Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system
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ORIGINAL PAPER
Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system Jian Zhou 1 & Mohammadreza Koopialipoor 2 & Enming Li 1 & Danial Jahed Armaghani 3 Received: 16 May 2019 / Accepted: 26 March 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
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 ABCANN 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. Keywords Rockburst . ABC-ANN . ANN . Prediction . Risk
Introduction Rockburst is one of the dynamic and geological disasters that occurs in underground hydropower caverns, tunnels, and hard rock mines. The phenomenon of rockbursts is attributable to * Danial Jahed Armaghani [email protected] Jian Zhou [email protected] Mohammadreza Koopialipoor [email protected] Enming Li [email protected] 1
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
2
Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran
3
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
the abrupt release of potential energy in the rock mass under some certain condition (Cook 1965; Kaiser et al. 1996; Wang et al. 2006; Gong et al. 2018; Zhou et al. 2018). The occurrence of rockbursts usually causes immeasurable damage to equipment and/or infrastructure and may even lead to fatalities due to the fact that rockbursts occur suddenly and intensely (Zhou et al. 2016a, 2016b). In contrast with the past, the geotechnical activities usually are carried out und
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