Digital Twin Applications in Building Lifecycle Management
Keywords:
Digital Twin, Building Lifecycle Management, Cyber-Physical Systems, Socio-Technical Infrastructure, Sustainable Engineering, Data Governance.Abstract
The integration of Digital Twin (DT) technologies within Building Lifecycle Management (BLM) represents a paradigm shift from static Building Information Modeling (BIM) toward dynamic, bidirectional cyber-physical systems. This paper provides a comprehensive interdisciplinary analysis of the architectural, socio-technical, and systemic implications of DT deployment across the building lifecycle, spanning from conceptual design and construction to operation, maintenance, and eventual decommissioning. Unlike traditional modeling approaches, the Digital Twin offers a high-fidelity, real-time reflection of physical assets, enabled by the convergence of the Internet of Things (IoT), edge computing, and advanced data analytics. However, the transition toward fully autonomous and responsive building systems introduces significant structural trade-offs regarding data interoperability, computational overhead, and long-term infrastructure robustness. This research examines the governance frameworks required to manage multi-stakeholder data ownership and the policy implications of large-scale urban DT integration. By exploring the nexus of artificial intelligence and physical infrastructure, we highlight how DTs facilitate predictive maintenance and energy optimization while simultaneously raising critical questions about algorithmic fairness, cybersecurity, and the digital divide in the built environment. The study concludes that while Digital Twins offer unprecedented opportunities for sustainability and operational efficiency, their successful implementation necessitates a shift from siloed technical solutions toward holistic, governance-led socio-technical architectures.
References
1.Aibinu, A. A., & Papadonikolaki, E. (2020). Conceptualizing the Digital Twin in the Built Environment. Journal of Management in Engineering, 36(6), 04020080.
2.Batty, M. (2018). Digital Twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817-820.
3.Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114, 103179.
4.Bolton, A., Butler, L., Soga, K., et al. (2018). The Gemini Principles. Centre for Digital Built Britain. University of Cambridge.
5.Chen, Z., Huang, L., & Zhang, Y. (2021). Digital Twin for Building Energy Efficiency: A Literature Review. Renewable and Sustainable Energy Reviews, 147, 111197.
6.D’Amico, B., Pikas, E., & Sacks, R. (2022). The Role of Digital Twins in Circular Economy for Construction. Resources, Conservation and Recycling, 178, 106068.
7.Davila Delgado, J. M., & Oyedele, L. (2021). Deep Learning with Digital Twin for Building Performance Optimization. Advanced Engineering Informatics, 48, 101282.
8.Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85-113). Springer.
9.Heaton, J., & Parlikad, A. K. (2019). A Conceptual Framework for the Alignment of Hierarchical Digital Twins. IFAC-PapersOnLine, 52(3), 68-73.
10.ISO. (2021). ISO 23247-1:2021 Automation systems and integration — Digital twin framework for manufacturing. International Organization for Standardization.
11.Ketzler, R., Naticchia, B., & Carbonari, A. (2020). Building Lifecycle Management through Digital Twin: A Review of the Current State. Applied Sciences, 10(15), 5240.
12.Khajavi, S. H., Motlagh, N. H., Jaribion, A., et al. (2019). Digital Twin: The Role of Fog Computing in Optimizing Building Management. IEEE Access, 7, 118249-118260.
13.Kritzinger, W., Karner, M., Trautner, G., et al. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016-1022.
14.Lamb, K. (2019). The Socio-Technical Nature of Digital Twins. Centre for Digital Built Britain.
15.Lu, Q., Parlikad, A. K., Woodall, P., et al. (2020). Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus. Journal of Management in Engineering, 36(3), 05020004.
16.Min, Q., Lu, Y., Liu, Z., et al. (2019). Machine Learning Based Digital Twin Framework for Production Optimization in Petrochemical Industry. International Journal of Information Management, 49, 502-519.
17.Nochta, T., Wan, L., Schooling, J. M., & Parlikad, A. K. (2021). Digital Twins for Urban Planning: A Socio-Technical Governance Approach. Journal of Urban Technology, 28(1-2), 263-286.
18.O’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019). Smart energy systems for sustainable cities: Opportunities for Digital Twin. Applied Energy, 237, 580-597.
19.Papadonikolaki, E., van Oel, C., & Kagioglou, M. (2019). Organising and managing digital-physical systems in the built environment. Construction Management and Economics, 37(9), 475-478.
20.Qi, Q., & Tao, F. (2018). Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access, 6, 3585-3593.
21.Sacks, R., Brilakis, I., Pikas, E., et al. (2020). Construction with Digital Twin: A Vision for Architecture, Engineering and Construction. Automation in Construction, 112, 103080.
22.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.
23.White, G., Zink, A., Codecá, L., & Clarke, S. (2021). A Digital Twin for Smart Cities: Foundations, Features and Applications. IEEE Communications Magazine, 59(2), 14-20.
24.Xie, X., Lu, Q., Parlikad, A. K., & Schooling, J. M. (2020). A Digital Twin-based system for maintenance management and sustainability. IFAC-PapersOnLine, 53(2), 17154-17159.
25.Ye, C., Butler, L., Cekic, B., et al. (2022). A Digital Twin Framework for Structural Health Monitoring of Bridges. Structural Health Monitoring, 21(5), 2240-2258.
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