Generative AI is reshaping structural design; however, current generative AI-based methods primarily focus on enhancing the AI models employed, overlooking another critical factor that influences outcomes: the generation framework. Existing frameworks fall into two categories: the end-to-end generation framework incorporating generative AI (EGAI), which includes EGAI-GAN and EGAI-DM, and is widely adopted by nearly all current methods; and the two-stage GAI (TGAI), specifically TGAI-DM + PCDM, proposed by generative AIBIM—one of the most representative pipelines. Generative AIBIM demonstrates, from a theoretical standpoint, that TGAI is more effective than EGAI; however, it lacks empirical validation. This paper designs EGAI-DM + PCDM and conducts a comparative analysis with TGAI-DM + PCDM. The experimental results indicate TGAI-DM + PCDM enables AI models to generate design drawings with greater precision and enhanced perceptual quality compared to EGAI-DM + PCDM. These findings substantiate that TGAI is indeed more effective than EGAI. We believe this conclusion will inspire further exploration and broader applications of TGAI.
He et al. (Wed,) studied this question.