Skip to main content
SpringerLink
Log in

Data-driven sequential development of geological cross-sections along tunnel trajectory

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

Abstract

Forecasting geological cross-section ahead of tunnel face is an essential ingredient for tunnel design and construction. Geological analysis and drilling have been the most traditional approach for predicting tunnel ahead geological conditions. However, this practice is often subjective, and geological information retrieved from previous tunnel excavation in the same project has not been used quantitatively. In this study, a data-driven framework is proposed to sequentially develop geological cross-sections along planned tunnel trajectory conditioning on site-specific data and prior geological knowledge. The proposed framework dynamically and continuously incorporates geological information revealed from tunnel excavation as additional site-specific data, which provide first-hand direct geological information from the immediate past tunnel sections and can actually serve as the most relevant prior geological knowledge for forward prediction. All the prior geological knowledge is compiled as a site-specific training image database. When the actual geological cross-sections are revealed from tunnel excavation, the training image database is also updated for the next loop of tunnel ahead geological prediction. The proposed method is illustrated using data obtained from a real tunnelling project in Australia. Results indicate that the proposed method continuously provides accurate prediction of geological cross-sections along planned tunnel trajectory with quantified stratigraphic uncertainty.

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

Similar content being viewed by others

References

  1. Chen T, Guestrin C (2016). Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794

  2. Chen J, Li X, Zhu H, Rubin Y (2017) Geostatistical method for inferring RMR ahead of tunnel face excavation using dynamically exposed geological information. Eng Geol 228:214–223

    Article  Google Scholar 

  3. Dearman WR (2013) Engineering geological mapping. Elsevier, Amsterdam

    Google Scholar 

  4. Efron N, Read M (2012) Analysing international tunnel costs. An interactive qualifying project. Worcester Polythecnic Institute

  5. Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using MATLAB. Prentice Hall, Englewood Cliffs

    Google Scholar 

  6. Guan Z, Deng T, Du S, Li B, Jiang Y (2012) Markovian geology prediction approach and its application in mountain tunnels. Tunn Undergr Space Technol 31:61–67

    Article  Google Scholar 

  7. Heim GE (1990) Knowledge of the origin of soil deposits is of primary importance to understanding the nature of the deposit. Bull Assoc Eng Geol 27(1):109–112

    Google Scholar 

  8. Inazaki T, Isahai H, Kawamura S, Kurahashi T, Hayashi H (1999) Stepwise application of horizontal seismic profiling for tunnel prediction ahead of the face. Lead Edge 18(12):1429–1431

    Article  Google Scholar 

  9. Lance GN, Williams WT (1967) A general theory of classificatory sorting strategies: 1. Hierarchical systems. Comput J 9(4):373–380

    Article  Google Scholar 

  10. Leca E, New B (2007) Settlements induced by tunneling in soft ground. Tunn Undergr Space Technol 22(2):119–149

    Article  Google Scholar 

  11. Lei GH, Ng CWW, Rigby DB (2001) Stress and displacement around an elastic artificial rectangular hole. J Eng Mech 127(9):880–890. https://doi.org/10.1061/(ASCE)0733-9399(2001)127:9(880)

    Article  Google Scholar 

  12. Li S, Liu B, Xu X, Nie L, Liu Z, Song J, Sun H, Chen L, Fan K (2017) An overview of ahead geological prospecting in tunneling. Tunn Undergr Space Technol 63:69–94

    Article  Google Scholar 

  13. Mariethoz G, Caers J (2014) Multiple-point geostatistics: stochastic modeling with training images. John Wiley & Sons, London

    Book  Google Scholar 

  14. Martí D, Carbonell R, Flecha I, Palomeras I, Font-Capó J, Vázquez-Suñé E, Pérez-Estaún A (2008) High-resolution seismic characterization in an urban area: subway tunnel construction in barcelona, spain. Geophysics 73(2):B41–B50

    Article  Google Scholar 

  15. Meerschman E, Pirot G, Mariethoz G, Straubhaar J, Van Meirvenne M, Renard P (2013) A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm. Comput Geosci 52:307–324

    Article  Google Scholar 

  16. Membah J, Asa E (2015) Estimating cost for transportation tunnel projects: a systematic literature review. Int J Constr Manag 15(3):196–218

    Google Scholar 

  17. Ng CWW, Shi JW, Hong Y (2013) Three-dimensional centrifuge modelling of basement excavation effects on an existing tunnel in dry sand. Can Geotech J 50(8):874–888. https://doi.org/10.1139/cgj-2012-0423

