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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Energy and buildings Ročník 295; s. 113317
Hlavní autori: Peng, Ziyu, Lu, Weisheng, Hao, Tongping, Tang, Xu, Huang, Jianxiang, Webster, Chris
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 15.09.2023
Predmet:
Construction cost
Design assistance tool
Generative design
Multi-objective combinatorial optimization
Outdoor thermal comfort
Construction cost
Generative design
Outdoor thermal comfort
Design assistance tool
Multi-objective combinatorial optimization
ISSN:0378-7788
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•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.
ISSN:0378-7788
DOI:10.1016/j.enbuild.2023.113317