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Forecasting tunnel geology, construction time and costs using machine learning methods

Abstract

This research intends to use machine learning approaches to predict tunnel geology and its construction time and costs. For this purpose, the Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree (DT) have been utilized. An estimation of the geological conditions of the Garan road tunnel and its construction time and cost has been conducted. In addition, after constructing about 200 m from the inlet and outlet sides of the tunnel, using the field-observed data of these sectors in the tools, all the previously forecasted results were updated for unconstructed parts. Fivefold cross-validation has been applied to assess the performance of each model. The obtained models are used to predict construction time and cost in real scenarios, and the accuracy of each model was investigated through different statistical evaluation criteria. Finally, it turns out that all the models provide relatively high performance and reduce the uncertainties of tunnel geology. However, the GPR provides more accurate results compared to the SVR and DT tools. Thus, we recommend the GPR for the prediction of geology and construction time and costs in future levels of a tunnel.

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References

  1. 1.

    Flyvbjerg B, Holm MS, Buhl S (2002) Underestimating costs in public works projects: error or lie? J Am Plan Assoc 68:279–295. https://doi.org/10.1080/01944360208976273

    Article  Google Scholar 

  2. 2.

    Wang S, Li L, Shi S, Cheng S, Hu H, Wen T (2020) Dynamic risk assessment method of collapse in mountain tunnels and application. Geotech Geol Eng. https://doi.org/10.1007/s10706-020-01196-7

    Article  Google Scholar 

  3. 3.

    Zhou H, Zhao Y, Shen Q, Yang L, Cai H (2020) Risk assessment and management via multi-source information fusion for undersea tunnel construction. Autom Constr 111:103050. https://doi.org/10.1016/j.autcon.2019.103050

    Article  Google Scholar 

  4. 4.

    Wang X, Shi K, Shi Q, Dong H, Chen M (2020) A normal cloud model-based method for risk assessment of water inrush and its application in a super-long tunnel constructed by a tunnel boring machine in the arid area of Northwest China. Water 12:644. https://doi.org/10.3390/w12030644

    Article  Google Scholar 

  5. 5.

    Shahrour I, Bian H, Xie X, Zhang Z (2020) Use of smart technology to improve management of utility tunnels. Appl Sci 10:711. https://doi.org/10.3390/app10020711

    Article  Google Scholar 

  6. 6.

    Mahmoodzadeh A, Zare S (2016) Probabilistic prediction of the expected ground conditions and construction time and costs in road tunnels. J Rock Mech Geotech Eng 8:734–745. https://doi.org/10.1016/j.jrmge.2016.07.001

    Article  Google Scholar 

  7. 7.

    Mahmoodzadeh A, Mohammadi M, Daraei A, Rashid TA, Sherwani AFH, Faraj RH, Darwesh AM (2019) Updating ground conditions and time-cost scatter-gram in tunnels during excavation. Autom Constr 105:102822. https://doi.org/10.1016/j.autcon.2019.04.017

    Article  Google Scholar 

  8. 8.

    Flyvbjerg B (2006) From Nobel Prize to project management: getting risks right. Proj Manag J 37:5–15. https://doi.org/10.1177/875697280603700302

    Article  Google Scholar 

  9. 9.

    Kermanshachi S, Safapour E (2020) Gap analysis in cost estimation, risk analysis, and contingency computation of transportation infrastructure projects: a guide to resource and policy-based strategy establishment. Practi Period Struct Des Constr 25:06019004. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000460

    Article  Google Scholar 

  10. 10.

    Alsultan M, Jun J, Lambert JH (2020) Program evaluation of highway access with innovative risk-cost-benefit analysis. Reliab Eng Syst Saf 193:106649. https://doi.org/10.1016/j.ress.2019.106649

    Article  Google Scholar 

  11. 11.

    Cerezo-Narváez A, Pastor-Fernández A, Otero-Mateo M, Ballesteros-Pérez P (2020) Integration of cost and work breakdown structures in the management of construction projects. Appl Sci 10:1386. https://doi.org/10.3390/app10041386

    Article  Google Scholar 

  12. 12.

    Ahn SJ, Han SU, Al-Hussein M (2020) Improvement of transportation cost estimation for prefabricated construction using geo-fence-based large-scale GPS data feature extraction and support vector regression. Adv Eng Inform 43:101012. https://doi.org/10.1016/j.aei.2019.101012

    Article  Google Scholar 

  13. 13.

    Min SY, Kim TK, Lee JS, Einstein HH (2008) Design and construction of a road tunnel in Korea including application of the decision aids for tunneling—a case study. Tunn Undergr Space Technol 23:91–102. https://doi.org/10.1016/j.tust.2007.01.003

    Article  Google Scholar 

  14. 14.

