Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Forecasting tunnel path geology using Gaussian process regression

  • Mahmoodzadeh, Arsalan (Department of Civil Engineering, University of Halabja) ;
  • Mohammadi, Mokhtar (Department of Information Technology, College of Engineering and Computer Science, Lebanese French University) ;
  • Abdulhamid, Sazan Nariman (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Ali, Hunar Farid Hama (Department of Civil Engineering, University of Halabja) ;
  • Ibrahim, Hawkar Hashim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Rashidi, Shima (Department of Computer Science, College of Science and Technology, University of Human Development)
  • Received : 2021.07.02
  • Accepted : 2021.12.08
  • Published : 2022.02.25
⟨ Previous Next ⟩

Abstract

Geology conditions are crucial in decision-making during the planning and design phase of a tunnel project. Estimation of the geology conditions of road tunnels is subject to significant uncertainties. In this work, the effectiveness of a novel regression method in estimating geological or geotechnical parameters of road tunnel projects was explored. This method, called Gaussian process regression (GPR), formulates the learning of the regressor within a Bayesian framework. The GPR model was trained with data of old tunnel projects. To verify its feasibility, the GPR technique was applied to a road tunnel to predict the state of three geological/geomechanical parameters of Rock Mass Rating (RMR), Rock Structure Rating (RSR) and Q-value. Finally, in order to validate the GPR approach, the forecasted results were compared to the field-observed results. From this comparison, it was concluded that, the GPR is presented very good predictions. The R-squared values between the predicted results of the GPR vs. field-observed results for the RMR, RSR and Q-value were obtained equal to 0.8581, 0.8148 and 0.8788, respectively.

