Starting point: 

How are practice data (BIM, GIS, Lidar models, etc.) structured to enable knowledge accessibility, interoperability, and security?  How are errors, risks, and file sharing handled?

This topic may include consideration of: vocabulary and semantics/ontology across architectural sectors and how it is covered by standards; the impacts of interfaces and communication methods needed to feed analysed practice data back to practice.

Project Summary: 

This research addresses a critical challenge in the AEC (Architecture, Engineering, and Construction) sector: the fragmentation of data and inefficient cross-disciplinary collaboration. Despite technological advances like BIM and digital twin, the industries struggle with data silos, incompatible platforms, and non-standardised data management approaches, leading to reduced efficiency and increased project costs. The study targets both academic researchers in data system construction and AEC professionals, particularly those involved in digital transformation initiatives. 

The research’s significance lies in its comprehensive approach to deal with long-standing data accessibility and interoperability issues. While existing solutions like ACC (Autodesk Construction Cloud) offer partial remedies, they remain limited by file-based collaboration and closed ecosystems. The innovation lies in developing a more flexible approach to database management for representing complex relationships between AEC objects with hybrid database system. By incorporating computer vision technology to process unstructured data like LiDAR scans and drone imagery, and establishing uniform data standards, this research creates a more efficient and comprehensive collaboration platform. 

The potential impact is twofold. First, it will enhance cross-disciplinary collaboration by breaking down data silos, enabling architects, engineers, and construction managers to share information seamlessly. Second, it will improve decision-making processes through real-time data analysis and visualization, particularly beneficial for early-stage design validation and building performance optimization.  

References： 

1. Hernández, J. L., Martín, S., Marinakis, V., & de Miguel, I. (2023, May). From silos to open, federated and enriched Data Lakes for smart building data management. In 2023 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv) (pp. 29-33). IEEE. 

2. Zhao, Y., Wang, N., Liu, Z., & Mu, E. (2022). Construction theory for a building intelligent operation and maintenance system based on digital twins and machine learning. Buildings, 12(2), 87. 

3. Poinet, P., Stefanescu, D. and Papadonikolaki, E., 2020, July. Collaborative workflows and version control through open-source and distributed common data environment. In International Conference on Computing in Civil and Building Engineering (pp. 228-247). Cham: Springer International Publishing. 

4. Stefanescu, D., 2020. Alternate Means of Digital Design Communication (PhD Thesis). UCL, London. 

5. Vatanen, J., 2024. Exploring NVIDIA Omniverse Ecosystem.