An Experimental-Based Model for Prediction of the Rock Mass-Related TBM Utilization by Adopting the RMR and Moisture-Dependent CAI

Changbin Yan; Ziang Gao; Gongbiao Yang; Zihe Gao; Lei Huang; Jihua Yang

doi:10.1007/s12583-022-1771-5

Volume 36 Issue 2

Apr 2025

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Journal of Earth Science > 2025 > 36(2): 668-684.

Changbin Yan, Ziang Gao, Gongbiao Yang, Zihe Gao, Lei Huang, Jihua Yang. An Experimental-Based Model for Prediction of the Rock Mass-Related TBM Utilization by Adopting the RMR and Moisture-Dependent CAI. Journal of Earth Science, 2025, 36(2): 668-684. doi: 10.1007/s12583-022-1771-5

Citation:

Changbin Yan, Ziang Gao, Gongbiao Yang, Zihe Gao, Lei Huang, Jihua Yang. An Experimental-Based Model for Prediction of the Rock Mass-Related TBM Utilization by Adopting the RMR and Moisture-Dependent CAI. Journal of Earth Science, 2025, 36(2): 668-684. doi: 10.1007/s12583-022-1771-5

Citation:

Changbin Yan, Ziang Gao, Gongbiao Yang, Zihe Gao, Lei Huang, Jihua Yang. An Experimental-Based Model for Prediction of the Rock Mass-Related TBM Utilization by Adopting the RMR and Moisture-Dependent CAI. Journal of Earth Science, 2025, 36(2): 668-684. doi: 10.1007/s12583-022-1771-5

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An Experimental-Based Model for Prediction of the Rock Mass-Related TBM Utilization by Adopting the RMR and Moisture-Dependent CAI

doi: 10.1007/s12583-022-1771-5

1.
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
2.
China Railway Construction Underwater Tunnel Engineering Laboratory, Jinan 250101, China
3.
Three Gorges Research Center for Geohazards, Ministry of Education, China University of Geosciences (Wuhan), Wuhan 430074, China
4.
Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China

More Information

Corresponding author: Zihe Gao, gaozihe@zzu.edu.cn
Received Date: 04 Apr 2022
Accepted Date: 17 Oct 2022
Issue Publish Date: 30 Apr 2025

Abstract

Abstract

To reduce the uncertainty associated with the traditional definition of tunnel boring machine (TBM) utilization (U) and achieve an effective indicator of TBM performance, a new performance indicator called rock mass-related utilization (U_r) is introduced; this variable considers only rock mass-related factors rather than all potential factors. This work aims to predict U_r by adopting the rock mass rating (RMR) and the moisture-dependent Cerchar abrasivity index (CAI). Substantial U_r, RMR and CAI data are acquired from a 31.57 km northwestern Chinese water conveyance tunnel via tunnelling field recordings, geological investigations and Cerchar abrasivity tests. The moisture dependence of the CAI is explored across four lithologies: quartz schists, granites, sandstones and metamorphic andesites. The potential influences of RMR and CAI on U_r are then investigated. As the RMR increases, U_r initially increases and then peaks at an RMR of 56 before declining. U_r appears to decline with CAI. An investigation-based relation among U_r, RMR and moisture-dependent CAI is developed for estimating U_r. The developed relation can accurately predict U_r using RMR and moisture-dependent CAI in the majority of the tunnelling cases examined. This work proposes a stable indicator of TBM performance and provided a fairly accurate prediction method for this indicator.
- tunnel boring machine (TBM),
- utilization,
- RMR system,
- Cerchar abrasivity index (CAI),
- predicting model,
- engineering geology

Conflict of Interest
The authors declare that they have no conflict of interest.

