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Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

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Abstract

Predicting and reducing blast-induced ground vibrations is a common concern among engineers and mining enterprises. Dealing with these vibrations is a challenging issue as they may result in the instability of the surrounding structures, highways, water pipes, railways, and residential areas. In this study, the effects of blasting in a quarry mine in Vietnam were examined. A total of 25 blasting events were investigated with the help of an unmanned aerial vehicle, micromate instruments, and blast patterns, and 83 observations were recorded. Subsequently, the fuzzy C-means clustering (FCM) algorithm was applied to classify the 83 observations based on the blast parameters. Finally, based on the classification of the blasts, quantile regression neural network (QRNN) models were developed. The combination of FCM and QRNN models resulted in a novel, hybrid model (FCM-QRNN) for predicting blast-induced ground vibration. The US Bureau of Mines (USBM), random forest (RF), QRNN (without clustering), and artificial neural network (ANN) models were also considered and compared with the FCM-QRNN model to obtain a comprehensive assessment of the proposed model. The results indicate that the proposed FCM-QRNN model has a higher accuracy than the other models: USBM, QRNN, RF, and ANN. The proposed model can be used to control the undesirable effects of blast-induced ground vibration. Although this study and the proposed FCM-QRNN model are original works with positive results, the performance of this model in other locations still needs to be considered as a case study for further scientific information.

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Acknowledgments

This research was supported by Center for Mining, Electro-Mechanical research, Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam. The authors would like to thank the three reviewers for a careful review with valuable comments. We thank Dr. Jagannath Aryal a lot for reviewing and helping us to improve the quality of the paper. We also thank all the engineers and leaders of the Tan Dong Hiep quarry mine who helped us with this project.

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Authors and Affiliations

  1. 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 & Quang-Hieu Tran

  2. 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 & Quang-Hieu Tran

  3. Department of Energy Resources Engineering, Pukyong National University, Busan, 48513, Korea

    Yosoon Choi

  4. Center for Advanced Training of Mining Engineers, National University of Science and Technology MISiS, Moscow, Russia, 119049

    Victor Atrushkevich

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

    Hoang Nguyen

  6. Faculty of Surveying and Mapping, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Nguyen Quoc Long

  7. Faculty of Mining, Quang Ninh University of Industry, Yen Tho Ward, Dong Trieu Town, Quang Ninh, Vietnam

    Hung-Thang Hoang

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  1. Xuan-Nam Bui
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Bui, XN., Choi, Y., Atrushkevich, V. et al. Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System. Nat Resour Res 29, 771–790 (2020). https://doi.org/10.1007/s11053-019-09573-7

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