Generative Structural Design Integrating BIM and Diffusion Model

• URL: http://arxiv.org/abs/2311.04052v1
• Date: Tue, 7 Nov 2023 15:05:19 GMT
• Title: Generative Structural Design Integrating BIM and Diffusion Model
• Authors: Zhili He, Yu-Hsing Wang, Jian Zhang
• Abstract summary: This study introduces building information modeling ( BIM) into intelligent structural design and establishes a structural design pipeline integrating BIM and generative AI. In terms of generation framework, inspired by the process of human drawing, a novel 2-stage generation framework is proposed to reduce the generation difficulty for AI models. 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.
• Score: 4.619347136761891
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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