Artificial intelligence-based risk management in construction projects: A framework for project-oriented organizations.

Document Type : Original Article

Authors
Vali Zivari Afzal 1
Mehrdad Karimimoshaver 2
1 Master of Architectural Technology, Digital Architectur, Department of Architecture, Faculty of Art and Architecture, Bu-Ali Sina University, Hamadan, Iran.
2 Department of Architecture, Faculty of Art and Architecture, Bu-Ali Sina University, Hamadan, Iran.
10.22034/he.2025.526968.1110
Abstract
In the era of new technologies, the challenges of risk management in construction projects have gained special importance due to the complexity and multidimensionality of risks. Therefore, this research was conducted using a developmental-applied method with a qualitative approach and based on the Delphi method with the aim of designing a comprehensive framework for risk management in construction projects using artificial intelligence (AI) in project-oriented organizations. In the first step, the initial components were extracted through a systematic literature review and content analysis of previous studies, and were modified and finalized by collecting the views of 15 experts in the field of project management and information technology. The final results of the research led to the presentation of a conceptual model with 54 main components in three important layers, in the final core (central) of which the four main risk management processes are located, in the middle layer the three supporting dimensions (technological, organizational, legal) and in the outer layer the two interactive components (stakeholder culture and supply chain management). This framework, by integrating artificial intelligence algorithms, enables real-time analysis, intelligent prediction, and adaptive response to various risks. Research findings show that the use of artificial intelligence in risk management, in addition to increasing decision-making accuracy, will lead to a strategic, systemic, and integrated improvement of risk management processes and will significantly reduce project delays and unexpected costs.

Keywords

Subjects

1.      Abioye, S.O.; Oyedele, L.O.; Akanbi, L.; Ajayi, A.; Manuel, J.; Delgado, D.; Bilal, M.; Akinade, O.O.; Ahmed, A.(2021). Artificial Intelligence in the Construction Industry: A Review of Present Status, Opportunities and Future Challenges. J. Journal of Building Engineering., Volume 44, 103299. https://doi.org/10.1016/j.jobe.2021.103299.
2.      Baum,S., Barrett, S., Yampolskiy, R.V.(2017). Modeling and interpreting expert disagreement about artificial superintelligence, Informatica 41 (7) ., PP 419–428.
3.      Chenya, L., Aminudin, E..,  Mohd., S., and Yap, L. S. (2022) Intelligent risk management in construction projects: Systematic literature review. IEEE Access, 10 (NA). pp. 72936-72954. ISSN 2169-3536 .
4.      Esmaeili, B.; Hallowell, M. Attribute-Based Safety Risk Assessment I: Analysis at the Fundamental Level. J. Constr. Eng. Manag. Vol 14. Issue 8, 141, 15–21. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000980.
5.      Goertzel, B., Wang, P.,.(2007).  A foundational architecture for artificial general intelligence, Advances in Artificial General Intelligence: Concepts, Architect. Algorithms 6 , 36.  PP 22-36.
6.      Guzman-Urbina , A., Aoyama, A., Choi, E.A (2018). Polynomial Neural Network Approach for Improving Risk Assessment and Industrial Safety. ICIC Express Lett., 12, 97–107.
7.      Hola, B.; Szostak, M.( 2017). Methodology of Analysing the Accident Rate in the Construction Industry. Procedia Engineering., Vol 172, PP 355–362. https://doi.org/10.1016/j.proeng.2017.02.040.
8.      Khodabakhshian, A., & Re Cecconi, F. (2022). Data-driven process mining framework for risk management in construction projects. IOP Conference Series: Earth and Environmental Science, 1101. DOI 10.1088/1755-1315/1101/3/032023.
9.      Li, J.; Wang, J.; Xu, N.; Hu, Y.; Cui, C. Importance Degree Research of Safety Risk Management Processes of Urban Rail Transit Based on Text Mining Method. Information 2018, 9, 26. ; doi:10.3390/info9020026.
10.   Mellit, A.; Kalogirou, S.A.(2008). Artificial Intelligence Techniques for Photovoltaic Applications: A Review. Prog. Energy Combust. Sci. 2008, 34, 574–632.
11.   Neeraj, S., & Balasubramanian, M. (2015). Assessment of Risk in Construction Industry. International Research Journal of Engineering and Technology (IRJET). Volume: 02 Issue: 01. PP 68-72.
12.   Project Management Institute (PMI),.(2017). A Guide to the Project Management Body of Knowledge (PMBOK Guide), 6th ed.; Project Management Institute, Inc.: Newtown Square, PA, USA.
13.   Sārtaputna, S., & Zvaigzne, A. (2018). Risks and Risk Management Alternatives for Building Construction industry enterprises in latvia: The results of an expert survey. Latgale National Economy Research. Vol. 1 No. 10. DOI: https://doi.org/10.17770/lner2018vol1.10.3599.
14.   Thabit, T.H., & Younus, S.Q. (2018). Risk Assessment and Management in Construction Industries. International Journal of Research and Engineering, 5(2),  PP 315-320. DOI:10.21276/ijre.2018.5.2.3.
15.   Wu, D.D.; Chen, S.H.; Olson, D.L.(2014). Business Intelligence in Risk Management: Some Recent Progresses. Information Sciences., vol 256, pp 1–7. https://doi.org/10.1016/j.ins.2013.10.008.
16.   Zhong, R.Y.; Xu, X.; Klotz, E.; Newman, S.T. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 3, 616–630.
Human Ecology
Volume 4, Issue 12
Autumn 2025
Pages 1183-1194