Statistical Characteristics and Rational Estimation of Rock TBM Utilization

암반굴착용 TBM 가동율의 통계적 특성 및 합리적 추정에 관한 연구

  • Received : 2019.10.21
  • Accepted : 2019.10.28
  • Published : 2019.10.31


Various TBM performance prediction models have been developed and most of them were considered penetration rate only. Despite the fact that some models have suggested equations and charts for estimating the utilization factor, but there are a few studies to estimate the TBM utilization factor. Utilization factor is affected by the type of TBM machine, operation, maintenance of machine, geological conditions, contractor experience and other factors. In this study, more than 100 case studies are analyzed to determine the relationship between the utilization factor and RMR, geological conditions, TBM types, tunnel length, and TBM diameter. Simple and multiple linear regression analysis are performed to develop predictive models for the utilization factor. The predictive model with explanatory variables of geological conditions, TBM types, tunnel length, and TBM diameter does not give a good correlation. The predictive models with explanatory variable of RMR give higher values of the coefficient of determination.


  1. Bruland, A., 2000, Hard Rock Tunnel Boring - Advance rate and cutter wear, Vol. 3, Doctoral Thesis at NTNU.
  2. Farrokh, E., 2012, Study of Utilization Factor and Advance Rate of Hard Rock TBMs, Ph.D. Thesis, Department of Energy and Minerals Engineering, The Pennsylvania State University, USA.
  3. Frough, O., Torabi, S.R., and Yagiz, S., 2015, Application of RMR for Estimating Rock-Mass-Related TBM Utilization and Performance Parameters: A Case Study, Rock Mech. Rock Eng., 48(3), 1305-1312.
  4. Jeong, H.Y., Jeon, S., Cho, J.W., Chang, S.H., and Bae, G.J., 2011, Assessment of Cutting Performance of a TBM Disc Cutter for Anisotropic Rock by Linear Cutting Test, Journal of Korean Society for Rock Mechanics, 21(6), 508-517.
  5. Jeong, H.Y., Jeon, S., and Cho, J.W., 2012, A Study on Punch Penetration Test for Performance Estimation of Tunnel Boring Machine, Journal of Korean Society for Rock Mechanics, 22(2), 144-156.
  6. Park, C.H., Shin, J.H., and Han, G.C., 2011, A Study on the Characteristics and Prediction of TBM Utilization Factor, Proceedings of Korean Society for Rock Mechanics, Korea, 241-246.
  7. 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. Thesis, Colorado School of Mines, Golden, Colorado, USA.
  8. Rostami, J., 2016, Performance Prediction of Hard Rock Tunnel Boring Machines (TBMs) in Difficult Ground, Tunnelling and Underground Space Technology, 57, 173-182.
  9. Valle, N.D., Fuoco, S., and Brino, G., 2016, Detailed TBM Boring Cycle Estimation Using Rock Mass Rating System, 2nd International Conference on Tunnel Boring Machines in Difficult Grounds (TBM DiGs Istanbul), Istanbul.
  10. Yagiz , S., Kim, T., Frough, O., and Torabi, S.R., 2013, A Rock Mass Rating System for Predicting TBM Utilization, ISRM International Symposium - EUROCK 2013, 23-26 October, Wroclaw, Poland, 921-925.