Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance.

Technical laboratories play an important role in engineering education; however, managing laboratory equipment commonly relies on fragmented, manual processes that make information difficult to locate, access and maintain. This study develops and assesses a BIM-based DT framework for the spatial representation, documentation, and information accessibility developed for a surveying equipment store asset at UniSQ. The proposed framework integrates reality capture and digital modelling technologies through a workflow involving mobile laser scanning and UAV-based LiDAR data, point-cloud processing, BIM development, asset information integration, and cloud-based DT implementation. The surveying equipment store was represented within a reconstructed BIM model of F Block, with equipment records and supporting documentation linked to modelled storage cabinets and shelves. The implementation linked information for 28 surveying equipment items across five equipment types to their modelled storage locations. A cloud-to-mesh comparison of the F Block BIM, including the surveying store interior (floor and roof excluded), with the noise-filtered mobile laser scanning (MLS) point cloud (16,131,551 points) gave a mean absolute deviation of 40.2 mm (SD 22.1 mm; RMSE 45.9 mm), with approximately 63% of points within 50 mm. The developed DT provides a centralised spatial interface for identifying equipment storage locations and accessing associated asset information. The assessment focused on technical implementation and functional capabilities; no user evaluation was conducted. Future studies will evaluate usability and information-retrieval performance with students, staff, and technical officers. The study contributes an application-focused framework that links laboratory equipment information and management workflows to a spatial BIM-based environment using storage-level asset representation, providing a foundation for future real-time asset tracking and monitoring.

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimizing lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires inter-disciplinary collaboration, standardization of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionize the AEC industries by enabling autonomous, data-driven decision-making and optimizing building operations for enhanced productivity and performance.