starting point: 

What are the barriers to architects adopting new technology, and what are the implications for individuals, teams, and managers when a firm adopts a new technology?

This topic covers issues of: accessibility, training, communication, and cost of the use of technology for design and fabrication; and may include visualising social connections and interfaces to support technology adoption in architectural manufacturing.

project summary: 

The global construction industry faces increasing pressure to minimise environmental impacts while delivering adaptable and functional spaces. One promising strategy is adaptive reuse, which prolongs the life of existing buildings by converting them for new purposes. This approach contributes to sustainability by reducing demolition waste, conserving materials, and preserving embodied carbon, while also supporting urban regeneration (Gao & Pishdad-Bozorgi, 2019; Liu et al., 2022). Despite these benefits, adaptive reuse projects present significant challenges. They often rely on incomplete or outdated documentation, encounter hidden structural complexities, and face difficulty in integrating new systems into existing layouts. These uncertainties lead to cost overruns, design inefficiencies, and schedule delays. Traditional construction workflows that are dependent on manual site inspections and fragmented data struggle to provide the accuracy and integration required. Consequently, stakeholders are left with limited information for decision-making, weakening collaboration and reducing confidence in the reliability of adaptive reuse as a mainstream solution. 

To overcome the above problems, digital reconstruction technologies such as 3D laser scanning, photogrammetry, and point cloud modelling have emerged as promising solutions. They generate accurate as-built models that reflect existing conditions and support better-informed design and construction decisions. However, while Scan-to-BIM has shown value in heritage preservation and facility management contexts, its integration into mainstream adaptive reuse projects remains limited (Anwar & Azhar, 2025; D’Auria & D’Agostino, 2024). The main barriers include fragmented workflows, poor interoperability between platforms, and high levels of manual intervention in converting scan data into usable BIM models. Even though advances in AI and machine learning have improved object recognition, semantic segmentation, and automated geometry reconstruction (Xu et al., 2023; Drobnyi et al., 2024), these methods are not yet widely adopted in adaptive reuse workflows. This results in data silos, inefficiency, and a lack of standardized processes to support stakeholders. 

Currently, this research is in the literature review and familiarisation stage, focusing on understanding different scanning technologies and their applications across various project types and built assets. This includes evaluating traditional manual laser scanning alongside emerging AI-based methods for generating point clouds and reconstructing BIM models. Early findings emphasize the need for integrated workflows where scan data can be seamlessly converted into actionable BIM models. This stage lays the foundation for refining the research scope and identifying specific workflow-related challenges in adaptive reuse projects. 

Moving forward, the research aims to fine-tune its scope by identifying specific problems associated with Scan-to-BIM implementation in adaptive reuse. A key objective will be to develop a stakeholder-driven workflow framework, ensuring that data captured at one stage is optimised for multiple downstream uses, from design feasibility to construction execution and lifecycle management. Future work will examine automation techniques to reduce manual effort, assess interoperability solutions to connect diverse platforms, and explore collaborative models that enhance information sharing between architects, engineers, contractors, and facility managers. By addressing these barriers, the research intends to deliver practical tools and guidelines that minimise rework, optimise resource utilisation, and foster collaboration. Ultimately, the study will contribute to improving efficiency, reliability, and sustainability in adaptive reuse projects, supporting the broader adoption of Scan-to-BIM technologies in the construction industry. 

references: 

Anwar, R. M. I., & Azhar, S. (2025). A systematic review on the application of reality capture in the construction industry. Smart and Sustainable Built Environment. https://doi.org/10.1108/SASBE-03-2024-0079 

D’Auria, S., & D’Agostino, P. (2024). Scan-to-BIM and segmentation processes for the management of historical buildings. Disegnarecon, 17(32), 131–139. https://doi.org/10.20365/disegnarecon.32.2024.13 

Drobnyi, V., Li, S., & Brilakis, I. (2024). Connectivity detection for automatic construction of building geometric digital twins. Automation in Construction, 159, 105281. https://doi.org/10.1016/j.autcon.2024.105281 

Gao, X., & Pishdad-Bozorgi, P. (2019). BIM-enabled virtual environment for improving building design and retrofit decision-making. Automation in Construction, 102, 45–60. https://doi.org/10.1016/j.autcon.2019.02.010 

Liu, Y., Xie, H., & Xu, Y. (2022). Scan-to-BIM for sustainable built environment: A review of methods and applications. Automation in Construction, 136, 104152. https://doi.org/10.1016/j.autcon.2022.104152 

Xu, Y., Chen, C., & Cheng, J. C. P. (2023). Automated as-built BIM creation using deep learning and point cloud data for construction applications. Automation in Construction, 150, 104889. https://doi.org/10.1016/j.autcon.2023.104889 

image reference: 

Peripitus. (2007, September 22). Bicentennial tropical conservatory in the Adelaide Botanic Gardens [Photograph]. Wikimedia Commons. Retrieved August 31, 2025, from https://commons.wikimedia.org/wiki/File:Adelaide_Botanic_Gardens_Bicentennial_conservatory.JPG