Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of duration and construction cost of road tunnels using Gaussian process regression

  • Mahmoodzadeh, Arsalan (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • 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) ;
  • Ibrahim, Hawkar Hashim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Ali, Hunar Farid Hama (Department of Civil Engineering, University of Halabja) ;
  • Nejati, Hamid Reza (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Rashidi, Shima (Department of Computer Science, College of Science and Technology, University of Human Development)
  • Received : 2021.08.17
  • Accepted : 2021.12.01
  • Published : 2022.01.10
⟨ Previous Next ⟩

Abstract

Time and cost of construction are key factors in decision-making during a tunnel project's planning and design phase. Estimations of time and cost of tunnel construction projects are subject to significant uncertainties caused by uncertain geotechnical and geological conditions. The Gaussian Process Regression (GPR) technique for predicting ground condition and construction time and cost of mountain tunnel projects is used in this work. The GPR model is trained with data from past mountain tunnel projects. The model is applied to a case study in which the predicted time and cost of tunnel construction using the GPR model are compared with the actual construction time and cost for model validation and reducing the uncertainty for the future projects. In addition, the results obtained from the GPR have been compared with to other models of artificial neural network (ANN) and support vector regression (SVR) that the GPR model provides more accurate results.

Keywords

References

  1. Alade, I.O., Zhang, Y. and Xu, X. (2021), "Modeling and prediction of lattice parameters of binary spinel compounds (AM2X4) using support vector regression with Bayesian optimization", New J. Chem., 34. https://doi.org/10.1039/d1nj01523k
  2. Bai, X.D., Cheng, W.C., Ong, D.E.L. and Ge Li. (2021), "Evaluation of geological conditions and clogging of tunneling using machine learning", Geomech. Eng., 25(1), 59-73. http://doi.org/10.12989/gae.2021.25.1.059.
  3. Carriere, S.D., Chalikakis, K., Guy, S., Charles, D. and Christophe, E. (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. Guan, Z., Deng, T., Jiang, Y., Zhao, C. and Huang, H. (2014), "Probabilistic estimation of ground condition and construction cost for mountain tunnels", Tun. Undergr. Sp. Tech., 42, 175-183. https://doi.org/10.1016/j.tust.2014.02.014.
  5. Khishe, M., Mosavi, M.R. and Moridi, A. (2018), "Chaotic fractal walk trainer for sonar data set classification using multi-layer perceptron neural network and its hardware implementation", Appl. Acoust., 137, 121-139. https://doi.org/10.1016/j.apacoust.2018.03.012.
  6. Khishe, M. and Mosavi, M.R. (2019), "Improved whale trainer for sonar datasets classification using neural network", Appl. Acoust., 154, 176-192. https://doi.org/10.1016/j.apacoust.2019.05.006.
  7. Khishe, M. and Mosavi, M.R. (2020). "Chimp optimization algorithm, Expert Systems with Applications", 149, 113338. https://doi.org/10.1016/j.eswa.2020.113338.
  8. Luat, N.V., Lee, K. and Duc-Kien, T. (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.
  9. 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. http://doi.org/10.12989/gae.2021.25.6.439.
  10. 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. http://doi.org/10.12989/gae.2021.25.1.001.
  11. 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.
  12. 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", Autom. Constr., 105, 102822. DOI: 10.1016/j.autcon.2019.04.017.
  13. 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", Tun. Undergr. Sp. Tech., 109, 103774. https://doi.org/10.1016/j.tust.2020.103774.
  14. Mahmoodzadeh, A., Mohammadi, M., Abdulhamid, S.N., Nejati, H.R., Noori, K.M.G., Ibrahim, H.H. and Hama Ali, H.F. (2021b), "Predicting construction time and cost of tunnels using Markov chain model considering opinions of experts", Tun. Undergr. Sp. Tech., 116, 104109. https://doi.org/10.1016/j.tust.2021.104109.
  15. 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", Autom. Constr., 120, 103375. https://doi.org/10.1016/j.autcon.2020.103375.
  16. 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", Tun. Undergr. Sp. Tech., 103, 103514. https://doi.org/10.1016/j.tust.2020.103514.
  17. 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", Transportation Geotech., 27, 100497. https://doi.org/10.1016/j.trgeo.2020.100497.
  18. 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
  19. 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", Autom. Constr,, 128, 103779. https://doi.org/10.1016/j.autcon.2021.103779.
  20. 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", Autom. Constr,, 127, 103719. https://doi.org/10.1016/j.autcon.2021.103719.
  21. 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", Autom. Constr,, 123, 103530. https://doi.org/10.1016/j.autcon.2020.103530.
  22. 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.
  23. Min, S. and Einstein, H.H. (2016), "Resource scheduling and planning for tunneling with a new resource model of the Decision Aids for Tunneling (DAT)", Tun. Undergr. Sp. Tech., 51, 212-225. https://doi.org/10.1016/j.tust.2015.10.038.
  24. Sousa, R.L. and Einstein, H.H. (2012), "Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study", Tun. Undergr. Sp. Tech., 27(1), 86-100. https://doi.org/10.1016/j.tust.2011.07.003
  25. Saffari, A., Zahiri, S.H and Khishe, M. (2022). "Fuzzy grasshopper optimization algorithm: A hybrid technique for tuning the control parameters of GOA using fuzzy system for big data sonar classification", IJEEE, 18(1), 2131-2131. http://ijeee.iust.ac.ir/article-1-2131-en.html.
  26. Taghavi, M. and Khishe, M. (2019). "A Modified Grey Wolf Optimizer by Individual Best Memory and Penalty Factor for Sonar and Radar Dataset Classification", Iran J. Mar. Technol., 6(1), 120-130. http://ijmt.iranjournals.ir/article_35433.html.
  27. Vapnik, V.N. (1995), The nature of statistical learning theory. New York: Springer.
  28. 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. http://doi.org/10.12989/gae.2021.25.5.395.
  29. Zhang, Y. and Xu, X. (2021a), "Solid particle erosion rate predictions through LSBoost", Powder Technol., 388, 517-525. https://doi.org/10.1016/j.powtec.2021.04.072.
  30. Zhang, Y. and Xu, X (2021b), "Predictions of the total crack length in solidification cracking through LSBoost", Metal. Mater. Trans. A, 52, 985-1005. https://doi.org/10.1007/s11661-020-06130-3.
  31. Zhang, Y. and Xu, X. (2021c), "Predicting the material removal rate during electrical discharge diamond grinding using the Gaussian process regression: a comparison with the artificial neural network and response surface methodology", Int. J. Adv. Manuf. Technol., 113, 1527-1533. https://doi.org/10.1007/s00170-021-06701-7.
  32. Zhang, Y. and Xu, X. (2021d), "Lattice misfit predictions via the Gaussian process regression for ni-based single crystal superalloys", Met. Mater. Int., 27, 235-253. https://doi.org/10.1007/s12540-020-00883-7.
  33. Zhang, Y. and Xu, X. (2020), "Solubility predictions through LSBoost for supercritical carbon dioxide in ionic liquids", New J. Chem., 47. https://doi.org/10.1039/d0nj03868g.