Evaluation of Rockburst Classification Using Deep Learning Algorithm - RASIM2022

The literature defines rockbursts in slightly different ways based on their generation mechanisms, field phenomena, and degree of damage, but in a way that is almost consistent. Generally, a rockburst refers to seismic events that mainly happen in underground excavations, for purposes such as underground mining, road and railway tunnels, nuclear power, etc., and that are followed by the collapse and outburst of rocks. Long-term rockburst prediction can be abstracted as a classification problem mathematically, in which we can introduce deep learning to solve it. Further, using deep learning in rockburst prediction can overcome drawbacks such as subjectivity and inconsistency brought from previous traditional approaches. A rockburst data-set, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the proposed algorithms can speed up parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.