User-centered building design preference assessment to develop data-driven interactive architectural design guideline models
Description
In many architectural designing scenarios, architects and clients inevitably spend a lot of time determining design agreements due to a lack of understanding about the client’s design needs and preferences. An architectural design process could be significantly expedited and simplified if modeling software can accurately extract the user’s preferred design features and integrate them into the design process. In this project, we addressed the challenges of demonstrating a stochastic model with the consideration of the user’s physiological responses and subjective design perceptions by using data analytic methods. This technical principle exploited personal design preferences that would adopt them to the design process to effectively complete an architecture project.
Students
Advisors
Skills Required by the team
- Python
- Statistics
- Machine Learning
- Sklearn
- WEKA
- R