Abstract
Rockburst is a kind of complex and catastrophic dynamic geological disaster in the development and utilization of underground space, which seriously threatens the safety of personnel and environment. Due to the suddenness in time and randomness in space, the prediction of rockburst becomes a great challenge. Microseismic monitoring is capable to continuously capture rock microfracture signals in real time, which offers an effective means for rockburst prediction. With the explosive growth of monitoring data, the conventional manual forecasting methods are laborious and time-consuming. Therefore, artificial intelligence was introduced to improve prediction efficiency. A novel tree-based algorithm was proposed. Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity. The database consisting of 1500 microseismic events was analyzed. To establish precursory microseismic sequences, dimensionality reduction of the database was first implemented by t-SNE algorithm. Then, k-means clustering algorithm was employed for labelling 1500 microseismic events. Before that, canopy algorithm was adopted to determine the number of clusters. Finally, 300 precursory microseismic sequences were formed by the grouping rule. They were further partitioned into two parts through stratified sampling: 70% for training and 30% for validation. The validation results indicated that the precursor tree with pruning achieved a higher prediction accuracy of 98.9% than one without pruning on the validation set. And the increase was separately 12.2%, 9.2% and 28.6% on the whole validation set and each classes (low/moderate rockburst). In comparison with low rockburst, moderate rockburst was minority class. The improved accuracy on moderate rockburst suggested that pruning can enhance the recognition ability of precursor tree for the minority class. Additionally, two extra rockburst cases were collected from a diversion tunnel in northwestern China, which provided a complete workflow about how to apply the built precursor tree model to achieve field rockburst warning in engineering practice. The tree-based algorithm served as a new and promising way for the real-time rockburst prediction, which successfully integrated field microseismic monitoring and artificial intelligence.
Highlights
Rockburst is a common geological disaster in the process of deep underground excavation (Cai et al 2018; Duan et al 2021; Liang et al 2020; Ma et al 2019a; Ma et al 2021; Wang et al 2020; Xu et al 2018)
The validation results indicated that 4 the precursor tree with pruning achieved higher prediction accuracy of 98.9% than one 5 without pruning on the validation set
The tunnel has a length of 41.832km and its maximum burial depth is 2268m, which is excavated by the combination of tunnel boring machine (TBM) and drillingblasting method (DBM) (Deng and Liu 2020; Deng et al 2020; Liu et al 2020)
Summary
Rockburst is a common geological disaster in the process of deep underground excavation (Cai et al 2018; Duan et al 2021; Liang et al 2020; Ma et al 2019a; Ma et al 2021; Wang et al 2020; Xu et al 2018). Internal factors include ground stress, physical and mechanical properties of surrounding rock They determine the energy storage capacity of rock (Xu et al 2017; Zhang et al 2018). Considering the difficulty in obtaining rock mass parameters quickly and accurately during excavation, these models are tough to conduct real-time rockburst prediction, which are more applied to long-term prediction in engineering investigation stage (Zhang et al 2020; Zhou et al 2020). Feng et al (2019b) built an microseismic monitoringbased intelligent rockburst prediction model and applied it in Jinping II Hydropower. Two extra rockburst cases, collected from a diversion tunnel in northwestern China, provided a complete workflow about how to apply the constructed precursor tree model to achieve warning in engineering practice
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