    Moret Y, Einstein HH (2016) Construction cost and duration uncertainty model: application to high-speed rail line project. J Constr Eng Manag 142:05016010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001161

    Article  Google Scholar 

  15. 15.

    Sousa RL, Einstein HH (2012) Risk analysis during tunnel construction using Bayesian networks: Porto Metro case study. Tunn Undergr Space Technol 27:86–100. https://doi.org/10.1016/j.tust.2011.07.003

    Article  Google Scholar 

  16. 16.

    Chung TH, Mohamed Y, AbouRizk S (2006) Bayesian updating application into simulation in the North Edmonton Sanitary Trunk tunnel project. J Constr Eng Manag 132:882–894. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:8(882)

    Article  Google Scholar 

  17. 17.

    Benardos AG, Kaliampakos DC (2004) Modelling TBM performance with artificial neural networks. Tunn Undergr Space Technol 19:597–605. https://doi.org/10.1016/j.tust.2004.02.128

    Article  Google Scholar 

  18. 18.

    Isaksson T, Stille H (2005) Model for estimation of time and cost for tunnel projects based on risk evaluation. Rock Mech Rock Eng 38:373–398. https://doi.org/10.1007/s00603-005-0048-5

    Article  Google Scholar 

  19. 19.

    Moayedi H, Mosallanezhad M, Rashid ASA, Jusoh WAW, Muazu MA (2020) A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Comput Appl 32:495–518. https://doi.org/10.1007/s00521-019-04109-9

    Article  Google Scholar 

  20. 20.

    Galende-Hernández M, Menéndez M, Fuente MJ, Palmero GIS (2018) Monitor-While-Drilling-based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front. Autom Constr 93:325–338. https://doi.org/10.1016/j.autcon.2018.05.019

    Article  Google Scholar 

  21. 21.

    Wauters M, Vanhoucke M (2014) Support vector machine regression for project control forecasting. Autom Constr 47:92–106. https://doi.org/10.1016/j.autcon.2014.07.014

    Article  Google Scholar 

  22. 22.

    Cheng MY, Wu YW (2009) Evolutionary support vector machine inference system for construction management. Autom Constr 18:597–604. https://doi.org/10.1016/j.autcon.2008.12.002

    Article  Google Scholar 

  23. 23.

    Tixier AJP, Hallowell MR, Rajagopalan B, Bowman D (2016) Application of machine learning to construction injury prediction. Autom Constr 69:102–114. https://doi.org/10.1016/j.autcon.2016.05.016

    Article  Google Scholar 

  24. 24.

    Cheng MY, Wu YW, Chen KL (2012) Risk preference based support vector machine inference model for slope collapse prediction. Autom Constr 22:175–181. https://doi.org/10.1016/j.autcon.2011.06.015

    Article  Google Scholar 

  25. 25.

    Gao X, Shi M, Song X, Zhang C, Zhang H (2019) Recurrent neural networks for real-time prediction of TBM operating parameters. Autom Constr 98:225–235. https://doi.org/10.1016/j.autcon.2018.11.013

    Article  Google Scholar 

  26. 26.

    Torabi-Kaveh M, Sarshari B (2019) Predicting convergence rate of Namaklan twin tunnels using machine learning methods. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-04239-1

    Article  Google Scholar 

  27. 27.

    Rohmer J, Foerster E (2011) Global sensitivity analysis of large-scale numerical land-slide models based on Gaussian-process metamodeling. Comput Geosci 37:91–927

    Article  Google Scholar 

  28. 28.

    Liu R, Ye Y, Hu N, Chen H, Wang X (2019) Classified prediction model of rock burst using rough sets-normal cloud. Neural Comput Appl 31:8185–8193. https://doi.org/10.1007/s00521-018-3859-5

    Article  Google Scholar 

  29. 29.

    Ning F, Shi Y, Cai M, Xu W, Zhang X (2020) Manufacturing cost estimation based on a deep-learning method. J Manuf Syst 54:186–195. https://doi.org/10.1016/j.jmsy.2019.12.005

    Article  Google Scholar 

  30. 30.

    Barzegar R, Sattarpour M, Deo R, Fijani E, Adamowski J (2019) An ensemble tree-based machine learning model for predicting the uniaxial compressive strength of travertine rocks. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04418-z

    Article  Google Scholar 

  31. 31.

    Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 4:705–715. https://doi.org/10.1007/s00366-016-0447-0

    Article  Google Scholar 

  32. 32.

    Goh ATC, Zhang W, Zhang Z, Xiao Y, Xiang Y (2018) Determination of earth pressure balance tunnel-related maximum surface settlement: a multivariate adaptive regression splines tool. Bull Eng Geol Environ 77:489–500. https://doi.org/10.1007/s10064-016-0937-8

    Article  Google Scholar 

  33. 33.

