Browse > Article
http://dx.doi.org/10.12989/gae.2022.28.4.359

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)
Publication Information
Geomechanics and Engineering / v.28, no.4, 2022 , pp. 359-374 More about this Journal
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
engineering geology; Gaussian process regression; geomechanical parameters; tunneling; tunnel geology;
Citations & Related Records
Times Cited By KSCI : 11  (Citation Analysis)
연도 인용수 순위
1 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.
2 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.   DOI
3 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.   DOI
4 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).   DOI
5 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   DOI
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 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   DOI
10 Bieniawski, Z.T. (1976), Rock mass classification in rock engineering, in exploration for rock engineering, 1, A.A. Balkema, Cape town.
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 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   DOI
15 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.   DOI
16 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.   DOI
17 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   DOI
18 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.   DOI
19 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.   DOI
20 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.   DOI
21 Bieniawski, Z.T. (1979), "The geomechanics classification in rock engineering applications", Proceedings of the 4th International Congress on Rock Mechanics.
22 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.   DOI
23 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.   DOI
24 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.   DOI
25 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.   DOI
26 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.   DOI
27 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.   DOI
28 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.   DOI
29 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.   DOI
30 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.   DOI
31 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.   DOI
32 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.   DOI
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.   DOI
34 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.   DOI
35 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.   DOI
36 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.   DOI