starting point: 

How is design data stored, made accessible, or secure, and what are the practical, legal, ethical, and commercial frameworks that allow or prevent the use of architectural data?   

This topic may include consideration of: the potential benefits and limits of current practice, the relevant timescales (years / decades) for different data uses (circular economy) and types of users (individual/society).  

project summary: 

The rapid digitalisation of the architectural, engineering, and construction (AEC) industries has significantly increased the value of data in the sector (Ahmed et al., 2018). However, managing this data remains a complex challenge due to its heterogeneous nature. Digital data has been integral to projects since the introduction of Building Information Models (BIM), which serve as a data management strategy in the sector (Hu et al., 2022). This embedded data from the design phase is stored in companies’ archives as both tacit and explicit knowledge that can inform and improve design decisions (Howard, 1991).   

AEC professionals face challenges not only in managing, extracting, and utilising digital data (Bilal et al., 2016) but also in leveraging it to minimise the environmental impact of AEC practices (Ghafoor et al., 2023). To address this, data circularity (Sen, 2023) emerges as a novel approach to data management and use within the circular economy framework (Ellen MacArthur Foundation, 2025). This approach treats data as an independent asset with intrinsic value, enabling its enhancement through strategies such as reuse, relearning, repurposing, reduction, and disposal [Figure 1].   

Figure 1 Circular Economy, (1-3) Evolution of the Butterfly diagram to the development of the Desirable Circular Cycle. (4) Compilation of the technical and biological cycle and the inclusion of the Digital Cycle with its strategies (Author interpretation). 

This research aims to improve the effectiveness of early design decisions by providing meaningful insights using data extracted from existing projects. Emphasises on the early stages of a project where key decisions can be made with minimal impact on the overall budget and timeline and maximum influence on the outcome (McLeame Curve, 2004).   

To achieve this goal, the research methodology combines two approaches: action design research, which involves collaboration with industry partners from the early stages (Arch_Manu), and speculative design (Dunne & Raby, 2014) [Figure 2], which offers a critical perspective on how data can influence future design decisions in the AEC industry.  

Figure 2 Research Strategy. Past – Present – Future Strategy. Illustrate the flow of knowledge, the methods used and the stages of the research methodology., it will be explained in detail in the section 3.1 The Methodology + Overview of the Iterative cycle 

This methodology will be applied using a ‘petri dish’ scheme (Langenheim, 2020) [Figure 3], where small projects will be developed and tested to verify the research hypothesis. The outcomes of this scheme will be validated in collaboration with industry partners on a real project.   

Figure 3. Petri Dish Scheme. Mapping and integration with the industry engagements in the validation stage. 

Ultimately, the study emphasises that integrating data circularity into AEC processes can enhance early design decisions within the circular economy paradigm, thereby promoting sustainable practices and fostering a digital mindset in the AEC sector. 

References 

• Ahmed, V., Aziz, Z., Tezel, A., & Riaz, Z. (2018). Challenges and drivers for data mining in the AEC sector. Engineering, Construction and Architectural Management, 25(11), 1436–1453. https://doi.org/10.1108/ECAM-01-2018-0035  

• Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., Owolabi, H. A., Alaka, H. A., & Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500–521. https://doi.org/10.1016/j.aei.2016.07.001  

• Dunne, A., & Raby, F. (2014). Speculative everything, design, fiction and social dreaming.  

• Ellen MacArthur Foundation. (2025). Circular Economy. https://www.ellenmacarthurfoundation.org/  

• Ghafoor, S., Hosseini, M. R., Kocaturk, T., Shooshtarian, S., Arnel, T., King, D., Bonsey, J., & Garofano, N. (2023). Unlocking the Power of the Circular Economy in the Australian AEC Industry: A Journey through Attitudes, Barriers and Enablers.  

• Howard, H. C. (1991). Project‐Specific Knowledge Bases in AEC Industry. Journal of Computing in Civil Engineering, 5(1), 25–41. https://doi.org/10.1061/(ASCE)0887-3801(1991)5:1(25)  

• Hu, Z.-Z., Leng, S., Lin, J.-R., Li, S.-W., & Xiao, Y.-Q. (2022). Knowledge Extraction and Discovery Based on BIM: A Critical Review and Future Directions. Archives of Computational Methods in Engineering, 29(1), 335–356. https://doi.org/10.1007/s11831-021-09576-9  

• Langenheim, N. (2020). Designing the Urban Forest: Toward a visual-functional design decision support system for tree-scapes. 72763251 Bytes. https://doi.org/10.26180/5EB25C0AB88CA 

• Sein, M., Henfridsson, O., Purao, S., Rossi, M., & Lindgren, R. (2011). Action Design Research. MIS Quarterly, 35, 37–56. https://doi.org/10.2307/23043488  

• Sen, D. (2023). What is data circularity, and why should you care? https://dataroots.io/blog/what-is-data-circularity-and-why-should-you-care