Geomechanics and Engineering

Techno-Press (테크노프레스)
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Prediction of TBM performance based on specific energy

  • Kim, Kyoung-Yul (Structural and Seismic Technology Group, Next Generation Transmission & Substation Laboratory, KEPCO Research Institute(KEPRI)) ;
  • Jo, Seon-Ah (Structural and Seismic Technology Group, Next Generation Transmission & Substation Laboratory, KEPCO Research Institute(KEPRI)) ;
  • Ryu, Hee-Hwan (Structural and Seismic Technology Group, Next Generation Transmission & Substation Laboratory, KEPCO Research Institute(KEPRI)) ;
  • Cho, Gye-Chun (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology(KAIST))
  • Received : 2019.08.28
  • Accepted : 2020.08.10
  • Published : 2020.09.25
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Abstract

This study proposes a new empirical model to effectively predict the excavation performance of a shield tunnel boring machine (TBM). The TBM performance is affected by the geological and geotechnical characteristics as well as the machine parameters of TBM. Field penetration index (FPI) is correlated with rock mass parameters to analyze the effective geotechnical parameters influencing the TBM performance. The result shows that RMR has a more dominant impact on the TBM performance than UCS and RQD. RMR also shows a significant relationship with the specific energy, which is defined as the energy required for excavating the unit volume of rock. Therefore, the specific energy can be used as an indicator of the mechanical efficiency of TBM. Based on these relationships with RMR, this study suggests an empirical performance prediction model to predict FPI, which can be derived from the correlation between the specific energy and RMR.

Keywords

References

  1. Barton, N. (1999), "TBM performance estimation in rock using Q(TBM)", Tunn. Tunn. Int., 31(9), 30-34.
  2. Barton, N. (2000), TBM Tunneling in Jointed and Faulted Rock, A.A. Balkema, Rotterdam, The Netherlands.
  3. Benato, A. and Oreste, P. (2015), "Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics", Int. J. Rock Mech. Min. Sci., 74, 119-127. https://doi.org/10.1016/j.ijrmms.2014.12.007.
  4. Bieniawski, R.Z.T., Celada, B., Tardaguila, I. and Rodrigues, A. (2012), "Specific energy of excavation in detecting tunnelling conditions ahead of TBMs", Tunn. Tunn. Int., 26-29.
  5. Blindheim, O.T. (1979), "Boreability predictions for tunneling", Ph.D. Dissertation, The Norwegian Institute of Technology, Trondheim, Norway.
  6. Bruland, A. (1998), "Hard rock tunnel boring", Ph.D. Dissertation, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
  7. Celada, B., Galera, J.M., Munoz, C. and Tardaguila, I. (2009), "The use of the specific drilling energy for rock mass characterization and TBM driving during tunnel construction", Proceedings of the ITA-AITES World Tunnel Congress, Budapest, Hungary, May.
  8. Chang, S., Choi, S., Bae, G. and Jeon, S. (2006a), "Performance prediction of TBM disc cutting on granitic rock by the linear cutting test", Tunn. Undergr. Sp. Technol., 21(3-4), 271. https://doi.org/10.1016/j.tust.2005.12.131
  9. Chang, S., Choi, S., Lee, S., Bae, G. and Jeon, S. (2006b), "Derivation of an optimum prediction model of a TBM cutterhead equipped with disc cutters", Proceedings of the KTA 2006 Symposium, Seoul, South Korea, October.
  10. Delisio, A. and Zhao, J. (2014), "A new model for TBM performance prediction in blocky rock conditions", Tunn. Undergr. Sp. Technol., 43, 440-452. https://doi.org/10.1016/j.tust.2014.06.004
  11. Delisio, A., Zhao, J. and Einstein, H.H. (2013), "Analysis and prediction of TBM performance in blocky rock conditions at the Lotschberg Base Tunnel", Tunn. Undergr. Sp. Technol., 33, 131-142. https://doi.org/10.1016/j.tust.2012.06.015
  12. Farrokh, E., Rostami, J. and Laughton, C. (2012), "Study of various models for estimation of penetration rate of hard rock TBMs", Tunn. Undergr. Sp. Technol., 30, 110-123. https://doi.org/10.1016/j.tust.2012.02.012
  13. Gong, Q.M. and Zhao, J. (2009), "Development of a rock mass characteristics model for TBM penetration rate prediction", Int. J. Rock Mech. Min. Sci., 46(1), 8-18. https://doi.org/10.1016/j.ijrmms.2008.03.003.
  14. Hassanpour, J., Ghaedi Vanani, A.A., Rostami, J. and Cheshomi, A. (2016), "Evaluation of common TBM performance prediction models based on field data form the second lot of Zagros water conveyance tunnel (ZWCT2)", Tunn. Undergr. Sp. Technol., 52, 147-156. https://doi.org/10.1016/j.tust.2015.12.006.
  15. Hassanpour, J., Rostami, J. and Zhao, J. (2011), "A new hard rock TBM performance prediction model for project planning", Tunn. Undergr. Sp. Technol., 26, 595-603. https://doi.org/10.1016/j.tust.2011.04.004.
  16. Minh, V.T., Katushin, D., Antonov, M. and Veinthal, R. (2017), "Regression models and fuzzy logic prediction of TBM penetration rate", Open Eng., 7(1), 60-68. https://doi.org/10.1515/eng-2017-0012.
  17. Mobarral, Y., Anbaranil, M.R. and Akbaril, E. (2013), "A statistical model for the prediction of TBM penetration rate using rock mass characteristics in the TBM-driven Golab Water Transfer Tunnel", Proceedings of the 4th International Joint Conference on Advances in Engineering and Technology-AET 2013, NCR, India, December.
  18. Rostami, J. (1997), "Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure", Ph.D. Dissertation, Colorado School of Mines, Golden, Colorado, U.S.A.
  19. Rostami, J. and Ozdemir, L. (1993), "A new model for performance prediction of hard rock TBMs", Proceedings of the Rapid Excavation Tunneling Conference (RETC), Boston, Massachusetts, U.S.A., June.
  20. Salimi, A., Rostami, J., Moormann, C. and Delisio, A. (2016), "Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs", Tunn. Undergr. Sp. Technol., 58, 236-246. https://doi.org/10.1016/j.tust.2016.05.009.
  21. Teale, R. (1965), "The concept of specific energy in rock drilling", Int. J. Rock Mech. Min. Sci., 2(1), 57-73. https://doi.org/10.1016/0148-9062(65)90022-7.
  22. Yagiz, S. (2002), "Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM model basic penetration for hard rock tunneling machines", Ph.D. Dissertation, Colorado School of Mines, Golden, Colorado, U.S.A.
  23. Yagiz, S. (2008), "Utilizing rock mass properties for predicting TBM performance in hard rock condition", Tunn. Undergr. Sp. Technol., 23(3), 326-339. https://doi.org/10.1016/j.tust.2007.04.011.
  24. Zhang, G., Tan, J., Zhang, L. and Xiang, Y. (2015), "Linear regression analysis for factors influencing displacement of high-filled embankment slopes", Geomech. Eng., 8(4), 511-521. https://doi.org/10.12989/gae.2015.8.4.511.
  25. Zhou, W., Li, S.L., Ma, G., Chang, X.L., Cheng, Y.G. and Ma, X. (2016), "Assessment of the crest cracks of the Pubugou rockfill dam based on parameters back analysis", Geomech. Eng., 11(4), 571-585. https://doi.org/10.12989/gae.2016.11.4.571.

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