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  A novel clustering classification method of surrounding rock mass for TBM tunnel

  Accurate classification of surrounding rock mass can identify the geological conditions in front of the tunnel face in time, and effectively guide the optimization of TBM tunneling parameters and the design of support schemes. According to on-site tunneling data from the 3rd TBM section of Songhua River water supply project in Jilin province, this paper first extracts the rock mass excavability parameter FPI0.7 and the rock mass machinability parameter TPI1.1 to effectively characterize the rock-machine interaction during the TBM tunneling process. Secondly, the penetration performance parameters FPI0.7 and TPI1.1 is used as the model input, an unsupervised clustering algorithm based on gaussian mixture model is used to establish the clustering classification method of surrounding rock mass by TBM tunnelling to realize the rapid prediction of the surrounding rock grades. The research results show that the proposed clustering classific ation method of surrounding rock mass can real-time predict the surrounding rock grades in front of the tunnel face, providing a novel classification method for surrounding rock mass for TBM tunnel.

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