EDITORIAL: INTEGRATION OF DIGITAL TWIN TECHNOLOGY IN CONSTRUCTION PROJECT MANAGEMENT – OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.33736/jcest.13899.2026Keywords:
digital twin, construction project management, building information modelling, internet of things, lifecycle management, predictive analyticsAbstract
Construction project management has traditionally relied on fragmented tools, linear workflows, and static documentation, often resulting in cost overruns, schedule delays, and suboptimal resource utilisation. The emergence of Digital Twin (DT) technology, coupled with advances in Building Information Modelling (BIM), the Internet of Things (IoT), and artificial intelligence (AI), presents a transformative opportunity to reimagine how civil engineering projects are planned, executed, monitored, and maintained throughout their lifecycle. This editorial scope examines the integration of DT technology into construction project management, highlighting its capacity to enable real-time visibility, predictive analytics, and data-driven decision-making. While the convergence of these technologies offers substantial benefits, including enhanced collaboration, risk mitigation, and lifecycle performance optimisation, significant challenges persist in areas such as interoperability, data security, workforce readiness, and implementation cost. This paper discusses the current state of adoption, identifies critical barriers, and proposes strategic pathways to accelerate the meaningful integration of DT-driven project management frameworks in the civil engineering sector
References
Institute, M. G. (2020). The next normal in construction: How disruption is reshaping the world’s largest ecosystem. McKinsey & Company.
Barbosa, F., Mischke, J., Ribeirinho, M. J., Parsons, M., Blanco, J. L., Woetzel, J., & Bertram, N. (2017). Reinventing construction: A route to higher productivity. McKinsey Global Institute. McKinsey Global Institute. Retrieved from https://www.mckinsey.com/~/media/McKinsey/Business Functions/Operations/Our Insights/Reinventing construction through a productivity revolution/MGI-Reinventing-construction-A-route-to-higher-productivity-Full-report.pdf
Oesterreich, T. D., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139. https://doi.org/10.1016/j.compind.2016.09.006
Grieves, M., & Vickers, J. (2016). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
Pan, Y., & Zhang, L. (2021). A BIM-data mining integrated digital twin framework for advanced project management. Automation in Construction, 124, 103564. https://doi.org/10.1016/j.autcon.2021.103564
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic construction digital twin: Directions for future research. Automation in Construction, 114, 103179. https://doi.org/10.1016/j.autcon.2020.103179
Institute, P. M. (n.d.). A guide to the Project Management Body of Knowledge (PMBOK Guide) 7th (Ed) (7th ed.). USA: Project Management Institute, Inc.
Botter, C. H. (1982). Project management: A systems approach to planning, scheduling and controlling. European Journal of Operational Research (13th ed., Vol. 10). Wiley. https://doi.org/10.1016/0377-2217(82)90164-3
Sacks, R., Eastman, C., Lee, G., & Teicholz, P. (2018). BIM handbook: A guide to Building Information Modeling for owners, designers, engineers, contractors, and facility managers. BIM Handbook: A Guide to Building Information Modeling for Owners, Designers, Engineers, Contractors, and Facility Managers (3rd ed.). Wiley.
Sacks, R., Gurevich, U., & Shrestha, P. (2016). A review of Building Information Modeling protocols, guides and standards for Large construction clients. Journal of Information Technology in Construction, 21, 479–503. Retrieved from http://www.itcon.org/2016/29
Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143
Deng, M., Menassa, C. C., & Kamat, V. R. (2021). From BIM to digital twins: A systematic review of the evolution of intelligent building representations in the AEC-FM industry. Journal of Information Technology in Construction, 26, 58–83. https://doi.org/10.36680/J.ITCON.2021.005
Wang, Q., & Kim, M. K. (2022). Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2005 to 2020. Advanced Engineering Informatics, 52, 101558.
Tang, P., Huber, D., Akinci, B., Lipman, R., & Lytle, A. (2010). Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques. Automation in Construction, 19(7), 829–843. https://doi.org/10.1016/j.autcon.2010.06.007
Zhang, S., Teizer, J., Lee, J. K., Eastman, C. M., & Venugopal, M. (2013). Building Information Modeling (BIM) and safety: Automatic safety checking of construction models and schedules. Automation in Construction, 29, 183–195. https://doi.org/10.1016/j.autcon.2012.05.006
Zhou, W., Whyte, J., & Sacks, R. (2012). Construction safety and digital design: A review. Automation in Construction, 22, 102–111. https://doi.org/10.1016/j.autcon.2011.07.005
Oesterreich, T. D., & Teuteberg, F. (2019). A taxonomy of digital twins in construction. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 4597–4606).
Liu, Z., Osmani, M., Demian, P., & Baldwin, A. (2015). A BIM-aided construction waste minimisation framework. Automation in Construction, 59, 1–23. https://doi.org/10.1016/j.autcon.2015.07.020
Lu, Q., Turner, C. J., Finlayson, M. P., Huang, L., & Girolami, M. (2021). BIM-DT: A building information modeling and digital twin framework for lifecycle management. Journal of Building Engineering, 43, 102826.
Opoku, D. G. J., Perera, S., Osei-Kyei, R., & Rashidi, M. (2021). Digital twin application in the construction industry: A literature review. Journal of Building Engineering, 40, 102726. https://doi.org/10.1016/j.jobe.2021.102726
Steel, J., Drogemuller, R., & Toth, B. (2012). Model interoperability in building information modelling. Software and Systems Modeling, 11(1), 99–109. https://doi.org/10.1007/s10270-010-0178-4
Akanbi, L. A., Oyedele, L. O., Delgado, M. D., Ajayi, A. O., Akinade, O. O., Bilal, M., & Owolabi, H. A. (2020). Reclaiming the environment from waste: A smart IoT and digital twin enabled construction and demolition waste management system. Journal of Cleaner Production, 260, 121017.
Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin information systems. Data-Centric Engineering, 1(6), e14. https://doi.org/10.1017/dce.2020.16
Sacks, R. (2020). Construction 4.0: An Innovation Platform for the Built Environment. Routledge. Routledge.
Stojanovic, B., Neumann, M., & Scherer, R. J. (2022). Cybersecurity challenges in digital twin-enabled construction projects. Automation in Construction, 138, 104210.
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
buildingSMART International. (2023). OpenBIM standards and the IFC schema: Current status and future directions. buildingSMART Technical Reports.
Teizer, J. (2015). Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites. Advanced Engineering Informatics, 29(2), 225–238. https://doi.org/10.1016/j.aei.2015.03.006
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 UNIMAS Publisher
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
During the manuscript submission process, the corresponding author, on behalf of all authors, will be asked to agree to the terms of the Open Access Publishing Agreement by checking the designated agreement box (see Copyright & Licensing for more information).
Open Access Publishing Agreement
1) By agreeing to these terms, the author(s) retain full copyright and grant UNIMAS Publisher the right of first publication. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract, or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and refer to the publication in the Journal.
2) The author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) The author(s) are responsible for ensuring their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or proprietary right of others. The corresponding author has obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If the submitted manuscript includes materials from others, the authors have obtained permission from the copyright owners.
4) By agreeing to this statement, the author(s) declare that the research which they have conducted complies with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving humans or the use of animal samples must obtain approval from the Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by the Chief Editor or UNIMAS Publisher.
5) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that they have no commercial attachments (e.g., patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor(s) or UNIMAS Publisher.
