (i.e., ICA–XGBoost and PSO–XGBoost). The SVR and ANN models gave average performances, whereas the GBM model yielded the worst performance. The results also reveal that the maximum explosive charge capacity, the elevation between blast sites and monitoring points, and the monitoring distance are the most critical variables that should be used in predicting the intensity of blast-induced ground vibration in a mine.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant Nos. 51874232; 41807259), and Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam; Duy Tan University, Da Nang, Vietnam; and the Center for Mining, Electro-Mechanical research of HUMG.

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Authors and Affiliations

  1. School of Energy, Xi’an University of Science and Technology, Xi’an, 710054, China

    Ziwei Ding

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

    Hoang Nguyen

  3. 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

  4. 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

  5. School of Resources and Safety Engineering, Central South University, Changsha, 410083, Hunan, China

    Jian Zhou

  6. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

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

    Hossein Moayedi

Authors
  1. Ziwei Ding
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  2. Hoang Nguyen
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  3. Xuan-Nam Bui
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  4. Jian Zhou
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  5. Hossein Moayedi
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Corresponding authors

Correspondence to Hoang Nguyen or Hossein Moayedi.

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Ding, Z., Nguyen, H., Bui, XN. et al. Computational Intelligence Model for Estimating Intensity of Blast-Induced Ground Vibration in a Mine Based on Imperialist Competitive and Extreme Gradient Boosting Algorithms. Nat Resour Res 29, 751–769 (2020). https://doi.org/10.1007/s11053-019-09548-8

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  • DOI: https://doi.org/10.1007/s11053-019-09548-8

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