    Article  Google Scholar 

  18. Oggeri C, Fenoglio TM, Vinai R (2014) Tunnel spoil classification and applicability of lime addition in weak formations for muck reuse. Tunn Undergr Space Technol 44:97–107

    Article  Google Scholar 

  19. Paraskevopoulou C, Boutsis G (2020) Cost overruns in tunnelling projects: Investigating the impact of geological and geotechnical uncertainty using case studies. Infrastructures 5(9):73

    Article  Google Scholar 

  20. Peck RB, Hendron AJ, Mohraz B (1972) State of the art of soft-ground tunneling. In: N Am Rapid Excav & Tunneling Conf Proc, vol 1

  21. Phoon K, Ching J, Shuku T (2021) Challenges in data-driven site characterization. Georisk Assess Manag Risk Eng Syst Geohazards 1–13

  22. Schepers R, Rafat G, Gelbke C, Lehmann B (2001) Application of borehole logging, core imaging and tomography to geotechnical exploration. Int J Rock Mech Min Sci 38(6):867–876

    Article  Google Scholar 

  23. Sheil BB, Suryasentana SK, Mooney MA, Zhu H (2020) Machine learning to inform tunnelling operations: recent advances and future trends. Proc Inst Civ Eng Smart Infrastruct Constr 1–22

  24. Shi C, Wang Y (2021a) Nonparametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics. Can Geotech J 58(2):261–280

    Article  Google Scholar 

  25. Shi C, Wang Y (2021b) Smart determination of borehole number and locations for stability analysis of multi-layered slopes using multiple point statistics and information entropy. Can Geotech J 58(11):1669–1689

    Article  Google Scholar 

  26. Shi C, Wang Y (2021c) Development of subsurface geological cross-section from limited site-specific boreholes and prior geological knowledge using iterative convolution XGBoost. J Geotech Geoenviron Eng 147(9):04021082

    Article  Google Scholar 

  27. Shi C, Wang Y (2021d) Training image selection for development of subsurface geological cross-section by conditional simulations. Eng Geol 106415

  28. Shi J, Wang Y, Ng CW (2016a) Numerical parametric study of tunneling-induced joint rotation angle in jointed pipelines. Can Geotech J 53(12):2058–2071

    Article  Google Scholar 

  29. Shi J, Wang Y, Ng CW (2016b) Three-dimensional centrifuge modeling of ground and pipeline response to tunnel excavation. J Geotech Geoenviron Eng 142(11):04016054

    Article  Google Scholar 

  30. Shi J, Wei J, Ng CW, Lu H, Ma S, Shi C, Li P (2022) Effects of construction sequence of double basement excavations on an existing floating pile. Tunn Undergr Space Technol 119:104230

    Article  Google Scholar 

  31. Sun W, Shi M, Zhang C, Zhao J, Song X (2018) Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data. Autom Constr 92:23–34

    Article  Google Scholar 

  32. Van Vliet LJ, Young IT, Beckers GL (1989) A nonlinear laplace operator as edge detector in noisy images. Comput Vis Graph Image Process 45(2):167–195

    Article  Google Scholar 

  33. Wang Y, Shi C, Li X (2022) Machine learning of geological details from borehole logs for development of high-resolution subsurface geological cross-section and geotechnical analysis. Georisk Assess Manage Risk Eng Systems and Geohazards 16(1):2–20

    Google Scholar 

  34. Yamamoto T, Shirasagi S, Yamamoto S, Mito Y, Aoki K (2003) Evaluation of the geological condition ahead of the tunnel face by geostatistical techniques using TBM driving data. Tunn Undergr Space Technol 18(2–3):213–221

    Article  Google Scholar 

  35. Zhang Q, Liu Z, Tan J (2019) Prediction of geological conditions for a tunnel boring machine using big operational data. Autom Constr 100:73–83

    Article  Google Scholar 

Download references

Acknowledgements

The work described in this paper was supported by a grant from the Innovation and Technology Commission of Hong Kong Special Administrative region (Project No: MHP/099/21 ), a grant from National Natural Science Foundation of China (Project No: 52130805), and a grant from Shenzhen Science and Technology Innovation Commission (Shenzhen-Hong Kong-Macau Science and Technology Project (Category C) No: SGDX20210823104002020), China. The financial support is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Architecture and Civil Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China

    Chao Shi & Yu Wang

Authors
  1. Chao Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Wang.

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 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

Shi, C., Wang, Y. Data-driven sequential development of geological cross-sections along tunnel trajectory. Acta Geotech. 18, 1739–1754 (2023). https://doi.org/10.1007/s11440-022-01707-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11440-022-01707-1

Keywords

Search

Navigation