Geomechanics and Engineering

  • 제37권3호
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  • Pages.223-241
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  • 2024
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Computing machinery techniques for performance prediction of TBM using rock geomechanical data in sedimentary and volcanic formations

  • Hanan Samadi (IRO, Civil Engineering Department, University of Halabja) ;
  • Arsalan Mahmoodzadeh (IRO, Civil Engineering Department, University of Halabja) ;
  • Shtwai Alsubai (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Abdullah Alqahtani (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Abed Alanazi (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Ahmed Babeker Elhag (Department of Civil Engineering, College of Engineering, King Khalid University)
  • 투고 : 2023.05.19
  • 심사 : 2024.04.11
  • 발행 : 2024.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R2 = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

키워드

과제정보

This study is supported via funding from Prince Satam bin Abdulaziz University project number (PSAU/2024/R/1445). The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large Groups RGP. 2/357/44.

참고문헌

  1. Adoko, A.C., Gokceoglu, C. and Yagiz, S. (2017), "Bayesian prediction of TBM penetration rate in rock mass", Eng.Geol., 226, 245-256. https://doi.org/10.1016/j.enggeo.2017.06.014.
  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. Carter, T.G. and Marinos, V. (2020), "Putting geological focus back into rock engineering design", Rock Mech. Rock Eng., 53(10), 4487-4508. https://doi.org/10.1007/s00603-020-02177-1.
  4. Cho, K., Merrienboer, B. van, Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. and Bengio, Y. (2014), "Learning phrase representations using RNN encoder-decoder for statistical machine translation", Computation and Language. https://doi.org/10.48550/arXiv.1406.1078.
  5. Elhaik, E. (2022), "Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated", Scientific Reports, 12(1), 14683. https://doi.org/10.1038/s41598-022-14395-4.
  6. Elmo, D. and Stead, D. (2021), "The role of behavioural factors and cognitive biases in rock engineering", Rock Mech. Rock Eng., 54(5), 2109-2128. https://doi.org/10.1007/s00603-021-02385-3.
  7. Farrokh, E., Rostami, J. and Laughton, C. (2012), "Study of various models for estimation of penetration rate of hard rock TBMs", Tunn. Undergr. Sp. Tech., 30, 110-123. https://doi.org/10.1016/j.tust.2012.02.012.
  8. Gokceoglu, C. (2022), "Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey", SN Appl. Sci., 4(1), 19. https://doi.org/10.1007/s42452-021-04903-y.
  9. 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.
  10. He, H., Wang, S., Shen, W. and Zhang, W. (2023), "The influence of pipe-jacking tunneling on deformation of existing tunnels in soft soils and the effectiveness of protection measures", Transport. Geotech., 42, 101061. https://doi.org/10.1016/j.trgeo.2023.101061.
  11. Hu, D., Li, Y., Yang, X., Liang, X., Zhang, K. and Liang, X. (2023), "Experiment and application of NATM tunnel deformation monitoring based on 3D laser scanning", Struct. Control Health Monit., 2023, 1-13. https://doi.org/10.1155/2023/3341788.
  12. Jolliffe, I.T. and Cadima, J. (2016), "Principal component analysis: a review and recent developments", Philos. T. Roy. Soc. A: Math. Phys. Eng. Sci., 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202.
  13. Jung, J.H., Chung, H., Kwon, Y.S. and Lee, I.M. (2019), "An ANN to predict ground condition ahead of tunnel face using TBM operational data", KSCE J. Civil Eng., 23(7), 3200-3206. https://doi.org/10.1007/s12205-019-1460-9.
