A human-machine integrated optimization method for long walkway space
With the development of cities, the design of long walkway spaces have exposed a series of issues. These spaces suffer from layout inefficiencies due to the complex interaction of multiple factors, affecting walking experiences. Optimization schemes for such spaces typically involve the calculation...
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| Format: | Article |
| Language: | English |
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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/10.1080/13467581.2025.2454611 |
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| _version_ | 1832096648437694464 |
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| author | Yating Wang Zijun Wang Hui Wang |
| author_facet | Yating Wang Zijun Wang Hui Wang |
| author_sort | Yating Wang |
| collection | DOAJ |
| description | With the development of cities, the design of long walkway spaces have exposed a series of issues. These spaces suffer from layout inefficiencies due to the complex interaction of multiple factors, affecting walking experiences. Optimization schemes for such spaces typically involve the calculation and evaluation of various influencing factors, necessitating comprehensive and systematic judgment. In recent years, the advancement of computer technology has made it possible to utilize genetic algorithms for multi-objective optimization of spatial layouts. Through human-machine collaboration based on genetic algorithms and expert judgment, diverse schemes can be generated more quickly and the optimal solution can be effectively selected. This article aims to explore how to use generative technology combined with expert judgment in a human-machine collaboration approach to address the multi-objective optimization of long walkway spaces. Research results indicate that this process can enhance the efficiency and quality of optimizing layouts for long walkway spaces. After further refinement through manual comparison, the rationality of the results is improved, achieving optimization effects based on the previous layouts. In the future, this method can be promoted to other areas and application scenarios within urban design and planning, demonstrating significant potential for development. |
| format | Article |
| id | doaj-art-1e711826f7944a0789e9fa4bddbbd257 |
| 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-1e711826f7944a0789e9fa4bddbbd2572025-02-05T12:46:13ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-01-010012410.1080/13467581.2025.24546112454611A human-machine integrated optimization method for long walkway spaceYating Wang0Zijun Wang1Hui Wang2Tsinghua UniversityTsinghua UniversityTsinghua UniversityWith the development of cities, the design of long walkway spaces have exposed a series of issues. These spaces suffer from layout inefficiencies due to the complex interaction of multiple factors, affecting walking experiences. Optimization schemes for such spaces typically involve the calculation and evaluation of various influencing factors, necessitating comprehensive and systematic judgment. In recent years, the advancement of computer technology has made it possible to utilize genetic algorithms for multi-objective optimization of spatial layouts. Through human-machine collaboration based on genetic algorithms and expert judgment, diverse schemes can be generated more quickly and the optimal solution can be effectively selected. This article aims to explore how to use generative technology combined with expert judgment in a human-machine collaboration approach to address the multi-objective optimization of long walkway spaces. Research results indicate that this process can enhance the efficiency and quality of optimizing layouts for long walkway spaces. After further refinement through manual comparison, the rationality of the results is improved, achieving optimization effects based on the previous layouts. In the future, this method can be promoted to other areas and application scenarios within urban design and planning, demonstrating significant potential for development.http://dx.doi.org/10.1080/13467581.2025.2454611long walkway spacewalking experience assessmentgenetic algorithmgenerative designhuman-machine collaboration |
| spellingShingle | Yating Wang Zijun Wang Hui Wang A human-machine integrated optimization method for long walkway space Journal of Asian Architecture and Building Engineering long walkway space walking experience assessment genetic algorithm generative design human-machine collaboration |
| title | A human-machine integrated optimization method for long walkway space |
| title_full | A human-machine integrated optimization method for long walkway space |
| title_fullStr | A human-machine integrated optimization method for long walkway space |
| title_full_unstemmed | A human-machine integrated optimization method for long walkway space |
| title_short | A human-machine integrated optimization method for long walkway space |
| title_sort | human machine integrated optimization method for long walkway space |
| topic | long walkway space walking experience assessment genetic algorithm generative design human-machine collaboration |
| url | http://dx.doi.org/10.1080/13467581.2025.2454611 |
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