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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.14840 |
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| _version_ | 1866929393693097984 |
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| author | Li, Zhipeng Li, Sichao Hinchcliffe, Geoff Maitless, Noam Birbilis, Nick |
| author_facet | Li, Zhipeng Li, Sichao Hinchcliffe, Geoff Maitless, Noam Birbilis, Nick |
| contents | The determination of space layout is one of the primary activities in the schematic design stage of an architectural project. The initial layout planning defines the shape, dimension, and circulation pattern of internal spaces; which can also affect performance and cost of the construction. When carried out manually, space layout planning can be complicated, repetitive and time consuming. In this work, a generative design framework for the automatic generation of spatial architectural layout has been developed. The proposed approach integrates a novel physics-inspired parametric model for space layout planning and an evolutionary optimisation metaheuristic. Results revealed that such a generative design framework can generate a wide variety of design suggestions at the schematic design stage, applicable to complex design problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14840 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated architectural space layout planning using a physics-inspired generative design framework Li, Zhipeng Li, Sichao Hinchcliffe, Geoff Maitless, Noam Birbilis, Nick Artificial Intelligence The determination of space layout is one of the primary activities in the schematic design stage of an architectural project. The initial layout planning defines the shape, dimension, and circulation pattern of internal spaces; which can also affect performance and cost of the construction. When carried out manually, space layout planning can be complicated, repetitive and time consuming. In this work, a generative design framework for the automatic generation of spatial architectural layout has been developed. The proposed approach integrates a novel physics-inspired parametric model for space layout planning and an evolutionary optimisation metaheuristic. Results revealed that such a generative design framework can generate a wide variety of design suggestions at the schematic design stage, applicable to complex design problems. |
| title | Automated architectural space layout planning using a physics-inspired generative design framework |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.14840 |