Using Machine Learning Techniques to Predict Esthetic Features of Buildings
Publication: Journal of Architectural Engineering
Volume 27, Issue 3
Abstract
Several substantial market barriers obstruct the widespread adoption of sustainable buildings. Esthetic features are amongst the main driving forces behind the marketability of buildings, thus improvement of sustainable buildings in terms of visual esthetics would enhance their marketability and thus their market intake. Nonetheless, esthetic improvement of the buildings is a challenging task because it lacks in scales and methods to measure and evaluate buildings’ facade esthetic. In this regard, this study aims to develop machine learning-based models to predict the esthetic appreciation of buildings related to their façade features. For this purpose, an artificial neural network and decision tree models are developed and validated with the results of a conducted comprehensive survey (n = 807). In addition, the impact of different window features (i.e., position, number, area, width, height, symmetry, and proportion) on housings esthetic and marketability is investigated. Results show a high level of accuracy for both models in the prediction of esthetic appreciation of buildings.
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Acknowledgments
The authors express their gratitude to the Faculty of Engineering of the University of Nottingham for use of their facilities and Mehmet Aydin for his financial support.
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Published In
Journal of Architectural Engineering
Volume 27 • Issue 3 • September 2021
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© 2021 American Society of Civil Engineers.
History
Received: Oct 6, 2020
Accepted: Mar 17, 2021
Published online: Jun 11, 2021
Published in print: Sep 1, 2021
Discussion open until: Nov 11, 2021
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