Mechanical and Civil Engineering Faculty Publications

Document Type

Article

Publication Title

Integrated Construction Risk Assessment Framework: AI, BIM, Digital Twin and Lean Strategies for Lifecycle Decision Support

Abstract

An integrated risk assessment architecture for construction projects synthesizes data governance, BIM, Digital Twins, and hybrid analytics into a continuous feedback loop that links sensing, semantic modelling, probabilistic simulation, and organizational learning. The framework distinguishes aleatory from cognitive uncertainty and prescribes layered methods: Monte Carlo and Bayesian updating for measurable variability; fuzzy inference and consensus aggregation for linguistic and expert judgements; and graph-based discovery for dynamic cyber-physical topologies represented as time-varying graphs 𝐺𝑡. Governance primitives, aligned with ISO 19650, enforce provenance, interoperability, and staged readiness thresholds that prevent premature prescriptive outputs until model convergence criteria are met. BIM and GIS act as semantic convergence spaces that host multi-criteria indicators, support 4D/5D sequencing, and feed Digital Twins for real-time compliance monitoring (e.g., ISO 14644-1 cases). AI modules operate within hybrid reasoning pipelines, combining explainable ML, SHAP-like interpretability, and probabilistic graphical models to produce transparent forecasts for schedule, cost, safety, and lifecycle environmental metrics. Operational design includes a ten-phase process from scoping and data fusion to intervention monitoring and maturity scaling, with lean and decentralized scheduling practices embedded to translate analytics into low-variance interventions. Tool selection guidance is maturity-sensitive: prioritize governance and CDEs before advanced Twins or autonomous controls and prefer continuous-discovery graph engines for cyber-physical infrastructures. Validation strategies combine PRISMA-style evidence synthesis, BIM-derived case studies benchmarked against CPM/PERT and Monte Carlo baselines, and expert evaluations using structured Likert measures. The resulting architecture aims to reconceptualize risk management as an adaptive socio-technical routine that integrates models, standards, and human learning for resilient project delivery.

First Page

Aigboduwa, J. (2025). Intelligent risk management and decision-making in complex offshore engineering projects. International Journal of Petroleum and Gas Engineering Research, 8(2). https://doi.org/10.37745/ijpger.17/vol8n24481 Amro, S. O. A., Naimi, S., & Ibrahim, A. A. (2026). AI-driven optimization of scheduling, risk, and sustainability in BIM-enabled construction project management. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-026-11351-6 Baiardi, F., & Sammartino, V. (2026). Simulation-powered cybersecurity: Real-time risk assessment via non-intrusive security twin. The Journal of Supercomputing, 82(5). https://doi.org/10.1007/s11227-026-08454-0 Bakhsh, M. M., Alam, G. T., & Nadia, N. Y. (2025). Adapting agile methodologies to incorporate digital twins in sprint planning, backlog refinement, and QA validation. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (Online), 4(2). https://doi.org/10.60087/jklst.v4.n2.006 Bilgin, G., Dikmen, I., Birgonul, M. T., & Ozorhon, B. (2022). A decision support system for project portfolio management in construction companies. International Journal of Information Technology &Amp; Decision Making, 22(02). https://doi.org/10.1142/s0219622022500821 Coskun, C., Dikmen, I., & Birgonul, M. T. (2023). Sustainability risk assessment in mega construction projects. Built Environment Project and Asset Management, 13(5). https://doi.org/10.1108/bepam-10-2022-0153 Denny, M., & Myneni, K. K. (2025). An integrated risk management framework for construction and operational phases of hyperscale data centres in india. International

Last Page

Opara, I. J., Lateef, J., Nii-Okai, E., Saah, B. P., Mensah, E. K., Wiafe, G. F. O., & Olayode, A. (2025). Digital resilience in construction projects: A narrative review of data governance, BIM, and real-time decision support systems. Journal of Management, and Development Research, 2(2). https://doi.org/10.69739/jmdr.v2i2.1129 Otoko, J. (2023). Optimizing cost, time, and contamination control in cleanroom construction using advanced bim, digital twin, and ai-driven project management solutions. World Journal of Advanced Research and Reviews, 19(2). https://doi.org/10.30574/wjarr.2023.19.2.1570 Patrisia, Y., Law, D. W., Gunasekara, C., & Setunge, S. (2025). Assessment of waste integrated concrete products: A cradle-to-cradle perspective. The International Journal of Life Cycle Assessment, 30(5). https://doi.org/10.1007/s11367-025-02443-w Sahani, S. K., Lee, T.-F., Pandey, D., Pandey, B. K., Sah, B. K., & Jha, R. (2026). Hybrid analytical, numerical, and machine learning approaches on decision-making, risk management, operational stability, and uncertainty analysis in nepalese management systems. Journal of Intelligent Decision Making and Information Science, 3(3s). Sajuyigbe, A. S., Ogundare, O. S., Sanusi, B. M., Akinbobola, A. O., Tella, A. R., & Obi, N. J. (2026). Assessing the mediating role of project management training on the nexus between job satisfaction and project performance in the nigerian construction industry. JCBM, 8(2). Savaş, S. (2025). Artificial intelligence in construction project management: Trends, challenges and future directions. Journal of Design for Resilience in Architecture and Planning, 6(2). https://doi.org/10.47818/drarch.2025.v6i2165 Varolgüneş, S. (2025). Mapping two decades of construction risk management: A scientometric study of global trends, gaps, and thematic evolution. Black Sea Journal of Engineering and Science, 8(6). https://doi.org/10.34248/bsengineering.1734348 Yang, Y., Zhang, J., & Zhang, R. (2025). Integration of BIM and emerging information technologies: Paths and challenges for driving intelligent construction management. Transactions on Engineering and Technology Research, 5. https://doi.org/10.62051/dn50pk33 Yu, H. (2025). A digital twin-based system for full-lifecycle safety management and dynamic risk assessment in nuclear power plants. Discover Artificial Intelligence, 5(1). https://doi.org/10.1007/s44163-025-00618-w Лялюк, О. Г., Осипенко, Р. С., & Мельник, Д. О. (2024). СИСТЕМИ ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕНЬ в БУДІВЕЛЬНИХ ПРОЕКТАХ НА ОСНОВІ МЕРЕЖ БАЙЄСА. НАУКОВО-ТЕХНІЧНИЙ ЖУРНАЛ “СУЧАСНІ ТЕХНОЛОГІЇ, МАТЕРІАЛИ І КОНСТРУКЦІЇ В БУДІВНИЦТВІ”. https://doi.org/10.31649/2311-1429-2024-1-96-101

Publication Date

Summer 7-27-2026

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