Cost-aware generative design for urban ‘cool spots’: A random forest-principal component analysis-augmented combinatorial optimization approach

Abstract

<p>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.</p><ul></ul>