부산대학교 도서관

Rockburst is a kind of common geological disaster in deep tunnel engineering. It has the characteristics of causing great harm and occurring at random locations and times. These characteristics seriously affect tunnel construction and threaten the physical and mental health and safety of workers. Therefore, it is of great significance to study the tendency of rockburst in the early stage of tunnel survey, design and construction. At present, there is no unified method and selected parameters for rockburst prediction. In view of the large difference of different rockburst criteria and the imbalance of rockburst database categories, this paper presents a two-step rockburst prediction method based on multiple factors and the stacking ensemble algorithm. Considering the influence of rock physical and mechanical parameters, tunnel face conditions and excavation disturbance, multiple rockburst criteria are predicted by integrating multiple machine learning algorithms. A combined prediction model of rockburst criteria is established, and the results of each rockburst criterion index are weighted and combined, with the weight updated using the field rockburst record. The dynamic weight is combined with the cloud model to comprehensively evaluate the regional rockburst risk. Field results from applying the model in the Grand Canyon tunnel show that the rockburst prediction method proposed in this paper has better applicability and higher accuracy than the single rockburst criterion.