Cost-aware generative design for urban ‘cool spots’: A random forest-principal component analysis-augmented combinatorial optimization approach
•A novel generative design approach is proposed for affordable urban cool spots.•Random Forest is deployed to accelerate simulating outdoor thermal environment.•Principal Component Analysis merges cost indicators into one objective function.•Genetic algorithm generates mass-based design layouts base...
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| Vydáno v: | Energy and buildings Ročník 295; s. 113317 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
15.09.2023
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| ISSN: | 0378-7788 |
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| Abstract | •A novel generative design approach is proposed for affordable urban cool spots.•Random Forest is deployed to accelerate simulating outdoor thermal environment.•Principal Component Analysis merges cost indicators into one objective function.•Genetic algorithm generates mass-based design layouts based on the Pareto front.•Average temperature decreases by 0.17 °C and costs by 82.57% in the case study.
Whilst designing cool small neighborhoods (called ‘cool spots’ in this paper) remains an enormous technical challenge, clients and their designers are also confronting with the perpetual burden of the financial sphere. This research aims to develop a novel methodological approach for designers to search for affordable cool spots in dense urban areas. It does so by conducting genetic combinatorial optimizations augmented by Random Forest (RF) and Principal Component Analysis (PCA) algorithms. What is particularly innovative is to develop a mass-based generative design approach to produce neighborhood options for the subsequent combinatorial optimization. The methodology is tested in a real-world urban renewal project in Hong Kong, which is epitomized by high density and hot and humid weather in the summer. The results show that the design approach can automatically identify high-performance schemes of cool spot design, reducing the daily average thermophysiological equivalent temperature from averagely 29.76 °C to at lowest 29.59 °C, and decreasing the construction cost by 82.57%. With proper translation, the approach can serve as a useful and robust design assisting tool for designing and developing cool and cost-aware buildings and neighborhoods in urban areas. |
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| AbstractList | •A novel generative design approach is proposed for affordable urban cool spots.•Random Forest is deployed to accelerate simulating outdoor thermal environment.•Principal Component Analysis merges cost indicators into one objective function.•Genetic algorithm generates mass-based design layouts based on the Pareto front.•Average temperature decreases by 0.17 °C and costs by 82.57% in the case study.
Whilst designing cool small neighborhoods (called ‘cool spots’ in this paper) remains an enormous technical challenge, clients and their designers are also confronting with the perpetual burden of the financial sphere. This research aims to develop a novel methodological approach for designers to search for affordable cool spots in dense urban areas. It does so by conducting genetic combinatorial optimizations augmented by Random Forest (RF) and Principal Component Analysis (PCA) algorithms. What is particularly innovative is to develop a mass-based generative design approach to produce neighborhood options for the subsequent combinatorial optimization. The methodology is tested in a real-world urban renewal project in Hong Kong, which is epitomized by high density and hot and humid weather in the summer. The results show that the design approach can automatically identify high-performance schemes of cool spot design, reducing the daily average thermophysiological equivalent temperature from averagely 29.76 °C to at lowest 29.59 °C, and decreasing the construction cost by 82.57%. With proper translation, the approach can serve as a useful and robust design assisting tool for designing and developing cool and cost-aware buildings and neighborhoods in urban areas. |
| ArticleNumber | 113317 |
| Author | Tang, Xu Hao, Tongping Webster, Chris Peng, Ziyu Lu, Weisheng Huang, Jianxiang |
| Author_xml | – sequence: 1 givenname: Ziyu orcidid: 0000-0002-2541-7537 surname: Peng fullname: Peng, Ziyu organization: Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong – sequence: 2 givenname: Weisheng surname: Lu fullname: Lu, Weisheng email: wilsonlu@hku.hk organization: Department of Real Estate and Construction, Faculty of Architecture, The University of Hong Kong, Hong Kong – sequence: 3 givenname: Tongping surname: Hao fullname: Hao, Tongping organization: Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Hong Kong – sequence: 4 givenname: Xu orcidid: 0000-0002-7361-0913 surname: Tang fullname: Tang, Xu organization: Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Hong Kong – sequence: 5 givenname: Jianxiang orcidid: 0000-0002-0027-3944 surname: Huang fullname: Huang, Jianxiang organization: Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Hong Kong – sequence: 6 givenname: Chris orcidid: 0000-0002-2171-7495 surname: Webster fullname: Webster, Chris organization: Faculty of Architecture, The University of Hong Kong, Hong Kong |
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| Keywords | Construction cost Generative design Outdoor thermal comfort Design assistance tool Multi-objective combinatorial optimization |
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| SubjectTerms | Construction cost Design assistance tool Generative design Multi-objective combinatorial optimization Outdoor thermal comfort |
| Title | Cost-aware generative design for urban ‘cool spots’: A random forest-principal component analysis-augmented combinatorial optimization approach |
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