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.
Similar content being viewed by others
References
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
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
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
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
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
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
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
Flyvbjerg B (2006) From Nobel Prize to project management: getting risks right. Proj Manag J 37:5–15. https://doi.org/10.1177/875697280603700302
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Bieniawski ZT (1973) Engineering classification of jointed rock masses. S Afr Inst Civ Eng 15:335–344
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
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
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
Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge. ISBN 026218253X
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
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
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
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
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
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-020-05006-2