Advertisement

Modified Q-index for prediction of rock mass quality around a tunnel excavated with a tunnel boring machine (TBM)

  • Feng Ji
  • Yuchuan ShiEmail author
  • Renjie Li
  • Chunhong Zhou
  • Ning Zhang
  • Jishun Gao
Original Paper
  • 165 Downloads

Abstract

Rock mass quality is closely related to tunnel stability and supporting measures. The Q-system, based on the drilling and blasting method, is one of the most important methods for rock mass classification systems and provides reliable long-term protection for tunnel excavation and reinforcement. However, in comparison to the drilling and blasting method, tunnels excavated using the tunnel boring machine (TBM) method have smooth and integral walls. The number of structural planes in these tunnels, their extension lengths, opening widths, and other characteristics are significantly different from those excavated using the drilling and blasting method. These differences lead to prediction errors in rock mass quality when the Q-system is applied to tunnels excavated by a TBM, and the coincidence rate is less than 70%. In this study, a reduction factor RKv, based on the wave velocity test, is used to replace the RQD/Jn term in the Q-system to reflect the integrity of the rock mass. This replacement can overcome the shortcomings that result from the smooth walls in TBM tunnels by applying the wave velocity during tunnel construction. Based on multiple regression analysis of RKv, we established a QT method for rock classification of material surrounding TBM tunnels. This new method provides a prediction coincidence rate of more than 85%.

Keywords

Rock mass classification Rock mass integrity Reduction factor TBM tunnel Q-system 

Notes

Acknowledgements

We acknowledge the support of the National Natural Science Foundation of China (no. 51308082), the Key Fund Project of the Sichuan Provincial Department of Education (no. 15ZA0075), and the Discovery Fund of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP00002296, SKLGP2016Z011, SKLGP2017Z008). The authors are grateful to all technicians who worked in the laboratory at SKLGP for providing assistance throughout the experimental work. We especially thank Dr. Ming Zeng who provided valuable advice on this research.

