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A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

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Abstract

One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole’s diameter (D), hole’s depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.

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Acknowledgements

This research is partially supported by the National Natural Science Foundation Project of China (Grant no. 41807259), the State Key Laboratory of Safety and Health for Metal Mines (Grant no. 2017-JSKSSYS-04), the Shenghua Lieying Program of Central South University (Principle Investigator: Dr. Jian Zhou). The authors would like to express their sincere appreciation to reviewers because of their valuable comments that increased quality of our paper.

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Authors and Affiliations

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

    Jian Zhou

  2. State Key Laboratory of Safety and Health for Metal Mines, Maanshan, 243000, China

    Jian Zhou

  3. School of Housing, Building and Planning, Universiti Sains Malaysia (USM), 11800, Penang, Malaysia

    Nasim Aghili

  4. Department of Real Estate, Faculty of Geo-Information and Real Estate, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Nasim Aghili

  5. Faculty of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran

    Ebrahim Noroozi Ghaleini

  6. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  7. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  8. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    M. M. Tahir

  9. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Mohammadreza Koopialipoor

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Zhou, J., Aghili, N., Ghaleini, E.N. et al. A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network. Engineering with Computers 36, 713–723 (2020). https://doi.org/10.1007/s00366-019-00726-z

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