Skip to main content
SpringerLink
Log in

Evaluation of Hard Rock Tunnel Boring Machine (TBM) Performance Using Stochastic Modeling

  • Original Paper
  • Published:
Geotechnical and Geological Engineering Aims and scope Submit manuscript

Abstract

Penetration rate is one of the most critical parameters affecting the performance of TBM machines and it is complicated to predict due to uncertainty in excavating operations and geological parameters. In this paper, using Monte Carlo simulation, the penetration rate is predicted, and the sensitivity of the parameters affecting the penetration rate in the TBM machine is evaluated. For this purpose, a database containing in-situ rock properties, rock mass characteristics, and mechanical parameters related to TBM in the Queens Water transfer tunnel has been used. A mathematical model has been developed using principal component analysis to simulate. The results of evaluating the performance of the developed model showed that the model has acceptable performance (VAF = 84.4%, RMSE = 0.03, NSE = 0.82, and R2 = 0.84) and can be used in the development of Monte Carlo simulations. The simulation results showed that during the excavating route, the probability of achieving a penetration rate higher than 2.4 m/h is 90%. Also, the results of the effect of effective parameters on the penetration rate showed that with increasing parameters such as brittleness index (The most effective with a correlation coefficient of 0.528), angle between weakness plane, and tunnel axis, cutter head power, and cutter head torque, the penetration rate increases. Conversely, increasing parameters such as distance between plane of weakness, specific energy, and thrust force (The lowest effect with a correlation coefficient of − 0.13) of the machine has a negative effect on the penetration rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

Enquiries about data availability should be directed to the authors.

References

  • Acaroglu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn Undergr Space Technol 23:600–608

    Article  Google Scholar 

  • Afradi A, Ebrahimabadi A (2020) Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate. SN Appl Sci 2:1–16

    Article  Google Scholar 

  • Aladejare AE, Akeju VO (2020) Design and sensitivity analysis of rock slope using Monte Carlo simulation. Geotech Geol Eng 38:573–585. https://doi.org/10.1007/S10706-019-01048-Z/FIGURES/9

    Article  Google Scholar 

  • Anele AO, Hamam Y, Abu-Mahfouz AM, Todini E (2017) Overview, comparative assessment and recommendations of forecasting models for short-term water demand prediction. Water 9:887

    Article  Google Scholar 

  • Barton N (1999) TBM performance in rock using QTBM. Tunnels Tunn Int 31:41–48

    Google Scholar 

  • Bolboaca S-D, Jäntschi L (2006) Pearson versus Spearman, Kendall’s tau correlation analysis on structure-activity relationships of biologic active compounds. Leonardo J Sci 5:179–200

    Google Scholar 

  • Bruland A (1998) Prediction model for performance and costs. Nor TBM Tunnelling, Publ, vol 11

  • Cai M (2011) Rock mass characterization and rock property variability considerations for tunnel and cavern design. Rock Mech Rock Eng 44:379–399

    Article  Google Scholar 

  • Cassinelli F (1982) Power consumption and metal wear in tunnel-boring machines: analysis of tunnel-boring operation in hard rock

  • Copur H, Aydin H, Bilgin N et al (2014) Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model. Tunn Undergr Space Technol 42:1–14

    Article  Google Scholar 

  • Farmer IW, Glossop NH (1980) Mechanics of disc cutter penetration. Tunn Tunn (United Kingdom) 12

  • Ghasemi E, Shahriar K, Sharifzadeh M, Hashemolhosseini H (2010) Quantifying the uncertainty of pillar safety factor by Monte Carlo simulation—a case study. Arch Min Sci 55:623–635

    Google Scholar 

  • Ghasemi E, Sari M, Ataei M (2012) Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. Int J Rock Mech Min Sci 52:163–170

    Article  Google Scholar 

  • Ghasemi E, Yagiz S, Ataei M (2014) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bull Eng Geol Environ 73:23–35

