Modelling the perception of visual design principles on façades through fuzzy sets: towards building an automated architectural data generation and labelling tool

Abstract

Recent studies showed that deep learning techniques and image processing can identify the distinguishing design principles in architectural façades. However, predicting the strength of a principle is still a challenging task, as it requires a huge amount of annotated design variations. The difficulties in both searching such big numbers of data – and its labelling by experts – slow down the research. This paper proposes a computation approach for obtaining this type of data faster. With the help of parametric modelling and evolutionary algorithms, we could manipulate the design elements, and thereby generate different solutions. An integrated fuzzy logic decision mechanism could enable to carry human knowledge in the judging and labelling of alternatives automatically. The final synthetic data developed from real building images could be used for machine learning applications to enhance our understanding of artistic expression.

KEYWORDS:

• Façade design
• Visual design principles
• Fuzzy Logic
• Parametric modelling
• Data generation
• Automated labelling

Acknowledgement

The author wishes to thank Sinem Kırkan and Tuğrul Agrikli for their valuable support in modelling and visualization parts. Thanks are due to the esteemed raters, whose profound expertise greatly enriched the verification phase. Lastly, the author would like to thank the anonymous reviewers for their constructive comments. The author received no financial support for the research, authorship and/or publication of this article.

Disclosure statement

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

Data availability

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