Digital Twin and AI-Integrated Framework for Proactive Risk Prediction and Dynamic Scheduling in Mega Infrastructure Projects
Keywords:
Digital Twin, Artificial Intelligence, Mega Infrastructure Projects, Risk Prediction, Dynamic Scheduling, Hardware-in-the-Loop, Building Information Modeling, Graph Neural Networks, Deep Reinforcement LearningAbstract
Mega infrastructure projects are characterized by tightly coupled activities, heterogeneous information streams, and persistent uncertainty in resources, environment, and stakeholder decisions. This paper develops a Digital Twin and Artificial Intelligence integrated framework (DTAIF) for proactive risk prediction and dynamic schedule adaptation. The architecture combines BIM/IFC information, IoT/edge data ingestion, graph-based dependency representation, temporal forecasting, and constraint-aware schedule optimization. To preserve scientific reproducibility, the revised study does not present unverified field deployments or physical HIL measurements as empirical evidence. Instead, validation is formulated as a transparent computational and virtual-HIL benchmark in which sensor noise, packet delay, missing observations, activity-duration uncertainty, and resource disruptions are injected into the cyber-physical loop. Performance is evaluated using prediction metrics, schedule robustness, constraint violations, and end-to-end computational latency. The resulting manuscript therefore provides a defensible methodology and an implementation-ready validation protocol while clearly separating proposed architecture, computational evidence, and future physical deployment.
References
[1] B. Flyvbjerg, “What You Should Know About Megaprojects and Why: An Overview,” Project Management Journal, 45(2), 6–19, 2014. doi:10.1002/pmj.21409.
[2] V. V. Tuhaise, J. H. M. Tah, and F. H. Abanda, “Technologies for digital twin applications in construction,” Automation in Construction, 152, 104931, 2023. doi:10.1016/j.autcon.2023.104931.
[3] N. A. N. Adu-Amankwa et al., “Digital Twins and Blockchain technologies for building lifecycle management,” Automation in Construction, 155, 105064, 2023. doi:10.1016/j.autcon.2023.105064.
[4] Y. Jiang et al., “Blockchain-enabled digital twin collaboration platform for fit-out operations in modular integrated construction,” Automation in Construction, 148, 104747, 2023. doi:10.1016/j.autcon.2023.104747.
[5] A. Pal, J. J. Lin, S.-H. Hsieh, and M. Golparvar-Fard, “Automated vision-based construction progress monitoring in built environment through digital twin,” Developments in the Built Environment, vol. 16, art. 100247, 2023, doi:10.1016/j.dibe.2023.100247.
[6] Y. Jiang et al., “Digital twin-enabled synchronized construction management: A roadmap from Construction 4.0 towards future prospect,” Developments in the Built Environment, 19, 100512, 2024. doi:10.1016/j.dibe.2024.100512.
[7] X. Wang, Y. Pan, and J. Chen, “Digital twin with data-mechanism-fused model for smart excavation management,” Automation in Construction, 168, 105749, 2024. doi:10.1016/j.autcon.2024.105749.
[8] Y. Yang, M. Li, C. Yu, and R. Y. Zhong, “Digital twin-enabled visibility and traceability for building materials in on-site fit-out construction,” Automation in Construction, 166, 105640, 2024. doi:10.1016/j.autcon.2024.105640.
[9] T. D. Moshood, J. O. B. Rotimi, W. M. Shahzad, and J. A. Bamgbade, “Infrastructure digital twin technology: A new paradigm for future construction industry,” Technology in Society, vol. 77, art. 102519, 2024, doi:10.1016/j.techsoc.2024.102519.
[10] V. K. Reja, M. S. Pradeep, and K. Varghese, “Digital Twins for Construction Project Management (DT-CPM): Applications and Future Research Directions,” Journal of The Institution of Engineers (India): Series A, 105(3), 793–807, 2024. doi:10.1007/s40030-024-00810-8.
[11] M. Wang, M. Ashour, A. Mahdiyar, and S. Sabri, “Opportunities and Threats of Adopting Digital Twin in Construction Projects: A Review,” Buildings, vol. 14, no. 8, art. 2349, 2024, doi:10.3390/buildings14082349.
[12] X. Cheng et al., “A preliminary investigation on enabling digital twin technology for operations and maintenance of urban underground infrastructure,” AI in Civil Engineering, 3, 4, 2024. doi:10.1007/s43503-024-00021-x.
[13] H. Aladağ, İ. Güven, and O. Ballı, “Contribution of artificial intelligence (AI) to construction project management processes: State of the art with scoping review method,” Sigma Journal of Engineering and Natural Sciences, 42(5), 1654–1669, 2024. doi:10.14744/sigma.2024.00125.
[14] S. D. Datta, M. Islam, and M. H. R. Sobuz, “Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review,” Heliyon, 10(5), e26888, 2024. doi:10.1016/j.heliyon.2024.e26888.
[15] H. Liang et al., “An Adaptive Scheduling Method for Infrastructure Construction Progress Based on Digital Twin,” Proc. IEEE INDIN, 2024. doi:10.1109/INDIN58382.2024.10774539.
[16] B. Aktürk and P. Irlayıcı Çakmak, “Digital twins for enhanced construction project management,” Smart and Sustainable Built Environment, 14(7), 2176–2200, 2025. doi:10.1108/SASBE-03-2024-0082.
[17] M. Najafzadeh and A. Yeganeh, “AI-Driven Digital Twins in Industrialized Offsite Construction: A Systematic Review,” Buildings, vol. 15, no. 17, art. 2997, 2025, doi:10.3390/buildings15172997.
[18] ISO 19650-1:2018, Organization and digitization of information about buildings and civil engineering works, including BIM — Information management — Part 1.
[19] ISO 19650-2:2018, Organization and digitization of information about buildings and civil engineering works, including BIM — Information management — Part 2.
[20] ISO 16739-1:2024, Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries — Part 1: Data schema.
[21] buildingSMART International, IFC 4.3 documentation and infrastructure domain resources, accessed 2026.
[22] IEEE Std 1588-2019, IEEE Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems.
[23] OASIS, MQTT Version 5.0, OASIS Standard, 2019.
[24] B. Flyvbjerg and D. W. Bester, “The Cost-Benefit Fallacy: Why Cost-Benefit Analysis Is Broken and How to Fix It,” Journal of Benefit-Cost Analysis, vol. 12, no. 3, pp. 395–419, 2021, doi:10.1017/bca.2021.9.
[25] M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” in Transdisciplinary Perspectives on Complex Systems, Springer, 2017.
[26] R. Sacks, C. Eastman, G. Lee, and P. Teicholz, BIM Handbook: A Guide to Building Information Modeling for Owners, Designers, Engineers, Contractors, and Facility Managers, 3rd ed. Wiley, 2018, doi:10.1002/9781119287568.
[27] Project Management Institute, A Guide to the Project Management Body of Knowledge (PMBOK Guide), 7th ed., PMI, 2021.
[28] ISO 31000:2018, Risk management — Guidelines.
[29] ISO 21502:2020, Project, programme and portfolio management — Guidance on project management.
[30] Y. Pan and L. Zhang, “Integrating BIM and AI for smart construction management: current status and future directions,” Archives of Computational Methods in Engineering, vol. 30, pp. 1081–1110, 2023, doi:10.1007/s11831-022-09830-8.
