Comparative aesthetic assessment of machine learning and human judgment for building wall designs

Abstract

Machine learning models can potentially provide alternative options in the field of architecture as aesthetic judgment tools, owing to their high capacity and data-driven environments. If a machine learning model can produce aesthetic evaluation results similar to those of humans, the process may be highly promising for further applications in architectural decision-making. In this study, we propose a series of interconnected workflows for a rigorous comparison, including data collection, machine learning, parametric designs, robotic fabrication, and human surveys, to test the compatibility between human judgment and machine learning models in the aesthetic assessment of architectural objects on the same design objects. We observed a wide gap between the aesthetic judgments of the two groups. We discuss certain drawbacks and current limitations to improve the vulnerability of the study process and conclude by providing an outlook for the subsequent direction of a similar study.

KEYWORDS:

• Deep learning
• Teachable Machine
• parametric design
• robotic fabrication
• human judgement survey
• aesthetic assessment

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This study was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2021R1A2C1093869).