Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of tunneling parameters for ultra-large diameter slurry shield TBM in cross-river tunnels based on integrated algorithms

  • Shujun Xu (China Railway 14th Bureau Group Large Shield Engineering Co., Ltd.)
  • Received : 2024.04.01
  • Accepted : 2024.06.28
  • Published : 2024.07.10
⟨ Previous Next ⟩

Abstract

The development of shield-driven cross-river tunnels in China is witnessing a notable shift towards larger diameters, longer distances, and higher water pressures due to the more complex excavation environment. Complex geological formations, such as fault and karst cavities, pose significant construction risks. Real-time adjustment of shield tunneling parameters based on parameter prediction is the key to ensuring the safety and efficiency of shield tunneling. In this study, prediction models for the torque and thrust of the cutter plate of ultra-large diameter slurry shield TBMs is established based on integrated learning algorithms, by analyzing the real data of Heyan Road cross-river tunnel. The influence of geological complexities at the excavation face, substantial burial depth, and high water level on the slurry shield tunneling parameters are considered in the models. The results reveal that the predictive models established by applying Random Forest and AdaBoost algorithms exhibit strong agreement with actual data, which indicates that the good adaptability and predictive accuracy of these two models. The models proposed in this study can be applied in the real-time prediction and adaptive adjustment of the tunneling parameters for shield tunneling under complex geological conditions.

