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Estimation of Ground Vibration Intensity Induced by Mine Blasting using a State-of-the-Art Hybrid Autoencoder Neural Network and Support Vector Regression Model

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Abstract

In surface mining, blasting is an indispensable method for fragmenting rock masses. Nevertheless, it can inherently induce many side effects like ground vibrations. At high intensities, the ground vibrations generated because of blasting operations can destroy structures and buildings. Also, in areas with adverse geological conditions, such vibrations can cause bench and slope failures. Therefore, the accurate prediction of ground vibration intensity (GVI) has critical implications in mitigating and controlling the adverse effects along with sustainable development and responsible mining. In this research, a novel intelligent model was proposed to predict GVI based on the hybridization of autoencoder neural networks (AutoencoderNN) and support vector machine regression (SVR), and it was named AutoencoderNN-SVR. Nine input variables were utilized to estimate GVI: borehole diameter, bench height, borehole length, burden, spacing, hardness coefficient, powder factor, maximum explosive charged per delay, and monitoring distance. Two hundred ninety-seven blasting events were collected, analyzed, and evaluated to achieve this aim. Also, the traditional SVR model without the support of AutoencoderNN, an empirical equation (i.e., USBM), and a nonlinear model based on gene expression programing were applied in this research and compared with the proposed AutoencoderNN-SVR model in terms of GVI prediction. Then, the models' obtained results were analyzed and computed through statistical indices, such as root mean squared error (RMSE) and coefficient of determination (R2). The AutoencoderNN-SVR's ensemble model was found to have obtained the highest accuracy and lowest error (i.e., RMSE = 1.232 and R2 = 0.887) compared to the other models and is an insight in predicting GVI in mine blasting with high reliability.

Highlights

  • An autoencoder neural network was investigated to predict GVI in mine blasting;

  • An autoencoder neural network was combined with support vector regression to generate a robust hybrid model (AutoencoderNN-SVR) to predict GVI in mine blasting;

  • The proposed AutoencoderNN-SVR model was compared with the empirical, SVR, and GEP models;

  • The proposed AutoencoderNN-SVR model was introduced as a novel and robust technique for predicting GVI with high accuracy.

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Acknowledgments

This paper was supported by the Ministry of Education and Training (MOET) in Viet Nam under Grant No. B2020-MDA-16.

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Authors and Affiliations

  1. School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China

    Bo Ke

  2. School of Intelligent Construction, Wuchang University of Technology, Wuhan, 430223, China

    Bo Ke

  3. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien str., Duc Thang ward, Bac Tu Liem district, Hanoi, 100000, Vietnam

    Hoang Nguyen & Xuan-Nam Bui

  4. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien str., Duc Thang ward, Bac Tu Liem district, Hanoi, 100000, Vietnam

    Hoang Nguyen & Xuan-Nam Bui

  5. Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 500152, Brasov, Romania

    Romulus Costache

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  1. Bo Ke
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  2. Hoang Nguyen
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  3. Xuan-Nam Bui
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  4. Romulus Costache
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Correspondence to Hoang Nguyen.

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Ke, B., Nguyen, H., Bui, XN. et al. Estimation of Ground Vibration Intensity Induced by Mine Blasting using a State-of-the-Art Hybrid Autoencoder Neural Network and Support Vector Regression Model. Nat Resour Res 30, 3853–3864 (2021). https://doi.org/10.1007/s11053-021-09890-w

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