A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Using Machine Learning Techniques to Predict Esthetic Features of Buildings
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.
Using Machine Learning Techniques to Predict Esthetic Features of Buildings
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.
Using Machine Learning Techniques to Predict Esthetic Features of Buildings
Aydin, Yusuf Cihat (author) / Mirzaei, Parham A. (author) / Hale, Jonathan (author)
2021-06-11
Article (Journal)
Electronic Resource
Unknown
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