Artificial Intelligence–Driven Intelligent Project Management: An Integrated Framework for Planning, Scheduling, and Control in Engineering and Construction Projects
Keywords:
- Artificial intelligence,
- Project planning,
- Project scheduling,
- Project control,
- Construction management,
- Digital transformation
Abstract
The implementation of AI in engineering and construction is transforming project management practices, with sophisticated forecasting, optimization, and real-time decision-support tools. The current studies, however, tend to investigate the applications of AI in the areas of project planning, scheduling, and control independently from each other as functional domains, and only a few studies focus on analytical frameworks that attempt to explain the synergistic effects of these various integrated applications on intelligent project management systems. A structured literature review is used in this study to analyze the latest progress of AI applications in project planning, scheduling, and control. The literature, across engineering, construction management and project management, was analysed and the most prevalent technologies and implementation trends, organizational factors, and performance impacts of AI adoption were identified. Thematic synthesis results are applied in this paper to propose an Integrated Artificial Intelligence Project Management Framework (IAIPMF) that integrates predictive planning, adaptive scheduling, intelligent project control, organizational readiness, and governance mechanisms in one analytical framework. The study contributes theoretically to project management research and provides practical guidance for organizations implementing AI-enabled project management systems.
Downloads
References
Ashif M, Mahjabeen F (2023) Revolutionizing solar energy: The impact of artificial intelligence on photovoltaic systems. Int J Multidiscip Sci Arts 2: 117-127.
Niederman F (2021) Project management: Openings for disruption from AI and advanced analytics. Inf Technol People 34:1570-1599.
Rane N (2023) Role of ChatGPT and similar generative Artificial Intelligence (AI) in construction industry. SSRN.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4598258
Taboada I, Daneshpajouh A, Toledo N, Vass TDe (2023) Artificial intelligence enabled project management: A systematic literature review. Appl Sci 13: 5014.
https://www.mdpi.com/2076-3417/13/8/5014
Victor NOC (2023) The application of artificial intelligence for construction project planning. J Adv Artif Intell 1: 67-95.
https://www.jaai.net/content-173-17-1.html
Shoushtari F, Daghighi A, Ghafourian E (2024) Application of artificial intelligence in project management. Int J Ind Eng Oper Res 6: 49-63.
http://bgsiran.ir/journal/ojs-3.1.1-4/index.php/IJIEOR/article/view/89
Kitchenham BA, Budgen D, Brereton P (2015) Evidence-based software engineering and systematic reviews.
Hashfi MI, Raharjo T (2023) Exploring the challenges and impacts of artificial intelligence implementation in project management: A systematic literature review. Int J Adv Comput Sci Appl 14.
https://pdfs.semanticscholar.org/34e9/9ed58c78ca681a4a374217a2e1bf51892a2b.pdf
Fridgeirsson TV, Ingason HT, Jonasson HI, Gunnarsdottir H (2023) A qualitative study on artificial intelligence and its impact on the project schedule, cost and risk management knowledge areas as presented in PMBOK®. Appl Sci 13: 11081.
https://www.mdpi.com/2076-3417/13/19/11081
Braun V, Clarke V (2021) Thematic analysis: A practical guide.
Hossain MZ, Hasan L, Dewan MA, Monira NA (2024) The impact of artificial intelligence on project management efficiency. Int J Manag Inf Syst Data Sci 1: 1-17.
Marzouk M, Enaba M (2021) Artificial intelligence-based risk assessment model for construction projects. Eng Constr Archit Manag 28: 23-44.
Kim SY, Nguyen LD, Luu VT (2022) Machine learning-based cost estimation model for construction projects. Eng Constr Archit Manag 29: 1023-1042.
Cheng MY, Cao MT, Wu YW (2020) Predicting project success using artificial intelligence: A hybrid deep learning approach. Auto Constr 119: 103316.
Zhang Y, Teizer J, Lee JK, Eastman CM, Venugopal M (2023) Artificial Intelligence and optimization for resource planning in construction projects. Eng Constr Archit Manag 30: 2489-2510.
Wang J, Zhang X, Lu W (2021) Dynamic construction scheduling using reinforcement learning. Auto Constr 124: 103556.
El-Sayegh SM, Manjikian S, Ibrahim A, Abouelyousr A, Jabbour R (2022) Risk identification and assessment in construction projects using machine learning. J Constr Eng Manag 148: 04022019.
Li H, Guo H, Skitmore M, Huang T (2023) Digital twin and artificial intelligence integration for construction scheduling and control. Auto Constr 148 104780.
Olawumi TO, Chan DWM (2020) Artificial intelligence-based decision support systems in construction project management. J Eng Design Technol 18: 1011-1030.
Moselhi O, Bardareh H, Zhu Z (2021) Automated data-driven project control using artificial intelligence. J Comput Civ Eng 35: 04020063.
Sacks R, Girolami M, Brilakis I (2020) Building information modelling, artificial intelligence and construction management: A critical review. Dev Built Environ 4: 100011.
https://www.sciencedirect.com/science/article/pii/S2666165920300077
Too EG, Weaver P (2020) The management of project management: A conceptual framework for project governance. Int J Proj Manag 32: 1382-1394.
https://www.sciencedirect.com/science/article/abs/pii/S026378631300094X
Berssaneti FT, Carvalho MM (2021) Identification of variables that impact project success in Brazilian companies. Int J Proj Manag 33: 638-649.
https://www.sciencedirect.com/science/article/abs/pii/S0263786314001203