  14. Kalnins, A. (2022), "When does multicollinearity bias coefficients and cause type 1 errors? A reconciliation of Lindner, Puck, and Verbeke (2020) with Kalnins (2018)", J. Int. Business Studies, 53(7), 1536-1548. https://doi.org/10.1057/s41267-022-00531-9.
  15. 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.
  16. Liu, J., Ren, J. and Guo, W. (2015), "Thrust and torque characteristics based on a new cutter-head load model", Chinese J. Mech. Eng., 28(4), 801-809. https://doi.org/10.3901/CJME.2015.0504.066.
  17. Liu, W., Liang, J. and Xu, T. (2023), "Tunnelling-induced ground deformation subjected to the behavior of tail grouting materials", Tunn. Undergr. Sp. Tech., 140, 105253. https://doi.org/10.1016/j.tust.2023.105253.
  18. Mahmoodzadeh, A., Nejati, H.R., Mohammadi, M., Hashim Ibrahim, H., Rashidi, S. and Ahmed Rashid, T. (2022), "Forecasting tunnel boring machine penetration rate using LSTM deep neural network optimized by grey wolf optimization algorithm", Exp. Syst. Appl., 209, 118303. https://doi.org/10.1016/j.eswa.2022.118303.
  19. Shahrour, I. and Zhang, W. (2021), "Use of soft computing techniques for tunneling optimization of tunnel boring machines", Underground Space, 6(3), 233-239. https://doi.org/10.1016/j.undsp.2019.12.001.
  20. Shao, C., Li, X. and Su, H. (2013), Performance prediction of hard rock TBM based on extreme learning machine, 409-416. https://doi.org/10.1007/978-3-642-40849-6_40.
  21. Shi, M.L., Lv, L. and Xu, L. (2023a), "A multi-fidelity surrogate model based on extreme support vector regression: fusing different fidelity data for engineering design", Eng. Comput., 40(2), 473-493. https://doi.org/10.1108/EC-10-2021-0583.
  22. Shi, M., Hu, W., Li, M., Zhang, J., Song, X. and Sun, W. (2023b), "Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine", Mech. Syst. Signal Pr., 188, 110022. https://doi.org/10.1016/j.ymssp.2022.110022.
  23. Su, Y., Wang, J., Li, D., Wang, X., Hu, L., Yao, Y. and Kang, Y. (2023), "End-to-end deep learning model for underground utilities localization using GPR", Automat. Constr., 149, 104776. https://doi.org/10.1016/j.autcon.2023.104776.
  24. Wang, X., Lu, H., Wei, X., Wei, G., Behbahani, S.S. and Iseley, T. (2020), "Application of artificial neural network in yunnel engineering: A systematic review", IEEE Access, 8, 119527-119543. https://doi.org/10.1109/ACCESS.2020.3004995.
  25. Yan, T., Xu, R., Sun, S.H., Hou, Z.K. and Feng, J.Y. (2024), "A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm", Petroleum Sci., 21(2), 1135-1148. https://doi.org/10.1016/j.petsci.2023.09.011.
  26. Yang, S.Q., Chen, M., Fang, G., Wang, Y.C., Meng, B., Li, Y.H. and Jing, H.W. (2018), "Physical experiment and numerical modelling of tunnel excavation in slanted upper-soft and lower-hard strata", Tunn. Undergr. Sp. Tech., 82, 248-264. https://doi.org/10.1016/j.tust.2018.08.049.
  27. Yin, H., Wu, Q., Yin, S., Dong, S., Dai, Z. and Soltanian, M.R. (2023), "Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest", J. Hydrology, 616, 128813. https://doi.org/10.1016/j.jhydrol.2022.128813.
  28. Zhao, N., Li, D.Q., Gu, S.X. and Du, W. (2024), "Analytical fragility relation for buried cast iron pipelines with lead-caulked joints based on machine learning algorithms", Earthq. Spectra, 40(1), 566-583. https://doi.org/10.1177/87552930231209195.
  29. Zhou, J., Qiu, Y., Armaghani, D.J., Zhang, W., Li, C., Zhu, S. and Tarinejad, R. (2021), "Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques", Geosci. Frontiers, 12(3), 101091. https://doi.org/10.1016/j.gsf.2020.09.020.