Exploration of Machine Learning Space Layout Generation of Apartments in Hamadan

Mahsa Hamouni ¹ ( Department of Architecture, Faculty of Art & Architecture, South Tehran Branch, Islamic Azad University, Tehran, Iran )
Hossein Soltanzadeh ² ( Associate Professor, Department of Architecture, Faculty of Art and Architecture, Central Tehran Branch, Islamic Azad University, Tehran, Iran. )
Hadi Ghoddusifar ³ ( Assistant Professor of Architecture, Department of Architecture, South Tehran, Islamic Azad University, Tehran, Iran. )
Muharram Mansoorizadeh ⁴ ( Department of Computer Science, Faculty of Engineering, Bu-Ali Sina University, Hamadan, Iran )

تاریخ ارسال : 1402/08/20 تاریخ تایید : 1403/08/08

کلید واژه: Spatial layout design, Machine learning, Image&ndash, to-image translation, Conditional GAN,

چکیده مقاله :

Artificial intelligence technology has become an influential and trending topic in the field of architectural layout design. The core technology of artificial intelligence, machine learning, has attracted the attention of architects as a decision-making tool. The focus of many studies that apply machine learning to layout design is using the generative adversarial network (GAN), which generates space layout within a given boundary. Previous research demonstrates that training a GAN with labels can help a computer understand how spatial elements relate to each other and the logical relationship between spatial elements and boundaries. However, this paper applied conditional GAN to generate space layouts with given boundaries and supplementary conditions. The additional conditions provide designers control over the generated layout plans by satisfying both input boundary and user requirements. It also allows designers to generate different space layout plans within the same boundary. For this purpose, a specific dataset is created. The dataset consists of 660 apartment plans in Hamadan. We split the dataset into a training set and a test set. The training set includes 594 images, and the test set includes 66 (10%) of the images. After completing the process of training the model with the training set, we test the model by using the test set. Finally, the outputs of the model are evaluated based on quantitative and qualitative methods. The results show that the supplementary conditions provide further guidance to the model for space layout generation and reduce the image quality problems of the synthetic images.

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