Artificial intelligence is transforming many industries and reshaping building design processes to be smarter and automated. While a large number of studies on automated building design have been carried out recently, they focused on architectural aspects, leaving a gap in its application to structural design. 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 shear-wall design automation model based on a generative adversarial network (GAN). Its goal is to learn from existing shear wall design documents and then perform structural design intelligently and swiftly. To this end, a database of representative architectural and structural design documents was developed. Then, 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.