Automated structural design of shear wall residential buildings using generative adversarial networks

• Published in: Automation in construction 2021-12, Vol.132, p.103931, Article 103931
• Main Authors: Liao, Wenjie, Lu, Xinzheng, Huang, Yuli, Zheng, Zhe, Lin, Yuanqing
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Automation; Building design; Computer vision; Data and hyper-parametric analytics; Design techniques; Generative adversarial network; Generative adversarial networks; High rise buildings; Intelligent structural design; Parameterization; Residential buildings; Seismic design; Shear wall system; Shear walls; Structural design; Theological schools
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2021.103931

Artificial intelligence is reshaping building design processes to be smarter and automated. Considering the increasingly wide application of shear wall systems in high-rise buildings and envisioning the massive benefit of automated structural design, this paper proposes a generative adversarial network (GAN)-based shear wall design method, which learns from existing shear wall design documents and then performs structural design intelligently and swiftly. To this end, structural design datasets were prepared via abstraction, semanticization, classification, and parameterization in terms of building height and seismic design category. The GAN model improved its shear wall design proficiency through adversarial training supported by data and hyper-parametric analytics. The performance of the trained GAN model was appraised against the metrics based on the confusion matrix and the intersection-over-union approach. Finally, case studies were conducted to evaluate the applicability, effectiveness, and appropriateness of the innovative GAN-based structural design method, indicating significant speed-up and comparable quality. •An generative adversarial network-based automated structural design framework.•An open-access datasets of structural design drawings.•The datasets pre-processing method via pioneering abstraction, semanticization, classification, and parameterization.•Model validation approach based on confusion matrix and intersection-over-union metrics.