Development of a framework for transitioning from a traditional campus to a smart campus using digital twin.

This study investigates the adoption of digital twin (DT) technology as a pathway for transitioning from traditional asset management to smart campus environments within the United Kingdom higher education sector. Despite the growing relevance of digital twins as real-time, data-driven representations of physical assets, their application in university campus management remains limited. Existing practices are characterised by fragmented data systems, reactive maintenance, and limited digital integration, compounded by organisational resistance, skills gaps, and the absence of structured implementation pathways. This highlights a critical gap in both academic research and industry practice, particularly the lack of a transition-oriented framework tailored to existing campus environments. To address this gap, the study adopts a mixed-methods approach, combining qualitative interviews, a quantitative survey of 50 industry professionals, and case study analysis. The findings reveal that digital twin adoption is constrained by multiple interrelated factors, including limited awareness, data interoperability challenges, high implementation costs, insufficient institutional readiness, and weak policy support. Statistical analysis further demonstrates a strong inverse relationship between organisational readiness and perceived challenges, emphasising the importance of capacity building and infrastructure investment. In response, a five-phase Digital Twin-Enabled Smart Campus Framework was developed, comprising inception, initiation, analysis, full implementation, and continuous review, designed to support incremental and context-sensitive adoption. The framework was critically evaluated through expert and user-centric validation, confirming its practical relevance and improvement over existing approaches. Participants highlighted its strengths in enabling data integration, real-time monitoring, predictive decision-making, and continuous optimisation through AI-driven feedback loops. However, the evaluation also identified areas for refinement, including the need for clearer operational guidance, enhanced data governance, and stronger change management strategies. Overall, the study contributes a holistic, evidence-based framework that advances understanding of digital twin adoption as a socio-technical transition process, offering both theoretical insight and practical guidance for universities seeking to implement smart campus solutions.

Author dc:creator

• Bolaji, Taiwo Awwal

Advisor dc:contributor.advisor

• H. Salman and J. Scott

• Digital twin
• Smart campus
• Building information modelling (BIM)
• Internet of things (IOT)
• Artificial intelligence
• Asset management
• Higher education

Language dc:language

en

Identifier

oai:rgu-repository.worktribe.com:3463732
https://doi.org/10.48526/rgu-wt-3463732

OAI identifier oai:identifier

oai:rgu-repository.worktribe.com:3463732