Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI

Abstract

AI-based intelligent structural design represents a transformative approach that addresses the inefficiencies inherent in traditional structural design practices. This paper innovates the existing AI-based design frameworks from four aspects and proposes Generative AIBIM: an automatic and intelligent structural design pipeline that integrates Building Information Modeling (BIM) and generative AI. First, the proposed pipeline not only broadens the application scope of BIM, which aligns with BIM's growing relevance in civil engineering, but also marks a significant supplement to previous methods that relied solely on CAD drawings. Second, in Generative AIBIM, a two-stage generation framework incorporating generative AI (TGAI), inspired by the human drawing process, is designed to simplify the complexity of the structural design problem. Third, for the generative AI model in TGAI, this paper pioneers to fuse physical conditions into diffusion models (DMs) to build a novel physics-based conditional diffusion model (PCDM). In contrast to conventional DMs, on the one hand, PCDM directly predicts shear wall drawings to focus on similarity, and on the other hand, PCDM effectively fuses cross-domain information, i.e., design drawings (image data), timesteps, and physical conditions, by integrating well-designed attention modules. Additionally, a new evaluation system including objective and subjective measures (i.e., $Scor{e}_{\text{IoU}}$ and $\text{FID}$) is designed to comprehensively evaluate models' performance, complementing the evaluation system in the traditional methods only adopting the objective metric. The quantitative results demonstrate that PCDM significantly surpasses recent state-of-the-art (SOTA) techniques (StructGAN and its variants) across both measures: $Scor{e}_{\text{IoU}}$ of PCDM is $30\%$ higher and $\text{FID}$ of PCDM is lower than $1/3$ of that of the best competitor. The qualitative experimental results highlight PCDM's superior capabilities in generating high perceptual quality design drawings adhering to essential design criteria. In addition, benefiting from the fusion of physical conditions, PCDM effectively supports diverse and creative designs tailored to building heights and seismic precautionary intensities, showcasing its unique and powerful generation and generalization capabilities. Associated ablation studies further demonstrate the effectiveness of our method.