    Tijanić K, Car-Puši D, Šperac M (2019) Costs estimation in road construction using artificial neural network. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04443-y

    Article  Google Scholar 

  34. 34.

    Hashemi ST, Ebadati OM, Kaur HA (2019) Hybrid conceptual costs estimating model using ANN and GA for power plant projects. Neural Comput Appl 31:2143–2154. https://doi.org/10.1007/s00521-017-3175-5

    Article  Google Scholar 

  35. 35.

    Gao W, Karbasi M, Hasanipanah M, Zhang X, Guo J (2018) Developing GPR model for forecasting the rock fragmentation in surface mines. Eng Comput 34:339–345. https://doi.org/10.1007/s00366-017-0544-8

    Article  Google Scholar 

  36. 36.

    Mohammadi M, Hossaini MF (2017) Modification of rock mass rating system: interbedding of strong and weak rock layers. J Rock Mech Geotech Eng 9:1165–1170. https://doi.org/10.1016/j.jrmge.2017.06.002

    Article  Google Scholar 

  37. 37.

    Bieniawski ZT (1973) Engineering classification of jointed rock masses. S Afr Inst Civ Eng 15:335–344

    Google Scholar 

  38. 38.

    Bieniawski ZT (1989) Engineering rock mass classifications: a complete manual for engineers and geologists in mining, civil, and petroleum engineering. Wiley-Interscience, New York, pp 40–47. ISBN 0-471-60172-1

  39. 39.

    Williams CKI (1998) Prediction with Gaussian processes: from linear regression to linear prediction and beyond. In: Jordan MI (ed) Learning in graphical models. NATO ASI series (Series D: behavioural and social sciences), vol 89. Springer, Dordrecht, pp 599–621. https://doi.org/10.1007/978-94-011-5014-9_23

    Chapter  Google Scholar 

  40. 40.

    Rasmussen CE (2004) Gaussian processes in machine learning. In: Bousquet O, von Luxburg U, Rätsch G (eds) Advanced lectures on machine learning. ML 2003. Lecture notes in computer science, vol 3176. Springer, Berlin, pp 63–71. https://doi.org/10.1007/978-3-540-28650-9_4

  41. 41.

    Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge. ISBN 026218253X

  42. 42.

    Maity R, Bhagwat PP, Bhatnagar A (2010) Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrol Process 24:917–923. https://doi.org/10.1002/hyp.7535

    Article  Google Scholar 

  43. 43.

    Yu PS, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328:704–716. https://doi.org/10.1016/j.jhydrol.2006.01.021

    Article  Google Scholar 

  44. 44.

    Behnia D, Ahangari K, Noorzad A, Moeinossadat SR (2013) Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods. J Zhejiang Univ Sci A 14:589–602. https://doi.org/10.1631/jzus.A1200301

    Article  Google Scholar 

  45. 45.

    Wang C, Wang X, Xia Z, Zhang C (2019) Ternary radial harmonic Fourier moments based robust stereo image zero-watermarking algorithm. Inf Sci 470:109–120. https://doi.org/10.1016/j.ins.2018.08.028

    Article  Google Scholar 

  46. 46.

    Garg A, Tai K, Vijayaraghavan V, Singru PM (2014) Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach. Int J Adv Manuf Technol 73:113–126. https://doi.org/10.1007/s00170-014-5817-4

    Article  Google Scholar 

  47. 47.

    Wang C, Wang X, Li Y, Xia Z, Zhang C (2018) Quaternion polar harmonic Fourier moments for color images. Inf Sci 450:141–156. https://doi.org/10.1016/j.ins.2018.03.040

    MathSciNet  Article  MATH  Google Scholar 

  48. 48.

    Shahin MA (2014) Load-settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks. Soils Found 54:515–522. https://doi.org/10.1016/j.sandf.2014.04.015

    Article  Google Scholar 

  49. 49.

    Wang C, Wang X, Xia X, Ma B, Shi YQ (2019) Image description with polar harmonic Fourier moments. IEEE Trans Circuits Syst Video Technol. https://doi.org/10.1109/TCSVT.2019.2960507

    Article  Google Scholar 

  50. 50.

    Ahangari K, Moeinossadat SM, Behnia D (2015) Estimation of tunnelling-induced settlement by modern intelligent methods. Soils Found 55:737–748. https://doi.org/10.1016/j.sandf.2015.06.006

    Article  Google Scholar 

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Correspondence to Arsalan Mahmoodzadeh.

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Mahmoodzadeh, A., Mohammadi, M., Daraei, A. et al. Forecasting tunnel geology, construction time and costs using machine learning methods. Neural Comput & Applic 33, 321–348 (2021). https://doi.org/10.1007/s00521-020-05006-2

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Keywords

  • Gaussian Process Regression
  • Support Vector Regression
  • Decision Tree
  • Tunneling