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

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.