    Article  Google Scholar 

  • Gokceoglu C (2022) Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey. SN Appl Sci 4:1–12. https://doi.org/10.1007/S42452-021-04903-Y/FIGURES/10

    Article  Google Scholar 

  • Gong QM, Zhao J (2007) Influence of rock brittleness on TBM penetration rate in Singapore granite. Tunn Undergr Space Technol 22:317–324

    Article  Google Scholar 

  • Gong Q, Zhao J (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Min Sci 46:8–18

    Article  Google Scholar 

  • Gong Q, Xu H, Lu J et al (2022) Rock mass characteristics model for TBM penetration rate prediction—an updated version. Int J Rock Mech Min Sci 149:104993. https://doi.org/10.1016/J.IJRMMS.2021.104993

    Article  Google Scholar 

  • Graham PC (1976) Rock exploration for machine manufacturers

  • Hamidi JK, Shahriar K, Rezai B, Rostami J (2010) Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system. Tunn Undergr Space Technol 25:333–345

    Article  Google Scholar 

  • Han DY, Cao P, Liu J, Zhu JB (2017) An experimental study of dependence of optimum TBM cutter spacing on pre-set penetration depth in sandstone fragmentation. Rock Mech Rock Eng 50:3209–3221

    Article  Google Scholar 

  • Hassanpour J, Rostami J, Khamehchiyan M et al (2010) TBM performance analysis in pyroclastic rocks: a case history of Karaj water conveyance tunnel. Rock Mech Rock Eng 43:427–445

    Article  Google Scholar 

  • Innaurato N, Mancini A, Rondena E, Zaninetti A (1991) Forecasting and effective TBM performances in a rapid excavation of a tunnel in Italy. In: 7th ISRM Congress. OnePetro

  • Jamshidi A (2018) Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis. Model Earth Syst Environ 4:383–394

    Article  Google Scholar 

  • Kadkhodaei MH, Ghasemi E, Sari M (2022) Stochastic assessment of rockburst potential in underground spaces using Monte Carlo simulation. Environ Earth Sci 8118(81):1–15. https://doi.org/10.1007/S12665-022-10561-Z

    Article  Google Scholar 

  • Kherif F, Latypova A (2020) Principal component analysis. Mach Learn Methods Appl Brain Disord. https://doi.org/10.1016/B978-0-12-815739-8.00012-2

    Article  Google Scholar 

  • Kim K, Kim J, Ryu H et al (2020) Estimation method for TBM cutterhead drive design based on full-scale tunneling tests for application in utility tunnels. Appl Sci 10:5187

    Article  Google Scholar 

  • Kong D, Luo Q, Zhang W et al (2022) Reliability analysis approach for railway embankment slopes using response surface method based Monte Carlo simulation. Geotech Geol Eng 40:4529–4538. https://doi.org/10.1007/S10706-022-02168-9/METRICS

    Article  Google Scholar 

  • Liu RX, Kuang J, Gong Q, Hou XL (2003) Principal component regression analysis with SPSS. Comput Methods Programs Biomed 71:141–147

    Article  Google Scholar 

  • Mahdevari S, Shahriar K, Yagiz S, Shirazi MA (2014) A support vector regression model for predicting tunnel boring machine penetration rates. Int J Rock Mech Min Sci 72:214–229

    Article  Google Scholar 

  • Minaeian B, Ahangari K (2013) Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method. Arab J Geosci 6:1925–1931

    Article  Google Scholar 

  • Minh VT, Katushin D, Antonov M, Veinthal R (2017) Regression models and fuzzy logic prediction of TBM penetration rate. Open Eng 7:60–68

    Article  Google Scholar 

  • Mohammadi SD, Torabi-Kaveh M, Bayati M (2015) Prediction of TBM penetration rate using intact and mass rock properties (case study: Zagros long tunnel, Iran). Arab J Geosci 8:3893–3904

