Geomechanics and Engineering

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Prediction models of rock quality designation during TBM tunnel construction using machine learning algorithms

  • Byeonghyun Hwang (School of Civil, Environmental and Architectural Civil Engineering, Korea University) ;
  • Hangseok Choi (School of Civil, Environmental and Architectural Civil Engineering, Korea University) ;
  • Kibeom Kwon (School of Civil, Environmental and Architectural Civil Engineering, Korea University) ;
  • Young Jin Shin (R&D division, Hyundai Engineering & Construction) ;
  • Minkyu Kang (Center for Defense Resource Management, Korea Institute for Defense Analyses)
  • Received : 2023.11.23
  • Accepted : 2024.02.04
  • Published : 2024.09.10
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Abstract

An accurate estimation of the geotechnical parameters in front of tunnel faces is crucial for the safe construction of underground infrastructure using tunnel boring machines (TBMs). This study was aimed at developing a data-driven model for predicting the rock quality designation (RQD) of the ground formation ahead of tunnel faces. The dataset used for the machine learning (ML) model comprises seven geological and mechanical features and 564 RQD values, obtained from an earth pressure balance (EPB) shield TBM tunneling project beneath the Han River in the Republic of Korea. Four ML algorithms were employed in developing the RQD prediction model: k-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). The grid search and five-fold cross-validation techniques were applied to optimize the prediction performance of the developed model by identifying the optimal hyperparameter combinations. The prediction results revealed that the RF algorithm-based model exhibited superior performance, achieving a root mean square error of 7.38% and coefficient of determination of 0.81. In addition, the Shapley additive explanations (SHAP) approach was adopted to determine the most relevant features, thereby enhancing the interpretability and reliability of the developed model with the RF algorithm. It was concluded that the developed model can successfully predict the RQD of the ground formation ahead of tunnel faces, contributing to safe and efficient tunnel excavation.

Keywords

Acknowledgement

This research was conducted with the support of the "National R&D Project for Smart Construction Technology (No. RS-2020-KA157074)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

