ISSN 2096-4498

   CN 44-1745/U

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Tunnel Construction ›› 2019, Vol. 39 ›› Issue (1): 48-53.DOI: 10.3973/j.issn.2096-4498.2019.01.005

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Multiple Regression Prediction Model for TBM Net Boring Rate Based on  Mechanical Parameters of Surrounding Rock

YAN Changbin, DU Xuyang, DAI Xiaoya, YAN Shan, LI Gaoliu, CHEN Siyuan   

  1. (School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China)
  • Received:2018-04-17 Revised:2018-10-30 Online:2019-01-20 Published:2019-02-01

Abstract:

The tunneling performances of TBM are closely related to the geological conditions of surrounding rock, especially the mechanical parameters of rock mass. Hence, the study of the relationship between the net boring rate of TBM and mechanical parameters of surrounding rock is of great importance. The doubleshield TBM tunneling in water conveyance tunnel of Lanzhou Water Source Construction Project is taken as an example to analyze the relationship between the net boring rate of TBM and mechanical parameters of surrounding rock; the mechanical parameters of surrounding rock, such as uniaxial compressive strength, uniaxial tensile strength, Poisson′s ratio and deformation modulus, are chosen as key parameters; and the corresponding fitting formula is obtained. And then the single factor fitting analysis is linearly processed to establish a multiple regression prediction model of TBM boring rate. The research results show that: (1) There is a clear linear correlation between the net boring rate of TBM and the mechanical parameters of surrounding rock; the TBM net boring rate decreases with the increase of uniaxial compressive strength, uniaxial tensile strength and deformation, while increases with the increase of Poisson′s ratio. (2) The model has a higher accuracy in general, and its prediction error is less than 15%, which shows good feasibility to different surrounding rocks. The research results can provide reference for TBM tunneling performance evaluation.

Key words: TBM tunneling, net boring rate, surrounding rock, mechanical parameters, multiple linear regression, prediction model

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