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References(48)

References

Alber, M. , 1996. Prediction of Penetration and Utilization for Hard Rock TBMs. In: ISRM International Symposium-EUROCK 96. International Society for Rock Mechanics and Rock Engineering, 721–725

Alber, M. , Yaralı, O. , Dahl, F. , et al. , 2014. ISRM Suggested Method for Determining the Abrasivity of Rock by the CERCHAR Abrasivity Test. Rock Mechanics and Rock Engineering, 47(1): 261–266. https://doi.org/10.1007/s00603-013-0518-0

Apaydin, A. , Korkmaz, N. , Ciftci, D. , 2019. Water Inflow into Tunnels: Assessment of the Gerede Water Transmission Tunnel (Turkey) with Complex Hydrogeology. Quarterly Journal of Engineering Geology and Hydrogeology, 52(3): 346–359. https://doi.org/10.1144/qjegh2017-125

ASTM-D7625, 2010. Standard Test Method for Laboratory Determination of Abrasiveness of Rock Using the CERCHAR Method. American Society for Testing and Materials

Abu Bakar, M. Z. , Majeed, Y. , 2020. Load-Indentation Testing to Assess Porewater Pressure Effects on Linear Drag and Disc Cutting of a Saturated Permeable Brittle Rock. Quarterly Journal of Engineering Geology and Hydrogeology, 53(1): 117–124. https://doi.org/10.1144/qjegh2018-008

Abu Bakar(Bakar), M. Z. , Majeed, Y. , Rostami, J. , 2016. Effects of Rock Water Content on CERCHAR Abrasivity Index. Wear, 368/369: 132–145. https://doi.org/10.1016/j.wear.2016.09.007

Barton, N. , 2000. TBM Tunnelling in Jointed and Faulted Rock. Balkema, Brookfield

Bieniawski, Z. T. , 1989. Engineering Rock Mass Classifications. Wiley, New York

Bilgin, N. , Balci, C. , Acaroglu, O. , et al. , 1999. The Performance Prediction of a TBM in Tuzla-Dragos Sewerage Tunnel. In: World Tunnel Congress, Challenges for the 21st Century. Balkema, 817–827

Bruland, A. , 1998. Hard Rock Tunnel Boring: [Dissertation]. The Norwegian University of Science and Technology, Trondheim

Cardu, M. , Oreste, P. , Cicala, T. , 2009. Analysis of the Tunnel Boring Machine Advancement on the Bologna-Florence Railway Link. American Journal of Engineering and Applied Sciences, 2(2): 416–420. https://doi.org/10.3844/ajeassp.2009.416.420

Delisio, A. , Zhao, J. , 2014. A New Model for TBM Performance Prediction in Blocky Rock Conditions. Tunnelling and Underground Space Technology, 43: 440–452. https://doi.org/10.1016/j.tust.2014.06.004

Einstein, H. H. , 1996. Risk and Risk Analysis in Rock Engineering. Tunnelling and Underground Space Technology, 11(2): 141–155. https://doi.org/10.1016/0886-7798(96)00014-4

Farrokh, E. , 2018. Introducing Hard Rock TBMS’ Downtime Analysis Model with Reference to Past Case Histories’ Data. Journal of Mining and Environment, 9: 457–472

Farrokh, E. , Rostami, J. , Laughton, C. , 2011. Analysis of Unit Supporting Time and Support Installation Time for Open TBMS. Rock Mechanics and Rock Engineering, 44(4): 431–445. https://doi.org/10.1007/s00603-011-0135-8

Frough, O. , Torabi, S. R. , 2013. An Application of Rock Engineering Systems for Estimating TBM Downtimes. Engineering Geology, 157: 112–123. https://doi.org/10.1016/j.enggeo.2013.02.003

Frough, O. , Torabi, S. R. , Yagiz, S. , 2015. Application of RMR for Estimating Rock-Mass–Related TBM Utilization and Performance Parameters: A Case Study. Rock Mechanics and Rock Engineering, 48(3): 1305–1312. https://doi.org/10.1007/s00603-014-0619-4

Frough, O. , Khetwal, A. , Rostami, J. , 2019. Predicting TBM Utilization Factor Using Discrete Event Simulation Models. Tunnelling and Underground Space Technology, 87: 91–99. https://doi.org/10.1016/j.tust.2019.01.017

