Rock and Soil Mechanics ›› 2020, Vol. 41 ›› Issue (5): 1721-1729.doi: 10.16285/j.rsm.2019.5963

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Prediction and classification of rock mass boreability in TBM tunnel

WU Xin-lin1, 2, ZHANG Xiao-ping1, 2, LIU Quan-sheng1, 2, LI Wei-wei3, HUANG Ji-min3   

  1. 1. School of Civil Engineering, Wuhan University, Wuhan, Hubei 430072, China; 2. Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province, Wuhan University, Wuhan, Hubei 430072, China; 3. Manufacturing and Installation Branch Sinohyaro Bureau 3 Co., Ltd, Xi’an, Shaanxi 710024, China
  • Online:2020-05-27 Published:2020-10-13
  • Contact: ZHANG Xiao-ping, male, born in 1982, PhD, Professor, PhD tutor, research interests: rock mechanics, engineering geology and underground engineering. E-mail: jxhkzhang@ 163. com
  • About author:WU Xin-lin, male, born in 1996, Master degree candidate, focusing on research on rock mass classification of tunnel constructed by TBM
  • Supported by:
    the National Natural Science Foundation of China (No. 51978541, No. 41941018, No. 51839009).

Abstract: Due to the extremely high sensitivity of tunnel boring machine(TBM)performance to rock mass conditions and its huge early investment, it is of great value to evaluate the rock mass boreability and predict the TBM performance. In this study, about 300 sets of field data from China and Iran are collected, covering three different rock types and 5 TBM tunnels. FPI (field penetration index) is selected as the evaluation index of rock mass boreability. Specifically, the relationships between rock uniaxial compressive strength(UCS), rock mass integrity index , angle between main structural plane of rock mass and axis of the tunnel ?, tunnel diameter, D and rock mass boreability are systematically analyzed. In addition, a unified approach of rock mass parameters which is suitable for the study of rock mass boreability is discussed in detail, and an empirical prediction model of rock mass boreability with relatively high accuracy ( 0.768) is further established. Based on this model and supplemented by K-center clustering method, the boreability of rock mass are classified into 6 groups, which are then applied to the exploration of the distribution of average cutter thrust and cutterhead rotation speed under various of rock mass boreability conditions. The findings in our work shed light on the evaluation of rock mass boreability, the selection of operational parameters as well as the arrangement of TBM tunnel construction schedule.

Key words: tunnel boring machine(TBM), boreability prediction, rock mass classification, operational parameters