A Rigorous Examination of Twelve Cutting-Edge Machine-Learning Techniques for Predicting Time and Cost in Tunneling Projects
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 12
Abstract
This study aimed to investigate and analyze the performance of twelve machine learning (ML) algorithms in estimating the construction time and cost of drill and blast tunnels. Thirteen tunnels located in different regions of Iran were selected, resulting in 900 data sets. Ten parameters were identified as influential factors affecting the construction time and cost of these tunnels. 80% of the data set was used for training, while the remaining 20% was reserved for testing. Additionally, 288 unseen data sets were utilized for evaluation purposes. All the algorithms demonstrated accurate performance on the test data sets, with R-squared values exceeding 0.93. However, only the Gaussian process regression algorithm achieved satisfactory results on the unseen data sets. Furthermore, a graphical user interface (GUI) was developed based on the trained ML models. This GUI allows real-time estimation of the time and cost of drill and blast tunnels and can be updated during construction.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study is supported via funding from Prince Satam bin Abdulaziz University Project No. (PSAU/2024/R/1445). The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the Project No. “NBU-FFR-2024-1069-01”.
References
Bruce, G. 2021. “Cost estimating.” In Shipbuilding management, 123–130. Singapore: Springer.
Chen, T., and C. Guestrin. 2016. “XGBoost.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 785–794. New York: ACM. https://doi.org/10.1145/2939672.2939785.
Cortes, C., and V. Vapnik. 1995. “Support-vector networks.” Mach. Learn. 20 (3): 273–297. https://doi.org/10.1007/BF00994018.
Cover, T., and P. Hart. 1967. “Nearest neighbor pattern classification.” IEEE Trans. Inf. Theory 13 (1): 21–27. https://doi.org/10.1109/TIT.1967.1053964.
Gad, A. F. 2018. “Artificial neural networks.” In Practical computer vision applications using deep learning with CNNs, 45–106. Berkeley, CA: Apress.
Gayathri, R., S. U. Rani, L. Čepová, M. Rajesh, and K. Kalita. 2022. “A comparative analysis of machine learning models in prediction of mortar compressive strength.” Processes 10 (7): 1387. https://doi.org/10.3390/pr10071387.
Guan, Z., T. Deng, Y. Jiang, C. Zhao, and H. Huang. 2014. “Probabilistic estimation of ground condition and construction cost for mountain tunnels.” Tunnelling Underground Space Technol. 42 (May): 175–183. https://doi.org/10.1016/j.tust.2014.02.014.
Haas, C., and H. H. Einstein. 2002. “Updating the decision aids for tunneling.” J. Constr. Eng. Manage. 128 (1): 40–48. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:1(40).
He, H., S. Wang, W. Shen, and W. Zhang. 2023. “The influence of pipe-jacking tunneling on deformation of existing tunnels in soft soils and the effectiveness of protection measures.” Transp. Geotech. 42 (Sep): 101061. https://doi.org/10.1016/j.trgeo.2023.101061.
Ho, T. K. 1998. “The random subspace method for constructing decision forests.” IEEE Trans. Pattern Anal. Mach. Intell. 20 (8): 832–844. https://doi.org/10.1109/34.709601.
Hu, D., Y. Li, X. Yang, X. Liang, K. Zhang, and X. Liang. 2023. “Experiment and application of NATM tunnel deformation monitoring based on 3D laser scanning.” Struct. Control Health Monit. 3341788 (Jun): 13. https://doi.org/10.1155/2023/3341788.
Jin, R., and G. Agrawal. 2003. “Communication and memory efficient parallel decision tree construction.” In Proc., 2003 SIAM Int. Conf. on Data Mining, 119–129. Philadelphia: Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611972733.11.
Kordestani, H., C. Zhang, and A. Arab. 2024. “An investigation into the application of acceleration responses’ trendline for bridge damage detection using quadratic regression.” Sensors 24 (2): 410. https://doi.org/10.3390/s24020410.
Liu, Q. Y., D. Q. Li, X. S. Tang, and W. Du. 2023a. “Predictive models for seismic source parameters based on machine learning and general orthogonal regression approaches.” Bull. Seismol. Soc. Am. 113 (6): 2363–2376. https://doi.org/10.1785/0120230069.
Liu, W., J. Liang, and T. Xu. 2023b. “Tunnelling-induced ground deformation subjected to the behavior of tail grouting materials.” Tunnelling Underground Space Technol. 140 (Oct): 105253. https://doi.org/10.1016/j.tust.2023.105253.
Mahmoodzadeh, A., M. Mohammadi, A. Daraei, R. H. Faraj, R. M. Omer, and A. F. Sherwani. 2020. “Decision-making in tunneling using artificial intelligence tools.” Tunnelling Underground Space Technol. 103 (Sep): 103514. https://doi.org/10.1016/j.tust.2020.103514.
Mahmoodzadeh, A., M. Mohammadi, A. Daraei, H. Farid Hama Ali, A. Ismail Abdullah, and N. Kameran Al-Salihi. 2021a. “Forecasting tunnel geology, construction time and costs using machine learning methods.” Neural Comput. Appl. 33 (1): 321–348. https://doi.org/10.1007/s00521-020-05006-2.
