Integration of convolutional and adversarial networks into building design: A review

• Type: Another Publication in an International Scientific Journal
• Date: 2023-06-29

Authors

Jean Parente (Author) FEUP; Eugénio Rodrigues (Author) Other; Bárbara Carvalho (Author) FEUP; João Poças Martins (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: JOURNAL OF BUILDING ENGINEERING

Vol. 76

ISSN: 2352-7102

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 11 Citations

Other information

Authenticus ID: P-00Y-K9H

DOI: 10.1016/j.jobe.2023.107155

Abstract (EN): Convolutional and adversarial networks are found in various fields of knowledge and activities. One such field is building design, a multi-disciplinary and multi-task process involving many different requirements and preferences. Although showing several advantages over traditional computational methods, they are still far from being part of the daily design practice. Nevertheless, if fully integrated, these methods are expected to accelerate design and automate procedures. This paper reviews these methods' latest advances and applications to identify current barriers and suggests future developments. For that, a systematic literature review extended with forward and backward snowball methods was carried out. The focus was on the first design phases, including site layout, floor planning, furniture arrangement, and facade design. The network models show great potential in exploring novel design paths, comparing alternative solutions, and reducing task-associated time and cost. In addition, newer approaches may benefit from combining convolutional and adversarial networks in decision-making since they may complement analysis and synthesis. However, the lack of a smooth integration into the design process and the need for a high-level mastery limit their widespread use. Furthermore, ethical issues arise, such as models being trained with biased datasets, ignoring the intellectual property of the data creators, potential violation of privacy, and the models limiting human creativity.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 24