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Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system

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Abstract

The prediction of the risk of rockbursts in burst-prone grounds is turned into a challenging and vital mission for most underground projects that attract great interest from engineers and researchers. In this study, a hybrid technique, the artificial neural network (ANN) and artificial bee colony (ABC), neuro-bee model, was considered to create the sophisticated relationship between the risk of rockbursts in burst-prone grounds and its influencing factors. The establishment and validation of ANN models were implemented via a data set extracted from previous works, and the database covers 246 reliable rockburst cases. Six influencing factors were selected as input variables. Five-fold cross validation were adopted to tune hyper-parameters of ABC-ANN models, and the performance of ANN models was evaluated by correlation coefficient (R2) and root mean square error (RMSE). Observational experiment results indicated that the ABC-ANN algorithm can be utilized as an effective tool for predicting the risk of rockbursts in burst-prone grounds. The R2 and RMSE values between the predicted and actual rockburst values were 0.9656 and 0.1281, respectively. Sensitivity analyses implemented by the response surface method revealed that the maximum tangential stress of the cavern wall and the elastic strain index parameters have a greater effects on rockburst compared with other input parameters. As a result, the proposed hybrid method outperforms the other models for rockburst prediction in terms of the prediction accuracy and the generalization capability.

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Funding

This work is supported by the National Natural Science Foundation Project of China (41630642; 41807259), the Natural Science Foundation of Hunan Province (Grant No. 2018JJ3693), the Innovation-Driven Project of Central South University (No. 2020CX040), and the Sheng Hua Lie Ying Program of Central South University.

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Authors and Affiliations

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

    Jian Zhou & Enming Li

  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

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  1. Jian Zhou
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  2. Mohammadreza Koopialipoor
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  3. Enming Li
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  4. Danial Jahed Armaghani
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Correspondence to Danial Jahed Armaghani.

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Zhou, J., Koopialipoor, M., Li, E. et al. Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system. Bull Eng Geol Environ 79, 4265–4279 (2020). https://doi.org/10.1007/s10064-020-01788-w

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  • DOI: https://doi.org/10.1007/s10064-020-01788-w

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