Keywords

References

  1. Ayat, H., Kellouche, Y., Ghrici, M. and Boukhatem, B. (2018), "Compressive strength prediction of limestone filler concrete using artificial neural networks", Adv. Comput. Design, 3(3), 289-302. https://doi.org/10.12989/acd.2018.3.3.289.
  2. Barton, N., Loset, F., Lien, R. and Lunde, J. (1980), "Application of the Q-system in design decisions", (Ed., Bergman, M.), Subsurface space, Volume 2: New York Pergamon.
  3. Bieniawski, Z.T. (1976), Rock mass classification in rock engineering, in exploration for rock engineering, 1, A.A. Balkema, Cape town.
  4. Bieniawski, Z.T. (1979), "The geomechanics classification in rock engineering applications", Proceedings of the 4th International Congress on Rock Mechanics.
  5. Bai, X.D., Cheng, W.C., Ong, D.E.L. and Ge, L. (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.
  6. Chore, H.S. and Magar, R.B. (2017), "Prediction of unconfined compressive and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes using multiple linear regression and artificial neural network", Adv. Comput. Design, 2(3), 225-240. https://doi.org/10.12989/acd.2017.2.3.225.
  7. Cavaleri, L., Chatzarakis, G.E., Trapani, F.D., Douvika, M.G., Roinos, K., Vaxevanidis N.M. and Asteris, P.G. (2017), "Modeling of surface roughness in electro-discharge machining using artificial neural networks", Adv. Mater. Res., 6(2), 169-184. https://doi.org/10.12989/amr.2017.6.2.169.
  8. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  9. Guan, Z., Deng, T., Du, S., Li, B. and Jiang, Y. (2012), "Markovian geology prediction approach and its application in mountain tunnels", Tunn. Undergr. Sp. Tech., 31, 61-67. https://doi.org/10.1016/j.tust.2012.04.007.
  10. Guan, Z., Deng, T., Jiang, Y., Zhao, C. and Huang, H. (2014), "Probabilistic estimation of ground condition and construction cost for mountain tunnels", Tunn. Undergr. Sp. Tech., 42, 175-183. https://doi.org/10.1016/j.tust.2014.02.014.
  11. Haas, C. and Einstein, H.H. (2002), "Updating in the decision aids for tunneling", J. Constr. Eng. Management, 128(1), 40-48. https://doi.org/10.1061/(ASCE)0733-9364(2002)128%3A1(40).
  12. Jeon, J., Martin, C., Chan, D.H. and Kim, J.S. (2005), "Predicting ground condition ahead of the tunnel face by vector orientation analysis", Tunn. Undergr. Sp. Tech., 20(4), 344-355. https://doi.org/10.1016/j.tust.2005.01.002.
  13. Kang, F., Han, S.X., Salgado, R. and Li, J.J. (2015), "System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling", Comput. Geotech., 63, 13-25. https://doi.org/10.1016/j.compgeo.2014.08.010.
  14. Kundapura, S. and Hegde, A.V. (2017), "Current approaches of artificial intelligence in breakwaters - A review", Ocean Syst. Eng., 7(2), 75-87. https://doi.org/10.12989/ose.2017.7.2.075.
  15. Luat, N.V., Lee, K. and Thai, D.K. (2020), "Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils", Geomech. Eng., 20(5), http://doi.org/10.12989/gae.2020.20.5.385.
  16. Liu, J., Jiang, Y., Zhang, Y. and Sakaguchi, O. (2021a), "Influence of different combinations of measurement while drilling parameters by artificial neural network on estimation of tunnel support patterns.", Geomech. Eng., 25(6), 439-454. https://doi.org/10.12989/gae.2021.25.6.439.
  17. Liu, L.L., Yang, C. and Wang, X.M. (2021b), "Landslide susceptibility assessment using feature selection-based machine learning models.", Geomech. Eng., 25(1), 1-16. https://doi.org/10.12989/gae.2021.25.1.001.
  18. Li, L.P., Shi, S.S., Zhang, Q.Q., Zhang, J. and Hu, J. (2017), "Gaussian process model of water in flow prediction in tunnel construction and its engineering applications", Tunn. Undergr. Sp. Tech., 69, 155-161. https://doi.org/10.1016/j.tust.2017.06.018.
  19. Mahmoodzadeh, A. and Zare, S. (2016), "Probabilistic prediction of the expected ground conditions and construction time and costs in road tunnels", J. Rock Mech. Geotech. Eng., 8(5), 734-745. https://doi.org/10.1016/j.jrmge.2016.07.001
  20. Mahmoodzadeh, A., Mohammadi, M., Daraei, A., Rashid, T.A., Sherwani, A.F.H., Faraj, R.H. and Darwesh, A.M. (2019), "Updating ground conditions and time-cost scatter-gram in tunnels during excavation", Automat. Constr., 105, 102822. https://doi.org/10.1016/j.autcon.2019.04.017
