Comparative Study of the Excavation Damage and Rockburst of the Deeply Buried Jinping II Diversion Tunnels Using a TBM and the Drilling-Blasting Method
|Veröffentlicht in:||Advances in Civil Engineering, Januar 2020, v. 2020|
The rock mass failure induced by high in-situ stresses during the excavation of deep diversion tunnels is one of the key problems in the construction of the Jinping II Hydropower Station. Based on the results of acoustic wave tests and rockburst statistical analysis conducted, this study focuses on the excavation damaged zone (EDZ) and rockburst events in the Jinping II diversion tunnels excavated using the tunnel boring machine (TBM) method and the drilling-blasting method. The unloading failure mechanism and the rockburst induced by the two different excavation methods were compared and analyzed. The results indicate that, due to the different stress adjustment processes, the degree of damage to the surrounding rock mass excavated using the drilling-blasting method was more serious than that using the TBM method. The EDZ induced by the TBM was usually distributed evenly along the edge of the excavation surface. While, the drilling-blasting method was more likely to cause stress concentration, resulting in a deeper EDZ in local areas. However, the TBM excavation method can cause other problems in high in-situ stress areas, such as strong rockbursts. The drilling-blasting method is more prone to structural controlled failure of the surrounding rock mass, while the TBM method would induce high stress concentration near the edge of excavation and more widely distributed of stress adjustment induced failure. As a result, the scale and frequency of the rockburst events generated by the TBM were significantly greater than those caused by the drilling-blasting method during the excavation of Jinping II diversion tunnels. The TBM method should be used carefully for tunnel excavation in high in-situ stress areas with burial depths of greater than 2000 m. If it is necessary to use the TBM method after a comprehensive selection, it is suggested that equipment adaptability improvement, advanced prediction, and prediction technology be used.
|Copyright:||© Jing Yang et al.|
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