Using artificial neural network and WebGL to algorithmically optimize window wall ratios of high-rise office buildings. Issue 2 (9th February 2021)

• Record Type: Journal Article
• Authors: Zhao, Shenghuan
• Is Part Of: Journal of computational design and engineering. Volume 8:Issue 2(2021)
• Page Start: 638
• Page End: 653
• Publication Date: 2021-02-09
• Subjects: building façade design -- design automation -- algorithm-aided design -- building performance simulation -- neural network; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwab005
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: By coupling parametric modeling, building performance simulation engines, and optimization algorithms, optimal design choices regarding predefined building performance objectives can be automatically obtained. This becomes an emerging research topic among scholars in the fields of architecture and built environment. However, it is not easy to apply this method to real building design projects, because of two main drawbacks: Building performance simulation is too time consuming, and the numerical visualization of final results is not intuitive for architects to make decisions. Therefore, this study tries to fill these two gaps by training an artificial neural network to replace simulation engines and developing a web application to speed up the 3D visualization of selected design choices. These two strategies are applied to optimize office towers' window wall ratios in Hangzhou, China. Architects working on new design projects in that city can obtain the optimal group of window wall ratios for four facades in 2 s, faster than using simulation engines, which cost architects 2 weeks. Moreover, architects can also efficiently observe the appearance of design solutions with the web application. By improving its usability from these two aspects, this study significantly improves the applicability of algorithmic optimization for building design projects. Graphical Abstract: