Integrating Monte Carlo simulation and Digital Twin technology for advanced risk management in fast-track infrastructure projects
DOI: https://doi.org/10.3846/jcem.2026.26549Abstract
Fast-tracked infrastructure projects are prone to unforeseen factors that can disrupt commissioning schedules, due to quicker timelines, concurrent work, and changing site conditions. Existing risk analysis techniques have very limited flexibility to keep pace with real-time changes and do not sufficiently address the dynamic nature of interdependent activities within the project. The study aims to bridge this gap by formulating an integrated risk management mechanism using Monte Carlo Simulation (MCS) and a Digital Twin (DT), along with a tailored optimization module. The framework describes the risks associated with multiple work package overlaps while calibrating performance forecasts via DT’s feedback loop. MCS is used to simulate uncertainty and monitor cost-time impacts, while the optimization module helps to find the least detrimental overlap arrangement. Continuous field data sync with the simulation model enables proactive, data-driven decisions that improve situational awareness in the DT environment. Real project conditions show this MCS/DT approach improves prediction accuracy, reduces risk, and aids change during execution. The proposed framework serves as a pragmatic, adaptive risk management tool for fast-track projects while enhancing the integrity and resilience of the parent project as a whole.
Keywords:
Monte Carlo simulation, Digital Twin technology, overlapping risks, infrastructure, economicsHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abd El-Karim, M. S. B. A., Mosa El Nawawy, O. A., & Abdel-Alim, A. M. (2017). Identification and assessment of risk factors affecting construction projects. HBRC Journal, 13(2), 202–216. https://doi.org/10.1016/j.hbrcj.2015.05.001
Acebes, F., Pajares, J., Galán, J. M., & López-Paredes, A. (2014). A new approach for project control under uncertainty. International Journal of Project Management, 32(3), 423–434. https://doi.org/10.1016/j.ijproman.2013.08.003
Alnuaimi, A. S., Taha, R. A., Al Mohsin, M., & Al-Harthi, A. S. (2010). Causes, effects, benefits, and remedies of change orders on public construction projects in Oman. Journal of Construction Engineering and Management, 136(5), 615–622. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000154
AlSehaimi, A., Koskela, L., & Tzortzopoulos, P. (2013). Need for alternative research approaches in construction management: Case of delay studies. Journal of Management in Engineering, 29(4), 407–418. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000148
AlTalhoni, A., Liu, H., & Abudayyeh, O. (2024). Forecasting construction cost indices: Methods, trends, and influential factors. Buildings, 14(10), Article 3272. https://doi.org/10.3390/buildings14103272
Aziz, R. F. (2013). Ranking of delay factors in construction projects after the Egyptian revolution. Alexandria Engineering Journal, 52(3), 387–406. https://doi.org/10.1016/j.aej.2013.03.002
Baker, S., Ponniah, D., & Smith, S. (1999). Risk response techniques employed currently for major projects. Construction Management and Economics, 17(2), 205–213. https://doi.org/10.1080/014461999371709
Beck, J. C., & Wilson, N. (2007). Proactive algorithms for job shop scheduling with probabilistic durations. Journal of Artificial Intelligence Research, 28, 183–232. https://doi.org/10.1613/jair.2080
Ben-David, I., & Raz, T. (2001). An integrated approach for risk response development in project planning. Journal of the Operational Research Society, 52(1), 14–25. https://doi.org/10.1057/palgrave.jors.2601029
Bickel, J. E. (2021). Risk assessment optimization using hybrid methods. Risk Analysis, 41(5), 870–884. https://doi.org/10.1111/risa.13749
Bogus, S. M., Garrido Martins, C., & Valentin, V. (2023). Quantitative risk assessment model and optimization in infrastructure fast-track construction projects. Infrastructures, 8(4), Article 78. https://doi.org/10.3390/infrastructures8040078
Canesi, R., & Gallo, B. (2023). Risk assessment in sustainable infrastructure development projects: A tool for mitigating cost overruns. Land, 13(1), Article 41. https://doi.org/10.3390/land13010041