    Article  Google Scholar 

  • Oraee K, Khorami MT, Hosseini N (2012) Prediction of the penetration rate of TBM using adaptive neuro fuzzy inference system (ANFIS). In: Proceeding of SME annual meeting and exhibit, pp 297–302

  • Ramezanzadeh A, Rostami J, Kastner R (2005) Influence of rock mass properties on performance of hard rock TBMs. RETC, 27–29

  • 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. Colorado School of Mines Golden

  • Salimi A, Rostami J, Moormann C, Hassanpour J (2022) Introducing tree-based-regression models for prediction of hard rock TBM performance with consideration of rock type. Rock Mech Rock Eng 55:4869–4891. https://doi.org/10.1007/S00603-022-02868-X/TABLES/12

    Article  Google Scholar 

  • Saltelli A (2002) Sensitivity analysis for importance assessment. Risk Anal 22:579–590

    Article  Google Scholar 

  • Sanio HP (1985) Prediction of the performance of disc cutters in anisotropic rock. Int J Rock Mech Min Sci Geomech Abstr 22:153–161

    Article  Google Scholar 

  • Sari M, Ghasemi E, Ataei M (2014) Stochastic modeling approach for the evaluation of backbreak due to blasting operations in open pit mines. Rock Mech Rock Eng 47:771–783

    Article  Google Scholar 

  • Thuro K, Plinninger RJ (2003) Hard rock tunnel boring, cutting, drilling and blasting: rock parameters for excavatability. In: 10th ISRM congress. OnePetro

  • Wang Y, Wang J, Wang R et al (2023) TBM penetration rate prediction ensemble model based on full-scale linear cutting test. Tunn Undergr Space Technol 131:104794. https://doi.org/10.1016/J.TUST.2022.104794

    Article  Google Scholar 

  • Wanner H, Aeberli U (1979) Tunnelling machine performance in jointed rock. In: 4th ISRM congress. OnePetro

  • Yagiz S (2003) 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

  • Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Space Technol 23:326–339

    Article  Google Scholar 

  • Yagiz S (2017) New equations for predicting the field penetration index of tunnel boring machines in fractured rock mass. Arab J Geosci 10:33

    Article  Google Scholar 

  • Yagiz S, Karahan H (2011) Prediction of hard rock TBM penetration rate using particle swarm optimization. Int J Rock Mech Min Sci 48:427–433

    Article  Google Scholar 

  • Yagiz S, Karahan H (2015) Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass. Int J Rock Mech Min Sci 80:308–315

    Article  Google Scholar 

  • Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22:808–814. https://doi.org/10.1016/J.ENGAPPAI.2009.03.007

    Article  Google Scholar 

  • Yoo W, Mayberry R, Bae S et al (2014) A study of effects of multicollinearity in the multivariable analysis. Int J Appl Sci Technol 4:9

    Google Scholar 

  • Zhao J, Gong QM (2006) Rock mechanics and excavation by tunnel boring machine–issues and challenges. In: Rock mechanics in underground construction: (with CD-ROM). World Scientific, pp 83–96

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Mining Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran

    Peyman Jafarshirzad, Ebrahim Ghasemi & Mohammad Hossein Kadkhodaei

  2. School of Mining and Geosciences, Nazarbayev University, Nur-Sultan City, 01000, Kazakhstan

    Saffet Yagiz

Authors
  1. Peyman Jafarshirzad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ebrahim Ghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saffet Yagiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Hossein Kadkhodaei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ebrahim Ghasemi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jafarshirzad, P., Ghasemi, E., Yagiz, S. et al. Evaluation of Hard Rock Tunnel Boring Machine (TBM) Performance Using Stochastic Modeling. Geotech Geol Eng 41, 3513–3529 (2023). https://doi.org/10.1007/s10706-023-02471-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10706-023-02471-z

Keywords

Search

Navigation