Dual generative adversarial networks for automated component layout design of steel frame-brace structures

With the development of artificial intelligence (AI), it gains in popularity to use AI to solve problems in civil engineering. However, the research on AI is mainly focused on the field of structural health monitoring, and less on the field of structural design. As one new direction in the AI domain, the generative adversarial network (GAN) method has developed rapidly, which is able to synthesize high-quality images based on demand. Therefore, it opens a new window for AI-aided automatic structure design. In this paper, a novel GAN-based method, namely FrameGAN, is proposed to realize automated component layout design of steel frame-brace structures. By collecting and processing drawings designed by senior structural engineers, FrameGAN and two mainstream GAN models (pix2pix and pix2pixHD) are tested and compared, which demonstrates the superiority of the proposed FrameGAN. In addition, the design results of FrameGAN are compared and analyzed with those of senior structural engineers based on two unique evaluation metrics, i.e., expert grading and objective comparison. The results show that the design of FrameGAN is close to that of structural engineers, which indicates the availability of FrameGAN in the component layout design of steel frame-brace structures.