SpringerLink
Log in

Intelligent assistant driving method for tunnel boring machine based on big data

  • Review Paper
  • Published:
Acta Geotechnica Aims and scope Submit manuscript

Abstract

During tunnel construction with tunnel boring machine (TBM), the TBM drivers determine the driving parameters depending only on their own experiences. Inappropriate TBM driving parameters may lead to low construction efficiency, severe disk cutter wear, even tunnel collapse. An assisted driving method for TBM is proposed in this study to assist drivers in determining suitable driving parameters in advance taking into account both the construction safety and efficiency. The proposed method starts with develo** a model to classify the grade of surrounding rock masses and a deep learning model to predict the TBM tunneling parameters (i.e., torque and thrust). Then, the models are used to predict the integrity and drivability of the surrounding rock in the tunneling. Finally, appropriate driving parameters for TBM in different rock grades and drivability classes can be determined automatically. This assisted driving method was examined by the data from Yin-song project in China. Essentially, this study can be helpful for the evaluation of rock mass drivability and the determination of driving parameters, paving the way to a self-driving machine in the harsh tunnel boring environment.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

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

Similar content being viewed by others

References

  1. Alber M (2000) Advance rates of hard rock TBMs and their effects on project economics. Tunn Undergr Space Technol 15(1):55–64

    Article  Google Scholar 

  2. Bastian A, Sujadi H, Febrianto DG (2018) Penerapan algoritma K-means clustering analysis pada aenyakit menular manusia (Studi Kasus Kabupaten Majalengka). Jurnal Sistem Informasi 14(1):28–34

    Google Scholar 

  3. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  4. Bruland A (1998) Hard rock tunnel boring. D.E. thesis, Norwegian University of Science and Technology

  5. Chen K, Sun ZC, Li T (2018) TBM design and construction. China Communications Press Co. Ltd, Bei**g

    Google Scholar 

  6. Chen ZY, Zhang YP, Li JB et al (2021) Diagnosing tunnel collapse sections based on TBM tunneling big data and deep learning: a case study on the Yinsong Project, China. Tunnel Undergr Space Technol 108:103700

    Article  Google Scholar 

  7. 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 43:427–455

    Article  Google Scholar 

  8. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  9. **g LJ, Li JB, Yang C et al (2019) A case study of TBM performance prediction using field tunnelling tests in limestone strata. Tunn Undergr Space Technol 83:364–372

    Article  Google Scholar 

  10. Li JH, Li PX, Guo D et al (2021) Advanced prediction of tunnel boring machine performance based on big data. Geosci Front 12(1):331–338

    Article  Google Scholar 

  11. Liu ZQ, Guo D, Lacasse S et al (2020) Algorithms for intelligent prediction of landslide displacements. J Zhejiang Univ Sci A 21:412–429

    Article  Google Scholar 

  12. Ozdemir L (1977) Development of theoretical equations for predicting tunnel boring ability. D.E. thesis, Colorado School of Mines

  13. Sandri M, Zuccolotto P (2006) Variable selection using random forests. In: Zani S, Cerioli A, Riani M, Vichi M (eds) Data analysis, classification and the forward search. Studies in classification, data analysis, and knowledge organization. Springer, Berlin

    Google Scholar 

  14. Sanio HP (1985) Prediction of the performance of disc cutters in anisotropic rock. Int J Rock Mech Min Sci Geomech Abstr 22(3):153–161

    Article  Google Scholar 

  15. Sapigni M, Berti M, Bethaz E et al (2002) TBM performance estimation using rock mass classifications. Int J Rock Mech Min Sci 39(6):771–788

    Article  Google Scholar 

  16. 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. D.E. thesis, Colorado School of Mines

  17. Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Space Technol 23(3):326–339

    Article  Google Scholar 

  18. Zhang WG, Zhang RH, Wu CZ et al (2020) State-of-the-art review of soft computing applications in underground excavations. Geosci Front 11(4):1095–1106

    Article  Google Scholar 

  19. Zhu CS, Idemudia CW, Feng WF (2019) Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Inform Med Unlocked 17:100179

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Key R&D Program of China (2019YFC1511104), Shenzhen Peacock Technology Innovation Project (KQJSCX20180328165808449) and Shenzhen Key Laboratory Launching Project (ZDSYS20200810113601005). Thanks for the support of China Railway Engineering Equipment Group Corporation for providing the comprehensive database in this study.

Author information

Authors and Affiliations

  1. School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Dong Guo & **hui Li

  2. School of Civil Engineering and Architecture, Nanchang University, Nanchang, China

    Shui-Hua Jiang

  3. School of Civil Engineering, Bei**g Jiaotong University, Bei**g, China

    Xu Li

  4. China Institute of Water Resources and Hydropower Research, Bei**g, China

    Zuyu Chen

Authors
  1. Dong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. **hui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shui-Hua Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zuyu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to **hui Li.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, D., Li, J., Jiang, SH. et al. Intelligent assistant driving method for tunnel boring machine based on big data. Acta Geotech. 17, 1019–1030 (2022). https://doi.org/10.1007/s11440-021-01327-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11440-021-01327-1

Keywords

Search

Navigation