  21. Mahmoodzadeh, A., Mohammadi, M., Abdulhamid, S.N., Ibrahim, H.H., Hama-Ali, H.F. and Salim, S.G. (2021a), "Dynamic reduction of time and cost uncertainties in tunneling projects", Tunn. Undergr. Sp. Tech., 109, 103774. https://doi.org/10.1016/j.tust.2020.103774
  22. Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Abdulhamid, S.N., Salim, S.G., Hama Ali, H.F. and Majeed, M.K. (2021b), "Artificial intelligence forecasting models of uniaxial compressive strength", Transport. Geotech., 27, 100499. https://doi.org/10.1016/j.trgeo.2020.100499.
  23. Mahmoodzadeh, A., Mohammadi, M., Hama Ali, H.F., Abdulhamid, S.N., Ibrahim, H.H. and Noori, K.M.G. (2021c), "Dynamic prediction models of rock quality designation in tunneling projects", Transport. Geotech., 27, 100497. https://doi.org/10.1016/j.trgeo.2020.100497.
  24. Mahmoodzadeh, A., Mohammadi, M., Daraei, A., Hama Ali, H.F., Abdullah, A.I. and Al-Salihi, N.K. (2021d), "Forecasting tunnel geology, construction time and costs using machine learning methods", Neural Comput. Appl., 33, 321-348. https://doi.org/10.1007/s00521-020-05006-2.
  25. Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Abdulhamid, S.N., Hama Ali, H.F., Hasan, A.M., Khishe, M. and Mahmud, H. (2021e), "Machine learning forecasting models of disc cutters life of tunnel boring machine", Automat. Constr., 128, 103779. https://doi.org/10.1016/j.autcon.2021.103779.
  26. Mahmoodzadeh, A., Mohammadi, M., Noori, K.M.G., Khishe, M., Ibrahim, H.H., Hama Ali, H.F. and Abdulhamid, S.N. (2021f), "Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques", Automat. Constr., 127, 103719. https://doi.org/10.1016/j.autcon.2021.103719.
  27. Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Noori, K.M.G., Abdulhamid, S.N. and Hama Ali, H.F. (2021g), "Forecasting sidewall displacement of underground caverns using machine learning techniques", Automat. Constr., 123, 103530. https://doi.org/10.1016/j.autcon.2020.103530.
  28. Mahmoodzadeh, A., Mohammadi, M., Ibrahim, H.H., Rashid, T.A., Aldalwie, A.H.M., Hama Ali, H.F. and Daraei, A. (2021h), "Tunnel geomechanical parameters prediction using Gaussian process regression", Mach. Learn. Appl., 3, 100020. https://doi.org/10.1016/j.mlwa.2021.100020.
  29. Mahmoodzadeh, A., Mohammadi, M., Abdulhamid, S.N., Nejati, H.R., Noori, K.M.G., Ibrahim, H.H. and Hama Ali, H.F. (2021i), "Predicting construction time and cost of tunnels using Markov chain model considering opinions of experts", Tunn. Under. Sp. Tech., 116, 104109. https://doi.org/10.1016/j.tust.2021.104109.
  30. Mahmoodzadeh, A., Mohammadi, M., Daraei, A., Hama Ali, H.F., Al-Salihi, N.K. and Omer, R.M.D. (2020a), "Forecasting maximum surface settlement caused by urban tunneling", Automat. Constr., 120, 103375. https://doi.org/10.1016/j.autcon.2020.103375.
  31. Mahmoodzadeh, A., Mohammadi, M., Daraei, A., Faraj, R.H., Omer, R.M.D. and Sherwani, A.F.H. (2020b), "Decision-making in tunneling using artificial intelligence tools", Tunn. Undergr. Sp. Tech., 103, 103514. https://doi.org/10.1016/j.tust.2020.103514.
  32. Pal, M. and Deswal, S. (2010), "Modelling pile capacity using Gaussian process regression", Comput. Geotech., 37(4), 942-947. https://doi.org/10.1109/MSP.2013.2250352.
  33. Sousa, R.L. and Einstein, H.H. (2012), "Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study", Tunn. Undergr. Sp. Tech., 27(1), 86-100. https://doi.org/10.1016/j.tust.2011.07.003.
  34. Wang, J., Li, S., Li, L., Shi, S., Xu, Z. and Lin, P. (2017), "Collapse risk evaluation method on Bayesian network prediction model and engineering application", Adv. Comput. Design, 2(2), 121-131. https://doi.org/10.12989/acd.2017.2.2.121.
  35. Xiang, G., Yin, D., Cao, C. and Yuan, L. (2021), "Application of artificial neural network for prediction of flow ability of soft soil subjected to vibrations.", Geomech. Eng., 25(5), 395-403. https://doi.org/10.12989/gae.2021.25.5.395.
  36. Yuan, J., Chen, W., Tan, X., Yang, D. and Wang, S. (2019), "Countermeasures of water and mud inrush disaster in completely weathered granite tunnels: a case study", Environ. Earth. Sci., 78:576. https://doi.org/10.1007/s12665-019-8590-8