EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 354 (2026) EPJ Web Conf., 354 (2026) 02002 References
Open Access
Issue
EPJ Web Conf.
Volume 354, 2026
19th Global Congress on Manufacturing and Management (GCMM 2025)
Article Number 02002
Number of page(s) 25
Section Artificial Intelligence, Machine Learning, and Intelligent Decision Systems
DOI https://doi.org/10.1051/epjconf/202635402002
Published online 02 March 2026
  1. B. Flyvbjerg, M. K. Skamrisholm, and S. L. Buhl, "How common and how large are cost overruns in transport infrastructure projects?," Transp Rev, vol. 23, no. 1, pp. 71–88, Jan. 2003, doi: 10.1080/01441640309904. [Google Scholar]
  2. H. Doloi, "Cost Overruns and Failure in Project Management: Understanding the Roles of Key Stakeholders in Construction Projects," J Constr Eng Manag, vol. 139, no. 3, pp. 267–279, Mar. 2013, doi: 10.1061/(ASCE)CO.1943-7862.0000621. [Google Scholar]
  3. P. E. D. Love, D. D. Ahiaga-Dagbui, and Z. Irani, "Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation," Transp Res Part A Policy Pract, vol. 92, pp. 184–194, Oct. 2016, doi: 10.1016/j.tra.2016.08.007. [Google Scholar]
  4. L. Breiman, "Random Forests," Mach Learn, vol. 45, pp. 5–32, 2001. [CrossRef] [Google Scholar]
  5. S. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," Nov. 2017. [Google Scholar]
  6. I. T. Jolliffe and J. Cadima, "Principal component analysis: a review and recent developments," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, p. 20150202, Apr. 2016, doi: 10.1098/rsta.2015.0202. [Google Scholar]
  7. J. H. Jung, D. Y. Kim, and H. K. Lee, "The computer-based contingency estimation through analysis cost overrun risk of public construction project," KSCE Journal of Civil Engineering, vol. 20, no. 4, pp. 1119–1130, May 2016, doi: 10.1007/s12205-015-0184-8. [Google Scholar]
  8. J. Theodore J. Trauner and William A. Manginelli, Construction Delays. Elsevier, 2009. doi: 10.1016/B978-1-85617-677-4.X0001-3. [Google Scholar]
  9. A. Kavuma, J. Ock, and H. Jang, "Factors influencing Time and Cost Overruns on Freeform Construction Projects," KSCE Journal of Civil Engineering, vol. 23, no. 4, pp. 1442–1450, Apr. 2019, doi: 10.1007/s12205-019-0447-x. [Google Scholar]
  10. S. Durdyev, "Review of construction journals on causes of project cost overruns," Engineering, Construction and Architectural Management, vol. 28, no. 4, pp. 1241–1260, Apr. 2021, doi: 10.1108/ECAM-02-2020-0137. [Google Scholar]
  11. Mostafa H. Kotb and Mohamed M. Ghattas, "Risk Identification Barriers in Construction Projects in MENA," PM World Journal, vol. 6, no. 8, Aug. 2018. [Google Scholar]
  12. S. Shoar, N. Chileshe, and J. D. Edwards, "Machine learning-aided engineering services' cost overruns prediction in high-rise residential building projects: Application of random forest regression," Journal of Building Engineering, vol. 50, p. 104102, Jun. 2022, doi: 10.1016/j.jobe.2022.104102. [Google Scholar]
  13. M. Tayyab, M. Furkhan, M. Rizwan, M. Jameel, and A. Chadee, "A Study on Factors Influencing Cost Overrun in High-rise Building Construction across India," Journal of Smart Buildings and Construction Technology, vol. 5, no. 1, pp. 52–83, May 2023, doi: 10.30564/jsbct.v5i1.5489. [Google Scholar]
  14. G. Kazar, U. Yigit, and K. E. Boyabatli, "Predicting maintenance cost overruns in public school buildings using a rough topological approach," Autom Constr, vol. 168, p. 105810, Dec. 2024, doi: 10.1016/j.autcon.2024.105810. [Google Scholar]
  15. G. H. Coffie and S. K. F. Cudjoe, "Toward predictive modelling of construction cost overruns using support vector machine techniques," Cogent Eng, vol. 10, no. 2, Dec. 2023, doi: 10.1080/23311916.2023.2269656. [Google Scholar]