Chen, L., & Bai, Q. (2019). Optimization in decision making in infrastructure asset management: A review. Applied Sciences, 9(7), Article 1380. https://doi.org/10.3390/app9071380
Cho, Y. (2019). Systematic improvement for effective operation of long-term continuous construction contracts. Korean Journal of Construction Engineering and Management, 20(6), 3–10. https://doi.org/10.6106/KJCEM.2019.20.6.003
Chou, J. S. (2011). Cost simulation in an item-based project involving construction engineering and management. International Journal of Project Management, 29(6), 706–717. https://doi.org/10.1016/j.ijproman.2010.07.010
Dehghan, R., Hazini, K., & Ruwanpura, J. (2015). Optimization of overlapping activities in the design phase of construction projects. Automation in Construction, 59, 81–95. https://doi.org/10.1016/j.autcon.2015.08.004
Del Cano, A., & de la Cruz, M. P. (2002). Integrated methodology for project risk management. Journal of Construction Engineering and Management, 128(6), 473–485. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:6(473)
Dey, P. K. (2012). Project risk management using multiple criteria decision-making technique and decision tree analysis: A case study of Indian oil refinery. Production Planning & Control, 23(11), 903–921. https://doi.org/10.1080/09537287.2011.586379
Dey, P. K., & Ogunlana, S. O. (2004). Selection and application of risk management tools and techniques for build-operate-transfer projects. Industrial Management & Data Systems, 104(4), 334–346. https://doi.org/10.1108/02635570410530748
Doloi, H. K. (2011). Understanding stakeholders’ perspective of cost estimation in project management. International Journal of Project Management, 29(5), 622–636. https://doi.org/10.1016/j.ijproman.2010.06.001
Egbelakin, T., Ogunmakinde, O. E., Teshich, B., & Omotayo, T. (2021). Managing fast-track construction project in Qatar: Challenges and opportunities. Buildings, 11(12), Article 640. https://doi.org/10.3390/buildings11120640
Fazio, P., Moselhi, O., Théberge, P., & Revay, S. (1988). Fast-tracking of construction projects: A case study. Canadian Journal of Civil Engineering, 15(4), 493–499. https://doi.org/10.1139/l88-068
Feist, B. E., Buhle, E. R., Baldwin, D. H., Spromberg, J. A., Damm, S. E., Davis, J. W., & Scholz, N. L. (2017). Roads to ruin: Conservation threats to a sentinel species across an urban gradient. Ecological Applications, 27(8), 2382–2396. https://doi.org/10.1002/eap.1615
Garrido Martins, C., Bogus, S. M., & Valentin, V. (2023a). Optimization approaches for fast-track construction projects. Infrastructures, 8(4), Article 78. https://doi.org/10.3390/infrastructures8040078
Garrido Martins, C., Bogus, S. M., & Valentin, V. (2023b). Quantitative risk assessment model and optimization in infrastructure fast-track construction projects. Infrastructures, 8(4), Article 78. https://doi.org/10.3390/infrastructures8040078
Guan, L., Abbasi, A., & Ryan, M. J. (2021). A simulation-based risk interdependency network model for project risk assessment. Decision Support Systems, 148, Article 113602. https://doi.org/10.1016/j.dss.2021.113602
Hillson, D. (2002). Extending the risk process to manage opportunities. International Journal of Project Management, 20(3), 235–240. https://doi.org/10.1016/S0263-7863(01)00074-6
Ibrahim, A., Zayed, T., & Lafhaj, Z. (2024). Enhancing construction performance: A critical review of performance measurement practices at the project level. Buildings, 14(7), Article 1988. https://doi.org/10.3390/buildings14071988
Islam, M. S., Nepal, M. P., & Skitmore, M. (2017). Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects. Advances in Engineering Informatics, 33, 112–131. https://doi.org/10.1016/j.aei.2017.06.001
Joslin, R., & Müller, R. (2016). The impact of project methodologies on project success in different project environments. International Journal of Project Management, 34(4), 626–638. https://doi.org/10.1016/j.ijproman.2016.01.008
Jupally, S. P., Yalamati, S., & Jupally, A. (2024). Driving efficiency and success: The role of release management in project timelines, cost budgeting, and risk assessment. International Journal of Computer Engineering and Technology, 15(4), 1–11.
