Data-driven optimization design method and tool platform for green residential area layout
In early design stage of residential area layouts, sustainable design and performance optimization are crucial for minimizing environmental impact of buildings. This work integrates environmentally-friendly sustainable design with artificial intelligence technology to enhance residential area layout...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-01-01
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| Series: | Journal of Asian Architecture and Building Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/13467581.2025.2459824 |
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| _version_ | 1832096688346497024 |
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| author | Shanshan Wang Dayu Zhang Xiaosai Hao Jia Liang Shanshan Li |
| author_facet | Shanshan Wang Dayu Zhang Xiaosai Hao Jia Liang Shanshan Li |
| author_sort | Shanshan Wang |
| collection | DOAJ |
| description | In early design stage of residential area layouts, sustainable design and performance optimization are crucial for minimizing environmental impact of buildings. This work integrates environmentally-friendly sustainable design with artificial intelligence technology to enhance residential area layouts. A parametric generative algorithm is developed to automatically generate design schemes for typical Chinese urban residential areas based on a sustainable, performance-oriented design flow. The workflow of architects is summarized into the following steps:1) Extraction of spatial form features from the residential area layout database;2) Automatic generation and sunlight duration simulation of new design schemes;3) Evaluation and screening of generated schemes. The generative algorithm is implemented using Rhino/Grasshopper, Python, and Matlab. In a case study involving residential area layouts in Beijing, the design scheme with the lowest values of DF, WinH, QuVue, SiteH, and UTCI among 42,691 automatically generated schemes was identified as the optimal scheme. This optimal scheme’s total load is 40.7% lower than the original scheme. To validate the design flow, two additional case studies were conducted. The results demonstrate that the parametric generative design of residential area layouts facilitates passive sustainable design in the early design stage and enhances environmental effects without increasing construction costs. |
| format | Article |
| id | doaj-art-9b59289e5ab8416c8f00e4b2d178f552 |
| institution | Kabale University |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-9b59289e5ab8416c8f00e4b2d178f5522025-02-05T12:46:13ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-01-010011710.1080/13467581.2025.24598242459824Data-driven optimization design method and tool platform for green residential area layoutShanshan Wang0Dayu Zhang1Xiaosai Hao2Jia Liang3Shanshan Li4Beijing University of Civil Engineering and ArchitectureBeijing University of Civil Engineering and ArchitectureBeijing University of Civil Engineering and ArchitectureBeijing University of Civil Engineering and ArchitectureBeijing University of Civil Engineering and ArchitectureIn early design stage of residential area layouts, sustainable design and performance optimization are crucial for minimizing environmental impact of buildings. This work integrates environmentally-friendly sustainable design with artificial intelligence technology to enhance residential area layouts. A parametric generative algorithm is developed to automatically generate design schemes for typical Chinese urban residential areas based on a sustainable, performance-oriented design flow. The workflow of architects is summarized into the following steps:1) Extraction of spatial form features from the residential area layout database;2) Automatic generation and sunlight duration simulation of new design schemes;3) Evaluation and screening of generated schemes. The generative algorithm is implemented using Rhino/Grasshopper, Python, and Matlab. In a case study involving residential area layouts in Beijing, the design scheme with the lowest values of DF, WinH, QuVue, SiteH, and UTCI among 42,691 automatically generated schemes was identified as the optimal scheme. This optimal scheme’s total load is 40.7% lower than the original scheme. To validate the design flow, two additional case studies were conducted. The results demonstrate that the parametric generative design of residential area layouts facilitates passive sustainable design in the early design stage and enhances environmental effects without increasing construction costs.http://dx.doi.org/10.1080/13467581.2025.2459824green residential areaslayout designartificial intelligencemulti-objective optimizationparametric design |
| spellingShingle | Shanshan Wang Dayu Zhang Xiaosai Hao Jia Liang Shanshan Li Data-driven optimization design method and tool platform for green residential area layout Journal of Asian Architecture and Building Engineering green residential areas layout design artificial intelligence multi-objective optimization parametric design |
| title | Data-driven optimization design method and tool platform for green residential area layout |
| title_full | Data-driven optimization design method and tool platform for green residential area layout |
| title_fullStr | Data-driven optimization design method and tool platform for green residential area layout |
| title_full_unstemmed | Data-driven optimization design method and tool platform for green residential area layout |
| title_short | Data-driven optimization design method and tool platform for green residential area layout |
| title_sort | data driven optimization design method and tool platform for green residential area layout |
| topic | green residential areas layout design artificial intelligence multi-objective optimization parametric design |
| url | http://dx.doi.org/10.1080/13467581.2025.2459824 |
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