  16. M. M. Hamdan, M. Thneibat, and K. Hyari, "Predicting cost overrun in construction projects using machine learning algorithms: the case of Jordan," Engineering, Construction and Architectural Management, Apr. 2025, doi: 10.1108/ECAM-09-2024-1209. [Google Scholar]
  17. A. H. Turkyilmaz and G. Polat, "Risk-Based Completion Cost Overrun Ratio Estimation in Construction Projects Using Machine Learning Classification Algorithms: A Case Study," Buildings, vol. 14, no. 11, p. 3541, Nov. 2024, doi: 10.3390/buildings14113541. [Google Scholar]
  18. Theingi Aung, S. R. Liana, A. Htet, and Amiya Bhaumik, "Using Machine Learning to Predict Cost Overruns in Construction Projects," Journal of Technology Innovations and Energy, vol. 2, no. 2, pp. 1–7, Jun. 2023, doi: 10.56556/jtie.v2i2.511. [CrossRef] [Google Scholar]
  19. A. A. Aibinu and G. O. Jagboro, "The effects of construction delays on project delivery in Nigerian construction industry," International Journal of Project Management, vol. 20, no. 8, pp. 593–599, Nov. 2002, doi: 10.1016/S0263-7863(02)00028-5. [Google Scholar]
  20. P. F. Kaming, P. O. Olomolaiye, G. D. Holt, and F. C. Harris, "Factors influencing construction time and cost overruns on high-rise projects in Indonesia," Construction Management and Economics, vol. 15, no. 1, pp. 83–94, Jan. 1997, doi: 10.1080/014461997373132. [Google Scholar]
  21. Y. A. Olawale and M. Sun, "Cost and time control of construction projects: inhibiting factors and mitigating measures in practice," Construction Management and Economics, vol. 28, no. 5, pp. 509–526, May 2010, doi: 10.1080/01446191003674519. [Google Scholar]
  22. J. Zhou, X. Shi, K. Du, X. Qiu, X. Li, and H. S. Mitri, "Feasibility of Random -Forest Approach for Prediction of Ground Settlements Induced by the Construction of a Shield-Driven Tunnel," International Journal of Geomechanics, vol. 17, no. 6, Jun. 2017, doi: 10.1061/(ASCE)GM.1943-5622.0000817. [Google Scholar]
  23. X. Liu, Y. Song, W. Yi, X. Wang, and J. Zhu, "Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity," J Constr Eng Manag, vol. 144, no. 6, Jun. 2018, doi: 10.1061/(ASCE)CO.1943-7862.0001495. [Google Scholar]
  24. Y. Zhang et al., "How Does Experience with Delay Shape Managers' Making-Do Decision: Random Forest Approach," Journal of Management in Engineering, vol. 36, no. 4, Jul. 2020, doi: 10.1061/(ASCE)ME.1943-5479.0000776. [Google Scholar]
  25. T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22ndACMSIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA: ACM, Aug. 2016, pp. 785–794. doi: 10.1145/2939672.2939785. [Google Scholar]
  26. L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, "CatBoost: unbiased boosting with categorical features," Jan. 2019. [Google Scholar]
  27. D. Chicco, M. J. Warrens, and G. Jurman, "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation," PeerJ Comput Sci, vol. 7, p. e623, Jul. 2021, doi: 10.7717/peerj-cs.623. [Google Scholar]
  28. A. M. Jarkas and C. G. Bitar, "Factors Affecting Construction Labor Productivity in Kuwait," J Constr Eng Manag, vol. 138, no. 7, pp. 811–820, Jul. 2012, doi: 10.1061/(ASCE)CO.1943-7862.0000501. [Google Scholar]
  29. K. Koc, Ö. Ekmekcioglu, and A. P. Gurgun, "Accident prediction in construction using hybrid wavelet-machine learning," Autom Constr, vol. 133, p. 103987, Jan. 2022, doi: 10.1016/j.autcon.2021.103987. [Google Scholar]
  30. S. Shoar, T. W. Yiu, S. Payan, and M. Parchamijalal, "Modeling cost overrun in building construction projects using the interpretive structural modeling approach: a developing country perspective," Engineering, Construction and Architectural Management, vol. 30, no. 2, pp. 365–392, Mar. 2023, doi: 10.1108/ECAM-08-2021-0732 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.