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A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network

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Abstract

The primary purpose of this study was to develop a novel hybrid artificial intelligence model, with a robust performance, to predict ground vibration induced by bench blasting. An artificial neural network (ANN) was combined with the firefly algorithm (FFA), abbreviated as an FFA-ANN model, for this objective. To develop the FFA-ANN model, an ANN model (i.e., ANN 5-16-20-1) was established first; its weights and biases were then optimized by the FFA. A classification and regression tree (CART), a k-nearest neighbor (KNN), and a support vector machine (SVM) were also developed to confirm the power of the proposed FFA-ANN model. Eighty-three blasting events at a quarry mine in Vietnam were investigated to assess the danger of ground vibration through the developed models. The quality of the developed models was assessed through root-mean-squared error, mean absolute error, coefficient of correlation (R2), and variance account for. A simple ranking method and color gradient technique were also applied to evaluate the performance of the models. The results of this study indicated that the proposed FFA-ANN model was the most dominant model in comparison with other models (i.e., CART, SVM, KNN). The results also demonstrated that the FFA has a vital role in optimizing the ANN model in predicting blast-induced ground vibration.

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Acknowledgments

The authors would like to thank Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam; Duy Tan University, Da Nang, Vietnam; the Center for Mining, Electro-Mechanical research of HUMG, and all the engineers as well as leaders of the Tan Dong Hiep quarry clustering who helped us with this project.

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Authors and Affiliations

  1. Institute of Architecture Engineering, Huanghuai University, Zhumadian, 463000, Henan, China

    Yonghui Shang

  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 street, Duc Thang ward, Bac Tu Liem district, Hanoi, Vietnam

    Xuan-Nam Bui & Quang-Hieu Tran

  4. 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 & Quang-Hieu Tran

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

    Hossein Moayedi

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

    Hossein Moayedi

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  1. Yonghui Shang
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  2. Hoang Nguyen
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Correspondence to Hoang Nguyen or Hossein Moayedi.

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Shang, Y., Nguyen, H., Bui, XN. et al. A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network. Nat Resour Res 29, 723–737 (2020). https://doi.org/10.1007/s11053-019-09503-7

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