A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Architectural spatial layout planning using artificial intelligence
Abstract Spatial layout planning in architecture requires a deep understanding of topological spatial relationships, yet the process remains repetitive and laborious for designers. However, advancements in artificial intelligence (AI) offer new perspectives through automated spatial layout planning (ASLP), diverging from traditional human-centered approaches. This paper employs quantitative and qualitative analyses, reviewing 589 journal articles from the past decade, to gain a comprehensive understanding of ASLP+AI. It also proposes future research directions, exploring image-based, graph-based, performance-based, and agent-based approaches. Contributions to the existing body of knowledge include advancing ASLP through AI methodologies while preserving architects as curators, with a broader impact on academia and industry in promoting AI applications for spatial layout planning.
Graphical abstract Display Omitted
Highlights Explores the potential of machine learning in automated spatial layout planning. Reviews 589 articles to identify trends, challenges, and opportunities in the field. Categorizes into image-based, graph-based, performance-based, and agent-based. Proposes future research directions across four distinct categories. Emphasizes the importance of keeping architects as curators in the process.
Architectural spatial layout planning using artificial intelligence
Abstract Spatial layout planning in architecture requires a deep understanding of topological spatial relationships, yet the process remains repetitive and laborious for designers. However, advancements in artificial intelligence (AI) offer new perspectives through automated spatial layout planning (ASLP), diverging from traditional human-centered approaches. This paper employs quantitative and qualitative analyses, reviewing 589 journal articles from the past decade, to gain a comprehensive understanding of ASLP+AI. It also proposes future research directions, exploring image-based, graph-based, performance-based, and agent-based approaches. Contributions to the existing body of knowledge include advancing ASLP through AI methodologies while preserving architects as curators, with a broader impact on academia and industry in promoting AI applications for spatial layout planning.
Graphical abstract Display Omitted
Highlights Explores the potential of machine learning in automated spatial layout planning. Reviews 589 articles to identify trends, challenges, and opportunities in the field. Categorizes into image-based, graph-based, performance-based, and agent-based. Proposes future research directions across four distinct categories. Emphasizes the importance of keeping architects as curators in the process.
Architectural spatial layout planning using artificial intelligence
Ko, Jaechang (author) / Ennemoser, Benjamin (author) / Yoo, Wonjae (author) / Yan, Wei (author) / Clayton, Mark J. (author)
2023-07-06
Article (Journal)
Electronic Resource
English
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