Gong, Q. M. , Lu, J. W. , Wei, J. Z. , et al. , 2018. Study on Estimation and Prediction of TBM Utilization Rate Using Rock Mass Rating (RMR). Construction Technology, 47(5): 92–98, 127 (in Chinese with English Abstract) doi: 10.3969/j.issn.1009-4539.2018.05.025

Han, L. , Wang, L. , Zhang, W. G. , 2020. Quantification of Statistical Uncertainties of Rock Strength Parameters Using Bayesian-Based Markov Chain Monte Carlo Method. IOP Conference Series: Earth and Environmental Science, 570(3): 032051. https://doi.org/10.1088/1755-1315/570/3/032051

Han, L. , Wu, C. , Zhang, W. G. , et al. , 2022. Estimation of Compressibility of Bukit Timah Granitic Residual Soils in Singapore and Variability Analysis. International Journal of Geotechnical Engineering, 16(3): 327–342. https://doi.org/10.1080/19386362.2021.1968649

Hamidi, J. K. , Shahriar, K. , Rezai, B. , et al. , 2010. Performance Prediction of Hard Rock TBM Using Rock Mass Rating (RMR) System. Tunnelling and Underground Space Technology, 25(4): 333–345. https://doi.org/10.1016/j.tust.2010.01.008

Hassanpour, J. , Rostami, J. , Zhao, J. , 2011. A New Hard Rock TBM Performance Prediction Model for Project Planning. Tunnelling and Underground Space Technology, 26(5): 595–603. https://doi.org/10.1016/j.tust.2011.04.004

Jenni, J. P. , Balissat, M. , 1979. Rock Testing Methods Performed to Predict the Utilization Possibilities of a Tunnel Boring Machine. In: 4th ISRM Congress. International Society for Rock Mechanics and Rock Engineering, Montreux

Kahraman, S. , 2002. Correlation of TBM and Drilling Machine Performances with Rock Brittleness. Engineering Geology, 65(4): 269–283. https://doi.org/10.1016/s0013-7952(01)00137-5

Kim, T. , 2004. Development of a Fuzzy Logic-Based Utilization Predictor Model for Hard Rock Tunnel Boring Machines: [Dissertation]. Colorado School of Mines, Colorado

Koor, N. P. , 2018. What Lies Beneath? A Decade of Underground Construction in Hong Kong. Quarterly Journal of Engineering Geology and Hydrogeology, 51: 301–310. https://doi.org/10.1144/qjegh201-081

Linde-Arias, E. , Harris, D. , Ghail, R. , 2018. Engineering Geology and Tunnelling in the Limmo Peninsula, East London. Quarterly Journal of Engineering Geology and Hydrogeology, 51: 23–30

Liu, Q. S. , Liu, J. P. , Pan, Y. C. , et al. , 2017. A Wear Rule and Cutter Life Prediction Model of a 20-In. TBM Cutter for Granite: A Case Study of a Water Conveyance Tunnel in China. Rock Mechanics and Rock Engineering, 50(5): 1303–1320. https://doi.org/10.1007/s00603-017-1176-4

Liu, L. , Guo, J. , Wang, H. , 2012. Experimental Study on the Correlation between Rock Abrasiveness and Triaxial Strength of the Hanjiang-to-Weihe Water Conveyance Tunnel. Des. Hydroelectr. Power Stn., 28: 88–90 (in Chinese with English Abstract)

Maleki, Z. , Farhadian, H. , Nikvar-Hassani, A. , 2021. Geological Hazards in Tunnelling: The Example of Gelas Water Conveyance Tunnel, Iran. Quarterly Journal of Engineering Geology and Hydrogeology, 54(1): qjegh2019-2114. https://doi.org/10.1144/qjegh2019-114

Moosazadeh, S. , Aghababaie, H. , Hoseinie, H. , et al. , 2018. Simulation of Tunnel Boring Machine Utilization: A Case Study. Journal of Mining and Environment, 9: 53–60

