Generative Structural Design Integrating BIM and Diffusion Model
Zhili He Yu-Hsing Wang Jian Zhang
Intelligent structural design using AI can effectively reduce time overhead
and increase efficiency. It has potential to become the new design paradigm in
the future to assist and even replace engineers, and so it has become a
research hotspot in the academic community. However, current methods have some
limitations to be addressed, whether in terms of application scope, visual
quality of generated results, or evaluation metrics of results. This study
proposes a comprehensive solution. Firstly, we introduce building information
modeling (BIM) into intelligent structural design and establishes a structural
design pipeline integrating BIM and generative AI, which is a powerful
supplement to the previous frameworks that only considered CAD drawings. In
order to improve the perceptual quality and details of generations, this study
makes 3 contributions. Firstly, in terms of generation framework, inspired by
the process of human drawing, a novel 2-stage generation framework is proposed
to replace the traditional end-to-end framework to reduce the generation
difficulty for AI models. Secondly, in terms of generative AI tools adopted,
diffusion models (DMs) are introduced to replace widely used generative
adversarial network (GAN)-based models, and a novel physics-based conditional
diffusion model (PCDM) is proposed to consider different design prerequisites.
Thirdly, in terms of neural networks, an attention block (AB) consisting of a
self-attention block (SAB) and a parallel cross-attention block (PCAB) is
designed to facilitate cross-domain data fusion. The quantitative and
qualitative results demonstrate the powerful generation and representation
capabilities of PCDM. Necessary ablation studies are conducted to examine the
validity of the methods. This study also shows that DMs have the potential to
replace GANs and become the new benchmark for generative problems in civil
engineering.