Abstract
Rockburst is an extremely complex dynamic instability phenomenon for rock engineering. Due to the complex and unclear mechanism of rockburst, it is difficult to predict precisely and evaluate reasonably the potential of rockburst. With the development of data science and increasing of case history from rock engineering, the data-driven method provides a good way to mine the complex phenomenon of rockburst and then was used to predict the potential of rockburst. In this study, deep learning was adopted to build the data-driven model of rockburst prediction based on the rockburst datasets collected from the literature. The data-driven model was built based on a convolutional neural network (CNN) and compared with the traditional neural network. The results show that the data-driven model can effectively mine the complex phenomenon and mechanism of rockburst. And the proposed method not only can predict the rank of rockburst but also can compute the probability of rockburst for each corresponding rank. It provides a promising and reasonable approach to predict or evaluate the rockburst.
Highlights
Rockburst is an extremely complex dynamic instability phenomenon in rock underground excavation
Convolutional neural network (CNN) is a well-known deep learning architecture inspired by the natural visual perception mechanism of the living creatures [25]
A data-driven model was developed to evaluate the rank of rockburst and its probability of corresponding rank using deep learning
Summary
Rockburst is an extremely complex dynamic instability phenomenon in rock underground excavation. To decrease risk and losses of rockburst, predicting precisely or estimating the reasonable potential of rockburst is critical to the safety and efficient construction of rock underground excavation and mining engineering. Ough rockburst is an unsolved engineering issue for rock underground excavation, a deluge of rockburst data have been available; lots of case histories, monitoring in site, and various tests were implemented, analyzed, and published. Mathematical Problems in Engineering and knowledge from data is a good way to predict or evaluate rockburst. With the development of deep learning, it provides a good way to reveal the mechanism behind data [25]. E data-driven model of rockburst prediction was built based on deep learning.
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