Nelson, P. P. , O’Rourke, T. D. , Glaser, S. D. , 1985. TBM System Downtime―Causes, Frequency and Duration on Six Tunnel Projects. In: Rapid Excavation and Tunneling Conference Proceedings. Society of Mining Engineers, June 16–20, 1985, New York

Patel, S. , Pandit, D. , 2018. Assessment of TBM’s Performance Parameters. In: The 6th International Conference on Energy, Infrastructure & Management (ICEIM-2018). ICEIM, February 15–16, 2018, Gandhinagar

Palmström, A. , 2009. Combining the RMR, Q, and RMi Classification Systems. Tunnelling and Underground Space Technology, 24(4): 491–492. https://doi.org/10.1016/j.tust.2008.12.002

Phillips, H. R. , Roxborough, F. F. , 1981. The Influence of Tool Material on the Wear Rate of Rock Cutting Picks. In: Proceedings of the 34th Annual Conference of Australian Institute of Metals. Australian Institute of Physics, 52–56

Rasouli, M. , 2009. Engineering Geological Studies of the Diversion Tunnel, Focusing on Stabilization Analysis and Support Design, Iran. Engineering Geology, 108(3/4): 208–224. https://doi.org/10.1016/j.enggeo.2009.07.007

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: [Dissertation]. Colorado School of Mines, Colorado

Rostami, J. , 2016. Performance Prediction of Hard Rock Tunnel Boring Machines (TBMS) in Difficult Ground. Tunnelling and Underground Space Technology, 57: 173–182. https://doi.org/10.1016/j.tust.2016.01.009

Rostami, J. , Farrokh, E. , Laughton, C. , et al. , 2014a. Advance Rate Simulation for Hard Rock TBMS. KSCE Journal of Civil Engineering, 18(3): 837–852. https://doi.org/10.1007/s12205-014-0058-5

Rostami, J. , Ghasemi, A. , Alavi Gharahbagh, E. , et al. , 2014b. Study of Dominant Factors Affecting Cerchar Abrasivity Index. Rock Mechanics and Rock Engineering, 47(5): 1905–1919. https://doi.org/10.1007/s00603-013-0487-3

Sapigni, M. , Berti, M. , Bethaz, E. , et al. , 2002. TBM Performance Estimation Using Rock Mass Classifications. International Journal of Rock Mechanics and Mining Sciences, 39(6): 771–788. https://doi.org/10.1016/s1365-1609(02)00069-2

Şen, Z. , Sadagah, B. H. , 2003. Modified Rock Mass Classification System by Continuous Rating. Engineering Geology, 67(3/4): 269–280. https://doi.org/10.1016/s0013-7952(02)00185-0

Shang, Y. J. , Xue, J. H. , Wang, S. J. , et al. , 2004. A Case History of Tunnel Boring Machine Jamming in an Inter-Layer Shear Zone at the Yellow River Diversion Project in China. Engineering Geology, 71(3/4): 199–211. https://doi.org/10.1016/s0013-7952(03)00134-0

Torabi, S. R. , Shirazi, H. , Hajali, H. , et al. , 2013. Study of the Influence of Geotechnical Parameters on the TBM Performance in Tehran–Shomal Highway Project Using ANN and SPSS. Arabian Journal of Geosciences, 6(4): 1215–1227. https://doi.org/10.1007/s12517-011-0415-3

Valle, N. D. , Fuoco, S. , Brino, G. , 2016. Detailed Tbm Boring Cycle Estimation Using Rock Mass Rating System―Part Ⅰ. In: 2nd International Conference on Tunnel Boring Machines in Difficult Grounds (TBM DiGs Istanbul). TBM DiGs, 1–26

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: [Dissertation]. Colorado School of Mines, Colorado

Yagiz, S. , Frough, O. , Torabi, S. R. , 2013. A Rock Mass Rating System for Predicting TBM Utilization. In: ISRM International Symposium―EUROCK 2013. International Society for Rock Mechanics and Rock Engineering, September 21, 2013, 921–925

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