DOI QR코드

DOI QR Code

Prediction of EPB tunnelling performance for various grounds in Korea using discrete event simulation

  • Received : 2023.11.20
  • Accepted : 2024.02.20
  • Published : 2024.09.10

Abstract

This study investigates Tunnel Boring Machine (TBM) performance prediction by employing discrete event simulation technique, which is a potential remedy highlighting its stochastic adaptability to the complex nature of TBM tunnelling activities. The new discrete event simulation model using AnyLogic software was developed and validated by comparing its results with actual performance data for Daegok-Sosa railway project that Earth Pressure Balance (EPB) TBM machine was used in Korea. The results showed the successful implementation of predicting TBM performance. However, it necessitates high-quality database establishment including geological formations, machine specifications, and operation settings. Additionally, this paper introduces a novel methodology for daily performance updates during construction, using automated data processing techniques. This approach enables daily updates and predictions for the ongoing projects, offering valuable insights for construction management. Overall, this study underlines the potential of discrete event simulation in predicting TBM performance, its applicability to other tunneling projects, and the importance of continual database expansion for future model enhancements.

Keywords

Acknowledgement

This work was supported by the Institute for Korea Spent Nuclear Fuel(iKSNF) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Trade, Industry and Energy (MOTIE) (No.2021040101003C).

References

  1. AnyLogic Compnay (2023), AnyLogic 8.8, Available from: http://www.anylogic.com
  2. Benemaran, R.S. and Esmaeili-Falak, M. (2023), "Predicting the Young's modulus of frozen sand using machine learning approaches: State-of-the-art review", Geomech. Eng., 34(5), 507-527. https://doi.org/10.12989/gae.2023.34.5.507.
  3. Bruland, A. (1998), "Advance rate and cutter wear, hard rock tunnel boring machine", Ph.D. Dissertation; Norwegian University of Science and Technology, Trondheim, Norway.
  4. Chen, C. and Seo, H. (2023), "Prediction of rock mass class ahead of TBM excavation face by ML and DL algorithms with Bayesian TPE optimization and SHAP feature analysis", Acta Geotechnica, 1-24. https://doi.org /10.1007/s11440-022-01779-z.
  5. Einstein, H.H., Indermitte, C., Sinfield, J., Descoeudres, F.P. and Dudt, J.P. (1999), "Decision Aids for Tunneling", Transp. Res. Rec., 1656, 6-13. https://doi.org/10.3141/1656-02.
  6. Esmaeili-Falak, M. and Benemaran, R.S. (2023), "Ensemble deep learning-based models to predict the resilient modulus of modified base materials subjected to wet-dry cycles", Geomech. Eng., 32(6), 583-600. https://doi.org/10.12989/gae.2023.32.6.583.
  7. Fereidooni, D. and Karimi, Z. (2023), "Predicting rock brittleness indices from simple laboratory test results using some machine learning methods", Geomech. Eng., 34(6), 697-726. https://doi.org/10.12989/gae.2023.34.6.697.
  8. Frough, O., Khetwal, A. and Rostami, J. (2019), "Predicting TBM utilization factor using discrete event simulation models", Tunn. Undergr. Sp. Tech., 87, 91-99. https://doi.org/10.1016/j.tust.2019.01.017.
  9. Ilya, V.G. (2022), Anylogic in three days, (6th Ed.), Chicago: Anylogic.
  10. Khetwal, A., Einstein, H.H. and Rostami, J. (2023), "Combining the CSM2020 discrete event simulation model with Decision Aid in Tunnelling (DAT) to develop a robust approach for the estimation of completion time for TBM tunnels", Tunn. Undergr. Sp. Tech., 138, 105156. https://doi.org/10.1016/j.tust.2023.105156.
  11. Khetwal, A., Rostami, J. and Nelson, P.P. (2022), "Introducing uniform discrete event simulation (CSM2020) for modeling the TBM tunnelling process", Tunn. Undergr. Sp. Tech., 125, 104502. https://doi.org/10.1016/j.tust.2022.104502.
  12. Khetwal, A., Rostami, J., Frough, O. and Nelson, P.P. (2021), "Comparison between discrete event simulation approach and various existing empirically-based models for estimation of TBM utilization", Tunn. Undergr. Sp. Tech., 112, 103883. https://doi.org/ 10.1016/j.tust.2021.103883.
  13. Khetwal, A. (2021), "Predicting tunnel boring machine utilization using discrete event simulation models for hard rocks", Ph.D. Dissertation, Colorado School of Mines, Golden, USA.
  14. Khetwal, A., Rostami, J. and Nelson, P.P. (2020), "Investigating the impact of TBM downtimes on utilization factor based on sensitivity analysis", Tunn. Undergr. Sp. Tech., 106, 103586. https://doi.org/10.1016/j.tust.2020.103586.
  15. Kim, T.H., Kwak, N.S., Kim, T.K., Jung, S. and Ko, T.Y. (2021), "A TBM data-based ground prediction using deep neural network", J. Korean Tunn. Undergr. Sp. Assoc., 23(1), 13-24. https://doi.org/10.9711/KTAJ.2021.23.1.013.
  16. Mahmoodzadeh, A., Alizadeh, S.M.S., Mohammed, A.H., Elhag, A.B., Ibrahim, H.H. and Rashidi, S. (2023), "LSTM algorithm to determine the state of minimum horizontal stress during well logging operation", Geomech. Eng., 34(1), 43-49. https://doi.org/10.12989/gae.2023.34.1.043.
  17. Mahmoodzadeh, A., Taghizadeh, M., Mohammad, A.H., Ibrahim, H.H., Samadi, H., Mohammadi, M. and Rashidi, S. (2022), "Tunnel wall convergence prediction using optimized LSTM deep neural network",Geomech. Eng., 31(6), 546-556. https://doi.org/10.12989/gae.2022.31.6.545.
  18. Rahm, T., Konig, M., Koch, C., Sadri, K. and Thewes, M. (2012), "Process and logistics simulation in mechanized tunnelling", AnyLogic Conference 2012, Berlin.
  19. Rahm, T., Scheffer., M., Thewes, M. and Konig, M. (2016), "Evaluation of disturbances in mechanized tunnelling using process simulation", Comput-Aided. Civ. Inf., 31 174-192. https://doi.org/ doi.org/10.1111/mice.12143.
  20. Rockwell Automation (2022), Arena 16.2, Available from: .
  21. 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. Dissertation., Colorado School of Mines, Golden, USA.
  22. Rostami, J. and Ozdemir, L. (1993), "A new model for performance prediction of hard rock TBMs", Proceedings of the Rapid Excavation and Tunnelling Conference, USA.
  23. Shin,Y.J., Kang, S.W., Ji, S.W., Choi, I.C., Tatzki, T., Lee, J. and Shmelcher, M. (2022), "Major refurbishment of EPB TBM in high abrasive ground under Hangang River", ITA-AITES World Tunnel Congress, Copenhagen.
  24. Shin, Y.J., Kang, S.W., Lee J.W. and Kim, D.Y. (2021), "Challenges of EPB TBM in pressurized mixed grounds under Hangang River: Effect of clogging", Proceedings of the 2021 World Congress on Advances in Structural Engineering and Mechanics, Seoul
  25. Zhang, Q., Hu, W., Liu, Z. and Tan, J. (2020), "TBM performance prediction with Bayesian optimization and automated machine learning", Tunn. Undergr. Sp. Tech., 103, 103493. https://doi.org / 10.1016/j.tust.2020.103493.