The Role of Digital Twin Frameworks in Lifecycle Management and Predictive Maintenance of Architectural Projects
Keywords:
Digital Twin, Lifecycle Management, Predictive Maintenance, Architecture, Smart Buildings, BIM IntegrationAbstract
The integration of Digital Twin (DT) frameworks into architectural practice has gained significant traction in recent years, particularly in relation to lifecycle management and predictive maintenance. By providing real-time monitoring, simulation, and predictive analytics, DTs offer a transformative approach to optimizing performance across the full building lifecycle. This paper explores how DT frameworks contribute to enhanced asset management, predictive maintenance strategies, and sustainability goals in architectural projects. The review identifies key trends, methodological approaches, and challenges, while also proposing future research directions. A conceptual framework and comparative analysis are presented to illustrate the evolving role of DT in managing complex built environments.
References
Boschert, Stefan, and Roland Rosen. Digital Twin—The Simulation Aspect. Springer, 2018.
Grieves, Michael, and John Vickers. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. Springer, 2017.
Khajavi, Seyed Hamidreza, Naser Hossein Motlagh, Aida Jaribion, Leena C. Werner, and Jan Holmström. "Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings." IEEE Access, vol. 9, 2021, pp. 147726–147744.
Lu, Qian, Xin Wang, Yongze Xie, and Shang Gao. "Digital Twin-Enabled Smart Management of Built Environments." Automation in Construction, vol. 109, 2020, pp. 102958.
Opoku, Dickson-Gbadegbe R., Yacine Rezgui, and Asaad H. Mourshed. "Towards Artificial Intelligence-Driven Sustainability in the Built Environment: A Review of Digital Twin Applications." Sustainable Cities and Society, vol. 76, 2022, pp. 103445.
Shen, Wei, Min An, and Pei-Hua Guo. "The Application of Digital Twin for Smart Asset Management in the AEC Industry." Journal of Civil Engineering and Management, vol. 28, no. 5, 2022, pp. 403–418.
Jones, David, Chris Snider, Andrew Nassehi, Jonathan Yon, and Ben Hicks. "Characterising the Digital Twin: A Systematic Literature Review." CIRP Journal of Manufacturing Science and Technology, vol. 29, 2020, pp. 36–52.
Fuller, Aaron, Jennifer Fan, Chin Pang Day, and Shih-Chung Kang. "Digital Twin: Enabling Technologies, Challenges and Open Research." IEEE Access, vol. 8, 2020, pp. 108952–108971.
Boje, Calin, Armand Guerriero, Sébastien Kubicki, and Yacine Rezgui. "Towards a Semantic Construction Digital Twin: Directions for Future Research." Automation in Construction, vol. 114, 2020, pp. 103179.
Madni, Azad M., Charles C. Madni, and Scott D. Lucero. "Leveraging Digital Twin Technology in Model-Based Systems Engineering." Systems, vol. 7, no. 1, 2019, pp. 1–19.
Qi, Qinglin, and Fei Tao. "Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison." IEEE Access, vol. 6, 2018, pp. 3585–3593.
Sacks, Rafael, Chia-Fei Brilakis, and Yong-Cheol Lee. "Building Information Modelling, Digital Twins and Cyber-Physical Systems: A Roadmap for Research." Advanced Engineering Informatics, vol. 46, 2020, pp. 101225.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Blokham Rizz (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