Keywords

References

  1. Armaghani, D.J., Koopialipoor, M., Marto, A. and Yagiz, S. (2019), "Application of several optimization techniques for estimating TBM advance rate in granitic rocks", J. Rock Mech. Geotech. Eng., 11(4), 779-789. https://doi.org/10.1016/j.jrmge.2019.01.002.
  2. Ates, U., Bilgin, N. and Copur, H. (2014), "Estimating torque, thrust and other design parameters of different type TBMs with some criticism to TBMs used in Turkish tunneling projects", Tunn. Undergr. Sp. Tech., 40, 46-63. https://doi.org/10.1016/j.tust.2013. 09.004.
  3. Bai, X., Cheng, W., Ong Dominic, E.L. and Li, G. (2021), "Evaluation of geological conditions and clogging of tunneling using machine learning", Geomech. Eng., 25(1), 59-73. https://doi.org/10.12989/gae.2021.25.1.059.
  4. Benardos, A.G. and Kaliampakos, D.C. (2004), "Modelling TBM performance with artificial neural networks", Tunn. Undergr. Sp. Tech., 19(6), 597-605. https://doi.org/10.1016/j.tust.2004.02.128.
  5. Breiman, L. (2001), "Random Forests", Machine Learning, 45(1), 5-32. https://doi.org/10.1023/a:1010933404324.
  6. Bruland, A. (2000), Hard rock tunnel boring, Ph.D. Dissertation; Norwegian University of Science and Technology, Trondheim, Norway.
  7. Chen, R.P., Song, X., Meng, F.Y., Wu, H.N. and Lin, X.T. (2022), "Analytical approach to predict tunneling-induced subsurface settlement in sand considering soil arching effect", Comput. Geotech., 141, 104492. https://doi.org/10.1016/j.compgeo.2021.104492.
  8. Engineers, J.S.O.C. (2007), Standard Specifications for Tunneling: Shield Tunnel, Japan Society of Civil Engineers, Tokyo, Japan.
  9. Gonzalez, C., Arroyo, M. and Gens, A. (2016), "Thrust and torque components on mixed-face EPB drives", Tunn. Undergr. Sp. Tech., 57(8), 47-54. https://doi.org/10.1016/j.tust.2016.01.037.
  10. Hassanpour, J., Rostami, J., Khamehchiyan, M. and Bruland, A. (2009), "Developing new equations for TBM performance prediction in carbonate-argillaceous rocks: a case history of Nowsood water conveyance tunnel", Geomech. Geoeng., 4(4), 287-297. https://doi.org/10.1080/17486020903174303.
  11. Hassanpour, J., Rostami, J. and Zhao, J. (2011), "A new hard rock TBM performance prediction model for project planning", Tunn. Undergr. Sp. Tech., 26(5), 595-603. https://doi.org/10.1016/ j.tust.2011.04.004.
  12. Huang, X., Zhang, Q., Liu, Q., Liu, X., Liu, B., Wang, J. and Yin, X. (2022), "A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm", J. Rock Mech. Geotech. Eng., 247272490. https://doi.org/10.1016/j.jrmge. 2021. 11. 008.
  13. Krzywinski, M., and Altman, N. (2017), "Classification and regression trees", Nature Methods, 14, 757-758. https://doi.org/10.1038/nmeth.4370.
  14. Kim, D. (2021), "Large deformation finite element analyses in TBM tunnel excavation: CEL and auto-remeshing approach", Tunn. Undergr. Sp. Tech., 116(10), 104081. https://doi.org/10.1016/ j.tust.2021.104081.
  15. Koopialipoor, M., Fahimifar, A., Ghaleini, E.N., Momenzadeh, M. and Armaghani, D.J. (2020), "Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance", Eng. with Comput., 36(1), 345-357. https://doi.org/10.1007/s00366-019-00701-8.
  16. Lu, J., Gong, Q., Yin, L. and Zhou, X. (2021), "Study on the tunneling response of TBM in stressed granite rock mass in Yinhan Water Conveyance tunnel", Tunn. Undergr. Sp. Tech., 118(3), 104197. https://doi.org/10.1016/j.tust.2021.104197.
  17. Mahmoodzadeh, A., Nejati Hamid, R., Ibrahim Hawkar, H., Ali Hunar Farid, H., Mohammed Adil, H., Rashidi, S. and Majeed Mohammed, K. (2022), "Several models for tunnel boring machine performance prediction based on machine learning", Geomech. Eng., 30(1), 75-91. https://doi.org/10.12989/gae.2022.30.1.075.
  18. Meng, F.Y., Chen, R.P., Xu, Y., Wu, K., Wu, H.N. and Liu, Y. (2022), "Contributions to responses of existing tunnel subjected to nearby excavation: a review", Tunn. Undergr. Sp. Tech., 119, 104195. https://doi.org/ 10.1016/j.tust.2021.104195.
  19. Meng, F.Y., Chen, R.P., Liu, S.L. and Wu, H.N. (2021), "Centrifuge modeling of ground and tunnel responses to nearby excavation in soft soil", J. Geotech. Geoenviron. Eng., 147(3), 04020178. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002473.
  20. Ng, C.W.W., Shi, J.W. and 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.
  21. Pourhashemi, S.M., Ahangari, K., Hassanpour, J. and Eftekhari, S.M. (2022), "TBM performance analysis in very strong and massive rocks; case study: Kerman water conveyance tunnel project, Iran", Geomech. Geoeng., 17(4), 1110-1122. https://doi.org/ 10.1080/17486025.2021.1912410.
  22. Qin, C., Shi, G., Tao, J., Yu, H., Jin, Y., Lei, J. and Liu, C. (2021), "Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network", Mech. Syst. Signal Pr., 151(4), 107386. https://doi.org/10.1016/j.ymssp.2020. 107386.
  23. Qin, C., Shi, G., Tao, J., Yu, H., Jin, Y., Xiao, D., Zhang, Z. and Liu, C. (2022), "An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine", Mech. Syst. Signal Pr., 175(8), 109148. https://doi.org/ 10.1016/j.ymssp.2022.109148.
  24. Ring, B. and Comulada, M. (2018), "Practical numerical simulation of the effect of TBM process pressures on soil displacements through 3D shift iteration", Undergr. Sp., 3(4), 297-309. https://doi.org/10.1016/j.undsp. 2018.09.003.
  25. Salimi, A., Rostami, J., Moormann, C. and Hassanpour, J. (2018), "Examining feasibility of developing a rock mass classification for hard rock TBM application using non-linear regression, regression tree and generic programming", Geotech. Geol. Eng., 36(2), 1145-1159. https://doi.org/10.1007/s10706-017-0380-z.
  26. Shi, J.W., Chen, Y.H., Kong, G.Q., Lu, H., Chen, G. and Shi C. (2024), "Deformation mechanisms of an existing pipeline due to progressively passive instability of tunnel face: physical and numerical investigations", Tunn. Undergr. Sp. Tech., 150, 105822. https://doi.org/10.1016/j.tust.2024.105822.
  27. Shi, J.W., Wang, J.P., Chen Y.H., Shi, C., Lu, H., Ma, S.K. and Fan, Y.B. (2023), "Physical modeling of the influence of tunnel active face instability on existing pipelines", Tunn. Undergr. Sp. Tech., 140, 105281. https://doi.org/ 10.1016/j.tust.2023.105281.
  28. Shi, J.W., Chen, Y.H., Lu, H., Ma, S.K. and Ng, C.W.W. (2022), "Centrifuge modeling of the influence of joint stiffness on pipeline response to underneath tunnel excavation", Can. Geotech. J., 59(9), 1568-1586. https://doi.org/10.1139/cgj-2020-03601.
  29. Shi, J., Fu, Z.Z. and Guo, W.L. (2019), "Investigation of geometric effects on three-dimensional tunnel deformation mechanisms due to basement excavation", Comput. Geotech., 106, 108-116. https://doi.org/ 10.1016/j.compgeo.2018.10.019.
  30. Shi, J., Zhang, X., Chen, L. and Chen, L. (2017), "Numerical investigation of pipeline responses to tunneling-induced ground settlements in clay", Soil Mech. Found. Eng., 54, 303-309. https://doi.org/10.1007/s11204-017-9473-1.
  31. Shi, C. and Wang, Y. (2021), "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. https://doi.org/10.1061/(ASCE)GT. 1943-5606. 0002583.
  32. Song, X., Shi, M., Wu, J. and Sun, W. (2019), "A new fuzzy c-means clustering-based time series segmentation approach and its application on tunnel boring machine analysis", Mech. Syst. Signal Pr., 133(11), 106279. https://doi.org/10.1016/j.ymssp.2019. 106279.
  33. Wan, S. and Yang, H. (2013), "Comparison among methods of ensemble learning" Proceedings of the 2013 International Symposium on Biometrics and Security Technologies, Chengdu, China, July.
  34. Wang, W. and Sun, D. (2021), "The improved AdaBoost algorithms for imbalanced data classification", Inform. Sci., 563(7), 358-374. https://doi.org/10.1016/j.ins.2021.03.042.
  35. Xu, D., Wang, Y., Huang, J., Liu, S., Xu, S. and Zhou, K. (2023), "Prediction of geology condition for slurry pressure balanced shield tunnel with super-large diameter by machine learning algorithms", Tunn. Undergr. Sp. Tech., 131(1), 104852. https://doi.org/10.1016/j.tust.2022.104852.
  36. Zare Naghadehi, M., Samaei, M., Ranjbarnia, M. and Nourani, V. (2018), "State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming", Measurement, 126, 46-57. https://doi.org/10.1016/j.measurement.2018. 05.049.
  37. Zhao, Z., Gong, Q., Zhang, Y. and Zhao, J. (2007), "Prediction model of tunnel boring machine performance by ensemble neural networks", Geomech. Geoeng., 2(2), 123-128. https://doi.org/10.1080/ 17486020701377140.