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: 

Automation has been introduced to the AEC sector for a long time, with various technologies such as digital fabrication (Hana Begić et al., 2022), generative design empowered by machine learning (Zhuang et al., 2025), or artificial intelligence (Castro Pena et al., 2021; Fischer & Nakakoji, 1992), promising to enhance productivity. However, researchers and practitioners alike highlight that there are still many barriers to the adoption of these technologies, including complexity, lack of skilled labour, regulatory constraints, and inadequate infrastructure (Mohammadsoroush Tafazzoli et al., 2024). 

In Generative Design (my project’s area of focus), software tools, such as TestFit, Archistar and Hypar, have emerged, leveraging automation, integrating knowledge of building regulations, and applying form-finding and planning techniques in a user-friendly way, to make repetitive tasks more efficient (Hypar for Revit, n.d.; TestFit, 2025; Lewis, 2025; Loruenser et al., 2015). While these tools show great potential and are increasingly adopted by some AEC stakeholders, they have yet to gain broad acceptance among architectural designers. Issues such as the uncontrolled, unpredictable nature of automation and a lack of flexibility are key factors hindering acceptance among practitioners (Mohammadsoroush Tafazzoli et al., 2024; Naseri, 2024; Savu & Bungău, 2024). 

Therefore, my research aims to define the ideal levels and applications of automation (via generative design) in the design process for different design contexts and firms. In doing so, I will address research gaps in the practical application of automation in daily workflows, complementing the extensive body of theoretical and technical research on automation in the AEC sector. 

Ultimately, this supports designers by providing guidelines on types of software and levels of automation that best suit their needs and the projects’ requirements. At the same time, it supports software developers to improve the utility and design of these tools. 

This study will employ a mixed-methods approach to first better understand the needs of designers regarding automation, then propose and test workflows for different architectural design tasks and project contexts during the feasibility design phase. For example, identifying which phases or activities should be automated to enhance efficiency, and which processes or data should be manually controlled by designers to achieve optimal outcomes. I will then test the workflows through the case studies to identify the potential level of productivity gained when such tools are adopted. 

References 

Castro Pena, M. L., Carballal, A., Rodríguez-Fernández, N., Santos, I., & Romero, J. (2021). Artificial intelligence applied to conceptual design. A review of its use in architecture. Automation in Construction, 124, 103550. https://doi.org/10.1016/j.autcon.2021.103550 

Fischer, G., & Nakakoji, K. (1992). Beyond the macho approach of artificial intelligence: Empower human designers — do not replace them. Knowledge-Based Systems, 5(1), 15–30. https://doi.org/10.1016/0950-7051(92)90021-7 

Hana Begić, Mario Galić, & Zlata Dolaček-Alduk. (2022). DIGITALIZATION AND AUTOMATION IN CONSTRUCTION PROJECT’S LIFE-CYCLE: A REVIEW. Journal of Information Technology in Construction, 27, 441–460. https://doi.org/10.36680/j.itcon.2022.021 

Lewis, A. (2025). Optimising Early Stage Design Workflows for Architects—The Ultimate Generative Design Report—ADDD. ADDD. https://addd.io/product/gd-report/ 

Loruenser, T., Happe, A., & Slamanig, D. (2015). ARCHISTAR: Towards Secure and Robust Cloud Based Data Sharing. 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), 371–378. https://doi.org/10.1109/CloudCom.2015.71 

Mohammadsoroush Tafazzoli, Kishor Shrestha, & Hongtao Dang. (2024). Investigating Barriers to the Application of Automation in the Construction Industry. Investigating Barriers to the Application of Automation in the Construction Industry. Construction Research Congress 2024. https://doi.org/10.1061/9780784485262.096 

Naseri, S. (2024). AI in Architecture and Urban Design and Planning: Case studies on three AI applications. GSC Advanced Research and Reviews, 21, 565–577. https://doi.org/10.30574/gscarr.2024.21.2.0463 

Savu, C., & Bungău, C. (2024). Synthesis on the assessment of design platforms in terms of construction efficiency. Review of Management and Economic Engineering, 23, 220–232. https://doi.org/10.71235/rmee.4 

Skema. (n.d.). Skema. Retrieved August 22, 2025, from https://www.skema.ai/about-us 

TestFit: Real Estate Feasibility Platform. (2025). https://www.testfit.io/ 

Zhuang, X., Zhu, P., Yang, A., & Caldas, L. (2025). Machine learning for generative architectural design: Advancements, opportunities, and challenges. Automation in Construction, 174, 106129. https://doi.org/10.1016/j.autcon.2025.106129