Generative design for architectural spatial layouts: a review of technical approaches
The design of spatial layouts is a classic challenge in architectural design. This study reviews the current research on generative design for architectural spatial layouts, focusing mainly on technological methodologies. These methodologies can be broadly categorized into two main approaches: data-...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-06-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2025.2512235 |
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| _version_ | 1850117962609983488 |
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| author | Shurui Yan Chen Wu Yixin Zhang |
| author_facet | Shurui Yan Chen Wu Yixin Zhang |
| author_sort | Shurui Yan |
| collection | DOAJ |
| description | The design of spatial layouts is a classic challenge in architectural design. This study reviews the current research on generative design for architectural spatial layouts, focusing mainly on technological methodologies. These methodologies can be broadly categorized into two main approaches: data-driven and knowledge-driven. Initially, the study defines each approach by elucidating their principles, introducing typical methods, providing relevant research statistics, contrasting the characteristics of various methodologies, and critically analysing the advancements and constraints inherent in each technical approach. In conclusion, for the data-driven approach, key research areas include expanding datasets, enhancing alignment with human values, and improving model explainability. For the knowledge-driven approach, finding effective solutions to translate architectural design knowledge into programs is essential. Additionally, improving computational efficiency makes knowledge-driven methods, such as heuristic algorithms, more user-friendly. Furthermore, the study suggests that an intelligent combination of these two approaches at different phases of generative design yields a promising research direction. This synergy compensates for their respective limitations and leverages the full potential of human expertise encapsulated within data and rules, thereby addressing insufficient data and ensuring that models align well with human values. |
| format | Article |
| id | doaj-art-1985e06eb4024ef5b66307686fab75c4 |
| institution | OA Journals |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-1985e06eb4024ef5b66307686fab75c42025-08-20T02:35:59ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-06-010012110.1080/13467581.2025.25122352512235Generative design for architectural spatial layouts: a review of technical approachesShurui Yan0Chen Wu1Yixin Zhang2University of Chinese Academy of SciencesUniversity of Chinese Academy of SciencesTsinghua UniversityThe design of spatial layouts is a classic challenge in architectural design. This study reviews the current research on generative design for architectural spatial layouts, focusing mainly on technological methodologies. These methodologies can be broadly categorized into two main approaches: data-driven and knowledge-driven. Initially, the study defines each approach by elucidating their principles, introducing typical methods, providing relevant research statistics, contrasting the characteristics of various methodologies, and critically analysing the advancements and constraints inherent in each technical approach. In conclusion, for the data-driven approach, key research areas include expanding datasets, enhancing alignment with human values, and improving model explainability. For the knowledge-driven approach, finding effective solutions to translate architectural design knowledge into programs is essential. Additionally, improving computational efficiency makes knowledge-driven methods, such as heuristic algorithms, more user-friendly. Furthermore, the study suggests that an intelligent combination of these two approaches at different phases of generative design yields a promising research direction. This synergy compensates for their respective limitations and leverages the full potential of human expertise encapsulated within data and rules, thereby addressing insufficient data and ensuring that models align well with human values.http://dx.doi.org/10.1080/13467581.2025.2512235architectural spatial layoutgenerative designartificial intelligenceknowledge-drivendata-driven |
| spellingShingle | Shurui Yan Chen Wu Yixin Zhang Generative design for architectural spatial layouts: a review of technical approaches Journal of Asian Architecture and Building Engineering architectural spatial layout generative design artificial intelligence knowledge-driven data-driven |
| title | Generative design for architectural spatial layouts: a review of technical approaches |
| title_full | Generative design for architectural spatial layouts: a review of technical approaches |
| title_fullStr | Generative design for architectural spatial layouts: a review of technical approaches |
| title_full_unstemmed | Generative design for architectural spatial layouts: a review of technical approaches |
| title_short | Generative design for architectural spatial layouts: a review of technical approaches |
| title_sort | generative design for architectural spatial layouts a review of technical approaches |
| topic | architectural spatial layout generative design artificial intelligence knowledge-driven data-driven |
| url | http://dx.doi.org/10.1080/13467581.2025.2512235 |
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