Kasap, D., & Kaymak, M. (2007). Risk identification step of the project risk management process. In Proceedings of the Portland International Center for Management of Engineering and Technology (PICMET) (pp. 2116–2120), Portland, OR, USA. IEEE. https://doi.org/10.1109/PICMET.2007.4349543
Kimiagari, S., & Keivanpour, S. (2019). An interactive risk visualisation tool for large-scale and complex engineering and construction projects under uncertainty and interdependence. International Journal of Production Research, 57(21), 6827–6855. https://doi.org/10.1080/00207543.2018.1503426
Kumar, C., & Yadav, D. K. (2015). A probabilistic software risk assessment and estimation model for software projects. Procedia Computer Science, 54, 353–361. https://doi.org/10.1016/j.procs.2015.06.041
Laryea, S., & Watermeyer, R. (2020). Managing uncertainty in fast-track construction projects: Case study from South Africa. Proceedings of the Institution of Civil Engineers – Management, Procurement and Law, 173(2), 49–63. https://doi.org/10.1680/jmapl.19.00039
Lee, K.-W. (2024). Cost performance comparison of road construction projects considering bidding condition and project characteristics. Sustainability, 16(22), Article 10083. https://doi.org/10.3390/su162210083
Ling, F. Y. Y. (2018). International comparison of performance of public projects. Built Environment Project and Asset Management, 8(3), 281–292. https://doi.org/10.1108/BEPAM-08-2017-0059
Lu, Q., Xie, X., Parlikad, A. K., & Schooling, J. (2020). Digital twin-enabled asset management: A case study of an airport terminal. Automation in Construction, 118, Article 103278. https://doi.org/10.1016/j.autcon.2020.103278
Mali, A. S., Kolhe, A., Gorde, P., Kolekar, A., Umbrajkar, A., Solepatil, S., & Zare, K. (2025). Application of artificial intelligence and machine learning in construction project management: A comparative study of predictive models. Asian Journal of Civil Engineering, 26(6), 2671–2686. https://doi.org/10.1007/s42107-025-01335-6
Martins, C. G., Bogus, S. M., & Valentin, V. (2023). Perceptions of construction risks due to fast-track activity overlapping. Engineering, 4(4), 2879–2895. https://doi.org/10.3390/eng4040162
Mohammadipour, F., & Sadjadi, S. J. (2016). Project cost–quality–risk tradeoff analysis in a time-constrained problem. Computers & Industrial Engineering, 95, 111–121. https://doi.org/10.1016/j.cie.2016.02.025
Nasirzadeh, F., Afshar, A., Khanzadi, M., & Howick, S. (2008). Integrating system dynamics and fuzzy logic modelling for construction risk management. Construction Management and Economics, 26(11), 1197–1212. https://doi.org/10.1080/01446190802459924
Oke, A. E., & Ugoje, O. F. (2013). Assessment of rework cost of selected building projects in Nigeria. International Journal of Quality & Reliability Management, 30(7), 799–810. https://doi.org/10.1108/IJQRM-Jul-2011-0103
Pfeifer, J., Barker, K., & Ramirez-Marquez, J. E. (2015). Quantifying the risk of project delays with a genetic algorithm. International Journal of Production Economics, 170, 34–44. https://doi.org/10.1016/j.ijpe.2015.09.007
Rasul, N., Malik, M. S. A., Bakhtawar, B., & Thaheem, M. J. (2021). Risk assessment of fast-track projects: A systems-based approach. International Journal of Construction Management, 21(11), 1099–1114. https://doi.org/10.1080/15623599.2019.1602587
Senić, A., Ivanović, M., Dobrodolac, M., & Stojadinović, Z. (2025). Prioritization of preventive measures: A multi-criteria approach to risk mitigation in road infrastructure projects. Mathematics, 13(2), Article 278. https://doi.org/10.3390/math13020278
Shanmugapriya, S., & Subramanian, K. (2015). Structural equation model to investigate the factors influencing quality performance in Indian construction projects. Sādhanā, 40(6), 1975–1987. https://doi.org/10.1007/s12046-015-0421-3
Tang, S. L., & Qiang, M. (2007). Risk management in the Chinese construction industry. Journal of Construction Engineering and Management, 133(10), 1035–1043. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:12(944)
Wanjari, S. P., & Dobariya, G. (2016). Identifying factors causing cost overrun of construction projects in India. Sādhanā, 41(6), 679–693. https://doi.org/10.1007/s12046-016-0498-3
Ward, S., & Chapman, C. (2003). Transforming project risk management into project uncertainty management. International Journal of Project Management, 21(2), 97–105. https://doi.org/10.1016/S0263-7863(01)00080-1
Yildiz, A., & Dikmen, I. (2021). Advanced risk assessment techniques for fast-track construction. Automation in Construction, 126, Article 103672. https://doi.org/10.1016/j.autcon.2021.103672
Zhang, J., & Cai, M. (2011). Prioritizing highway tunnel risk factors with AHP method. In Proceedings of the International Conference on Information Science and Technology (ICIST) (pp. 1205–1207), Nanjing. IEEE. https://doi.org/10.1109/ICIST.2011.5765186
Zhang, Y., Xing, X., Antwi-Afari, M. F., & Wu, M. (2022). Safety risk estimation of construction project based on energy transfer model and system dynamics: A case study of collapse accident in China. International Journal of Environmental Research and Public Health, 19(21), Article 14386. https://doi.org/10.3390/ijerph192114386
Zhao, X., & Gao, Y. (2016). A fuzzy synthetic evaluation approach for risk assessment: A case of Singapore’s green projects. Journal of Cleaner Production, 115, 203–213. https://doi.org/10.1016/j.jclepro.2015.11.042
Zou, P. X., Zhang, G., & Wang, J. Y. (2007). Identifying key risks in construction projects: Life cycle and stakeholder perspectives. International Journal of Project Management, 25(6), 601–614. https://doi.org/10.1016/j.ijproman.2007.03.001
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.