References

  1. Arumugam, K., Naved, M., Shinde, P.P., Leiva-Chauca, O., Huaman-Osorio, A. and Gonzales-Yanac, T. (2023), "Multiple disease prediction using machine learning algorithms", Materials Today: Proceedings, 80, 3682-3685. https://doi.org/10.1016/j.matpr.2021.07.361.
  2. Broere, W. (2016), "Urban underground space: Solving the problems of today's cities", Tunn. Undergr. Sp. Tech., 55, 245-248. https://doi.org/10.1016/j.tust.2015.11.012.
  3. Carriere, S.D., Chalikakis, K., Senechal, G., Danquigny, C. and Emblanch, C. (2013), "Combining electrical resistivity tomography and ground penetrating radar to study geological structuring of karst unsaturated zone", J. Appl.Geophys., 94, 31-41. https://doi.org/10.1016/j.jappgeo.2013.03.014.
  4. Eftekhari, A., Aalianvari, A. and Rostami, J. (2018), "Influence of TBM operational parameters on optimized penetration rate in schistose rocks, a case study: Golab tunnel Lot-1, Iran", Comput. Concrete, 22(2), 239-248. https://doi.org/10.12989/cac.2018.22.2.239.
  5. Farrokh, E. and Rostami, J. (2009), "Effect of adverse geological condition on TBM operation in Ghomroud tunnel conveyance project", Tunn. Undergr. Sp. Tech., 24(4), 436-446. https://doi.org/10.1016/j.tust.2008.12.006.
  6. Grodner, M. (2001), "Delineation of rockburst fractures with ground penetrating radar in the Witwatersrand Basin, South Africa", Int. J. Rock Mech. Min. Sci., 38(6), 885-891. https://doi.org/10.1016/S1365-1609(01)00054-5.
  7. Kafy, A.A., Bakshi, A., Saha, M., Al Faisal, A., Almulhim, A.I., Rahaman, Z.A. and Mohammad, P. (2023). "Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms", Sci. Total Environ., 867, 161394. https://doi.org/10.1016/j.scitotenv.2023.161394.
  8. Kang, M., Kim, S., Lee, J. and Choi, H. (2022), "FE model of electrical resistivity survey for mixed ground prediction ahead of a TBM tunnel face", Geomech. Eng., 29(3), 301-310. https://doi.org/10.12989/gae.2022.29.3.301.
  9. Kang, M., Lee, J., Kwon, K., Park, S. and Choi, H. (2023), "Laboratory simulations on hybrid non-destructive survey of electrical resistivity and induced polarization to predict geological risks ahead of a TBM tunnel", Tunn. Undergr. Sp. Tech., 135, 105066. https://doi.org/10.1016/j.tust.2023.105066.
  10. Kaus, A. and Boening, W. (2008), "BEAM-Geoelectrical Ahead Monitoring for TBM-Drives", Geomechanik und Tunnelbau: Geomechanik und Tunnelbau, 1(5), 442-449. https://doi.org/10.1002/geot.200800048.
  11. Kim, D., Pham, K., Park, S., Oh, J.Y. and Choi, H. (2020), "Determination of effective parameters on surface settlement during shield TBM", Geomech. Eng., 21(2), 153-164. https://doi.org/10.12989/gae.2020.21.2.153.
  12. Kim, K.Y., Jo, S.A., Ryu, H.H. and Cho, G.C. (2020), "Prediction of TBM performance based on specific energy", Geomech. Eng., 21(6), 489-496. https://doi.org/10.12989/gae.2020.22.6.489.
  13. Kwon, K., Kang, M., Kim, D. and Choi, H. (2023), "Prioritization of hazardous zones using an advanced risk management model combining the analytic hierarchy process and fuzzy set theory", Sustainability, 15(15), 12018. https://doi.org/10.3390/su151512018.
  14. Lee, H. L., Song, K. I., Qi, C. and Kim, K.Y. (2022). "Sequential prediction of TBM penetration rate using a gradient boosted regression tree during tunneling", Geomech. Eng., 29(5), 523-533. https://doi.org/10.12989/gae.2022.29.5.523.
  15. Lee, K.H., Park, J.H., Park, J., Lee, I.M. and Lee, S.W. (2019), "Electrical resistivity tomography survey for prediction of anomaly in mechanized tunneling", Geomech. Eng., 19(1), 93-104. https://doi.org/10.12989/gae.2019.19.1.093.
  16. Liu, B., Wang, R., Guan, Z., Li, J., Xu, Z., Guo, X. and Wang, Y. (2019), "Improved support vector regression models for predicting rock mass parameters using tunnel boring machine driving data", Tunn. Undergr. Sp. Tech., 91, 102958. https://doi.org/10.1016/j.tust.2019.04.014.
  17. Li, J.B., Chen, Z.Y., Li, X., Jing, L.J., Zhangf, Y.P., Xiao, H.H.. and Fan, L.T. (2023), "Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods", Undergr. Sp., https://doi.org/10.1016/j.undsp.2023.01.001.
  18. Liu, Q., Wang, X., Huang, X. and Yin, X. (2020), "Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data", Tunn. Undergr. Sp. Tech., 106, 103595. https://doi.org/10.1016/j.tust.2020.103595.
  19. Lundberg, S.M. and Lee, S.I. (2017), "A unified approach to interpreting model predictions", Adv. Neural Inform. Process. Systems, 30.
  20. Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Abdulhamid, S.N., Salim, S.G., Ali, H.F.H. and Majeed, M.K. (2021), "Artificial intelligence forecasting models of uniaxial compressive strength", Transport. Geotech., 27, 100499. https://doi.org/10.1016/j.trgeo.2020.100499.
  21. Mahmoodzadeh, A., Nejati, H.R., Ibrahim, H.H., Ali, H.F.H., Mohammed, A.H., Rashidi, S. and Majeed, M.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.
  22. McDowell, P.W., Barker, R.D., Butcher, A.P., Culshaw, M.G., Jackson, P.D., McCann, D.M. and Arthur, J.C.R. (2002), "Geophysics in engineering investigations", Construction Industry Research and Information Association (CIRIA) and Geological Society Engineering Group Working Party Report. Geological Society, London, Engineering Geology Special Publications, 19.
  23. Mendez, M., Merayo, M.G. and Nunez, M. (2023), "Machine learning algorithms to forecast air quality: a survey", Artif. Intell. Rev., 1-36. https://doi.org/10.1007/s10462-023-10424-4.
  24. Pallathadka, H., Wenda, A., Ramirez-Asis, E., Asis-Lopez, M., Flores-Albornoz, J. and Phasinam, K. (2023), "Classification and prediction of student performance data using various machine learning algorithms", Materials today: Proceedings, 80, 3782-3785. https://doi.org/10.1016/j.matpr.2021.07.382.
  25. 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.
  26. Rezaei, A.H., Shirzehhagh, M. and Golpasand, M.R.B. (2019), "EPB tunneling in cohesionless soils: A study on Tabriz Metro settlements", Geomech. Eng., 19(2), 153-165. https://doi.org/10.12989/gae.2019.19.2.153.
  27. Shapley, L.S. (1953). "A value for n-person games", Contrib. Theory Games, 2(28), 307-317. https://doi.org/10.1515/9781400881970-018.
  28. Sebbeh-Newton, S., Ayawah, P.E., Azure, J.W., Kaba, A.G., Ahmad, F., Zainol, Z. and Zabidi, H. (2021), "Towards TBM automation: on-the-fly characterization and classification of ground conditions ahead of a TBM using data-driven approach", Appl. Sci., 11(3), 1060. https://doi.org/10.3390/app11031060.
  29. Sharafat, A., Latif, K. and Seo, J. (2021), "Risk analysis of TBM tunneling projects based on generic bow-tie risk analysis approach in difficult ground conditions", Tunn. Undergr. Sp. Tech., 111, 103860. https://doi.org/10.1016/j.tust.2021.103860.
  30. Wen, S., Zhang, C. and Zhang, Y. (2019), "Favorable driving direction of double shield TBM in deep mixed rock strata: Numerical investigations to reduce shield entrapment", Geomech. Eng., 17(3), 237-245. https://doi.org/10.12989/gae.2019.17.3.237.
  31. Xu, Z.H., Yu, T.F., Lin, P., Wang, W.Y. and Shao, R.Q. (2022), "Integrated geochemical, mineralogical, and microstructural identification of faults in tunnels and its application to TBM jamming analysis", Tunn. Undergr. Sp. Tech., 128, 104650. https://doi.org/10.1016/j.tust.2022.104650.
  32. Yoon, Y., Choi, H., Kwon, K., Hwang, B. and Kang, M. (2023), "Optimization of electrical resistivity survey utilizing modified harmony search algorithm to predict anomalous zone ahead of tunnel faces", Measurement, 223, 113747. https://doi.org/10.1016/j.measurement.2023.113747.
  33. Zhao, J., Gong, Q.M. and Eisensten, Z. (2007), "Tunnelling through a frequently changing and mixed ground: a case history in Singapore", Tunn. Undergr. Sp. Tech., 22(4), 388-400. https://doi.org/10.1016/j.tust.2006.10.002.
  34. Zhao, K., Janutolo, M., Barla, G. and Chen, G. (2014), "3D simulation of TBM excavation in brittle rock associated with fault zones: The Brenner Exploratory Tunnel case", Eng. Geol., 181, 93-111. https://doi.org/10.1016/j.enggeo.2014.07.002.