References

  1. Alber M (1996) Prediction of penetration, utilization for hard rock TBMs. In: Proceedings of the International Conference of Eurock ‘96, Balkema, Rotterdam, pp 721–725Google Scholar
  2. Alber M (2000) Advance rates of hard rock TBMs and their effects on project economics. Tunnel Undergr Space Technol 15(1):55–64CrossRefGoogle Scholar
  3. Aydin A (2004) Fuzzy set approaches to classification of rock masses. Eng Geol 74:227–245CrossRefGoogle Scholar
  4. Barton N (1999) TBM performance in rock using Q TBM. Tunnels Tunnel Int 31:41–48Google Scholar
  5. Barton N (2000) TBM tunneling in jointed and faulted rock. Balkema, Brookfield, Rotterdam, p 173Google Scholar
  6. Barton N (2002) Some new Q-value correlations to assist in site characterization and tunnel design. Int J Rock Mech Min Sci 39:185–216CrossRefGoogle Scholar
  7. Barton N (2007) Thermal over-closure of joints and rock masses and implications for HLW repositories. Proc. of 11th ISRM Congress, LisbonGoogle Scholar
  8. Barton N, Bieniawski ZT (2008) RMR and Q-setting records. Tunnels Tunnel Int (Feb) 26–29Google Scholar
  9. Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. Int J Rock Mech Min Sci 6:189–236CrossRefGoogle Scholar
  10. Bieniawski ZT (1989) Engineering rock mass classifications. Wiley, New YorkGoogle Scholar
  11. Bieniawski ZT, Grandori R (2007) Predicting TBM excavability – part II. Tunnels Tunnel Int (Dec) 15–18Google Scholar
  12. Bieniawski ZT, Tamames BC, Fernandez JMG, Hernandez MA (2006) Rock Mass Excavability (RME) Indicator: new way to selecting the optimum tunnel construction method. In: ITA-AITES World Tunnel Congress & 32nd ITA General Assembly, SeoulGoogle Scholar
  13. Bieniawski ZT, Celada B, Galera JM (2007a) TBM Excavability: prediction and machine-rock interaction. In: Proceedings, Rapid Excavation and Tunneling Conference pp 1118–1130Google Scholar
  14. Bieniawski ZT, Celada B, Galera JM (2007b) Predicting TBM excavability – part I. Tunnels Tunnel Int (Sep) 32–35Google Scholar
  15. Chen CS, Liu YC (2007) A methodology for evaluation and classification of rock mass quality on tunnel engineering. Tunneling and Underground SpaceGoogle Scholar
  16. Choi Y, Yoon SY, Park HD (2009) Tunneling analyst: a 3D GIS extension for rock mass classification and fault zone analysis in tunneling. Comput Amp Geosci 35:1322–1333CrossRefGoogle Scholar
  17. Christe P, Turberg P, Labiouse V, Meuli R, Parriaux A (2011) An X-ray computed tomography-based index to characterize the quality of cataclastic carbonate rock samples. Eng Geol 117(3–4):180–188CrossRefGoogle Scholar
  18. Deere DU, Hendron AJ, Patton FD, Cording EJ (1967) Design of surface and nearsurface construction in rock. In: Fairhurst C (ed) Proceedings of the US rock mechanics symposium, failure and breakage of rock. Society of Mining Engineers of AIME, New York, pp 237–302Google Scholar
  19. Ergül Y (2001) A new rock mass classification for coal measures rocks. Eng Geol 62:293–300CrossRefGoogle Scholar
  20. Grimstad E, Barton N (1993) Updating of the Q-system for NMT. Int. symposium on spayed concrete — modern use of wet mix sprayed concrete for underground support. Fagernes Technol 22:377–387Google Scholar
  21. González LI, Vallejo D (2003) SRC rock mass classification of tunnels under high tectonic stress excavated in weak rocks. Eng Geol 69:273–285CrossRefGoogle Scholar
  22. Heuer RE (1995) Estimating rock tunnel water inflow. In: Proceedings of the rapid excavation and tunneling conference p 41–60Google Scholar
  23. Hoek E, Diederichs MS (2006) Empirical estimation of rock mass modulus. Int J Rock Mech Min Sci 43:203–215CrossRefGoogle Scholar
  24. Hoseinie SH, Aghababaei H, Pourrahimian Y (2008) Development of a new classification system for assessing of rock mass drillability index (RDi). Int J Rock Mech Min Sci 45:1–10CrossRefGoogle Scholar
  25. Hoseinie SH, Ataei M, Osanloo M (2009) A new classification system for evaluating rock penetrability. Int J Rock Mech Min Sci 46:1329–1340CrossRefGoogle Scholar
  26. Jalalifar H, Mojedifar S, Sahebi AA, Nezamabadi H (2011) Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 38:783–790CrossRefGoogle Scholar
  27. Li C, Gu T, Ding J et al (2010) Discussion on rock classification in TBM construction tunnel. J Eng Geol 18:730–735Google Scholar
  28. Li SJ, Feng XT, Li ZH, Chen BR, Zhang CQ, Zhou H (2012a) In situ monitoring of rockburst nucleation and evolution in the deeply buried tunnels of Jinping II hydropower station. Eng Geol 137–138:85–96Google Scholar
  29. Li SJ, Feng XT, Li ZH, Zhang CQ, Chen BR (2012b) Evolution of fractures in the excavation damaged zone of a deeply buried tunnel during TBM construction. Int J Rock Mech Min Sci 55:125–138Google Scholar
  30. Liu F, Huang X, Shi K (2011) Surround rock classification of risk analysis based on TBM roadway excavation. Coal Eng 12:77–79Google Scholar
  31. Liu YC, Chen CS (2007) A new approach for application of rock mass classification on rock slope stability assessment. Eng Geol 89:129–143CrossRefGoogle Scholar
  32. Qi S, Wu F (2011) Surrounding rock mass quality classification of tunnel cut by TBM with fuzyy mathematics method. Chin J Rock Mech Eng 30:1225–1229Google Scholar
  33. Qiu DH, Li S, Zhang LW, Xue YG (2010) Application of GA–SVM in classification of surrounding rock based on model reliability examination. Min Sci Technol (China) 20:428–433CrossRefGoogle Scholar
  34. Read SAL, Richards LR, Perrin ND (1999) Applicability of the Hoek–Brown failure criterion to New Zealand greywacke rocks. In: Vouille G, Berest P, editors. Proceedings of the 9th international congress on rock mechanics. Paris, 2 August, p 655–60Google Scholar
  35. Ramamurthy T (2004) A geo-engineering classification for rocks and rock masses. Int J Rock Mech Min Sci 41:89–101CrossRefGoogle Scholar
  36. Sun J, Lu W, Su L et al (2008) Rock mass rating identification based on TBM performance parameters and muck characteristics. Chin J Geotech Eng 30(12):82–89Google Scholar
  37. Wu Y, Wu X, Yin J (2006) Research with relation to rock classification of TBM tunnels. Hydrogeol Eng Geol 33:120–122Google Scholar
  38. Xu NW, Tang CA, Li LC, Zhou Z, Sha C, Liang ZZ, Yang JY (2011) Microseismic monitoring and stability analysis of the left bank slope in Jinping first stage hydropower station in southwestern China. Int J Rock Mech Min Sci 48(6):950–963CrossRefGoogle Scholar
  39. Zhang CQ, Feng XT, Zhou H, Qiu SL, Wu WP (2012b) Case histories of four extremely intense rockbursts in deep tunnels. Rock Mech Rock Eng 45:275–288Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Geohazard Prevention and Geoenvironment ProtectionChengdu University of TechnologyChengduPeople’s Republic of China
  2. 2.China Hydropower Consulting Group Co.East China Investigation & Design InstituteFuzhouPeople’s Republic of China

Personalised recommendations