Mahmoodzadeh, A., M. Mohammadi, S. Nariman Abdulhamid, H. H. Ibrahim, H. F. Ali, and S. G. Salim. 2021b. “Dynamic reduction of time and cost uncertainties in tunneling projects.” Tunnelling Underground Space Technol. 109 (Mar): 103774. https://doi.org/10.1016/j.tust.2020.103774.
Mahmoodzadeh, A., and H. R. Nejati. 2023. “An optimized equation based on the gene expression programming method for estimating tunnel construction costs considering a variety of variables and indexes.” Appl. Soft Comput. 147 (Nov): 110749. https://doi.org/10.1016/j.asoc.2023.110749.
Mahmoodzadeh, A., H. R. Nejati, M. Mohammadi, H. H. Ibrahim, M. Khishe, S. Rashidi, and A. H. Mohammed. 2022. “Developing six hybrid machine learning models based on gaussian process regression and meta-heuristic optimization algorithms for prediction of duration and cost of road tunnels construction.” Tunnelling Underground Space Technol. 130 (Dec): 104759. https://doi.org/10.1016/j.tust.2022.104759.
Min, S., and H. H. Einstein. 2016. “Resource scheduling and planning for tunneling with a new resource model of the Decision Aids for Tunneling (DAT).” Tunnelling Underground Space Technol. 51 (Jan): 212–225. https://doi.org/10.1016/j.tust.2015.10.038.
Min, S. Y., H. H. Einstein, J. S. Lee, and H. S. Lee. 2005. “Application of the Decision Aids for Tunneling (DAT) to update excavation cost/time information.” KSCE J. Civ. Eng. 9 (4): 335–346. https://doi.org/10.1007/BF02829047.
Ning, F., Y. Shi, X. Tong, M. Cai, and W. Xu. 2023. “Manufacturing cost estimation based on similarity.” Int. J. Comput. Integr. Manuf. 36 (8): 1238–1253. https://doi.org/10.1080/0951192X.2023.2165160.
Paraskevopoulou, C., and A. Benardos. 2013. “Assessing the construction cost of Greek transportation tunnel projects.” Tunnelling Underground Space Technol. 38 (Sep): 497–505. https://doi.org/10.1016/j.tust.2013.08.005.
Prasad, D. V. V., and S. Jaganathan. 2019. “Null-space based facial classifier using linear regression and discriminant analysis method.” Cluster Comput. 22 (S4): 9397–9406. https://doi.org/10.1007/s10586-018-2178-z.
Quinlan, J. R. 1986. “Induction of decision trees.” Mach. Learn. 1 (1): 81–106. https://doi.org/10.1007/BF00116251.
Rasmussen, C. E. 2004. “Gaussian processes in machine learning.” In Summer school on machine learning, 63–71. Berlin: Springer.
Rostami, J., M. Sepehrmanesh, E. A. Gharahbagh, and N. Mojtabai. 2013. “Planning level tunnel cost estimation based on statistical analysis of historical data.” Tunnelling Underground Space Technol. 33 (Jan): 22–33. https://doi.org/10.1016/j.tust.2012.08.002.
Shi, M., W. Hu, M. Li, J. Zhang, X. Song, and W. Sun. 2023. “Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine.” Mech. Syst. Signal Process. 188 (Apr): 110022. https://doi.org/10.1016/j.ymssp.2022.110022.
Sinfield, J. V., and H. H. Einstein. 1996. “Evaluation of tunneling technology using the “decision aids for tunneling.” Tunnelling Underground Space Technol. 11 (4): 491–504. https://doi.org/10.1016/S0886-7798(96)89245-5.
Szwarcfiter, C., Y. T. Herer, and A. Shtub. 2022. “Project scheduling in a lean environment to maximize value and minimize overruns.” J. Sched. 25 (2): 177–190. https://doi.org/10.1007/s10951-022-00727-9.
Wei, W., J. Gong, J. Deng, and W. Xu. 2023. “Effects of air vent size and location design on air supply efficiency in flood discharge tunnel operations.” J. Hydraul. Eng. 149 (12): 04023050. https://doi.org/10.1061/JHEND8.HYENG-13305.
Ye, X., P. Ding, D. Jin, C. Zhou, Y. Li, and J. Zhang. 2023. “Intelligent analysis of construction costs of shield tunneling in complex geological conditions by machine learning method.” Mathematics 11 (6): 1423. https://doi.org/10.3390/math11061423.
Yin, H., Q. Wu, S. Yin, S. Dong, Z. Dai, and M. R. Soltanian. 2023. “Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest.” J. Hydrol. 616 (Jan): 128813. https://doi.org/10.1016/j.jhydrol.2022.128813.
Zhong, J., L. Feng, and Y.-S. Ong. 2017. “Gene expression programming: A survey.” IEEE Comput. Intell. Mag. 12 (3): 54–72. https://doi.org/10.1109/MCI.2017.2708618.
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Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 12 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 22, 2024
Accepted: Jul 5, 2024
Published online: Sep 27, 2024
Published in print: Dec 1, 2024
Discussion open until: Feb 27, 2025
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