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Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

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Abstract

When working on underground projects, especially where ground is burst prone, it is of a high significance to accurately predict the risk of rockburst. The present paper integrates the firefly algorithm (FA) and artificial neural network (ANN) aiming at modeling the complex relationship between the rockburst risk in deep mines and tunnels and factors effective on this phenomenon. The model was established and validated through the use of a data set extracted from previously conducted studies. The data set involves a total of 196 reliable rockburst cases. The use of smart systems was used to classify and determine patterns in this research using model development. The hybrid FA–ANN model provides a solution for determining different classes of hazard under different conditions. The capability of these developed systems was implemented to determine the four types of levels defined for this phenomenon. The results of these systems led to new solutions to classify this phenomenon by success rates. Each system, given its performance, yields a unique error. Finally, by combining the number of correctly classified classes and their error values, the success rates in the classification of rockburst phenomena in mines and underground tunnels were evaluated.

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Acknowledgements

This research was funded by the National Science Foundation of China (41630642; 41807259), the Natural Science Foundation of Hunan Province (2018JJ3693), the Innovation-Driven Project of Central South University (2020CX040) and the Shenghua Lieying Program of Central South University (Principle Investigator: Dr. Jian Zhou).

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Authors and Affiliations

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

    Jian Zhou & Hongquan Guo

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

    Mohammadreza Koopialipoor

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

    Danial Jahed Armaghani

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

    M. M. Tahir

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  1. Jian Zhou
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Zhou, J., Guo, H., Koopialipoor, M. et al. Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm. Engineering with Computers 37, 1679–1694 (2021). https://doi.org/10.1007/s00366-019-00908-9

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