Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866912176935010304 |
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| author | He, Zhili Wang, Yu-Hsing Zhang, Jian |
| author_facet | He, Zhili Wang, Yu-Hsing Zhang, Jian |
| contents | AI-based 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 intelligent design pipeline that integrates 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 on CAD drawings. Second, a two-stage generation framework incorporating generative AI (TGAI), inspired by the human drawing process, is designed to simplify the complexity of the design problem. Third, for the 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, timesteps, and physical conditions, by integrating well-designed attention modules. Additionally, a new evaluation system including objective and subjective measures is designed to evaluate models' performance, complementing the evaluation system in the traditional methods. The quantitative results demonstrate that PCDM significantly surpasses recent SOTA techniques across both measures. The qualitative results highlight PCDM's superior capabilities in generating high-perceptual-quality 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 generation and generalization abilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_04052 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI He, Zhili Wang, Yu-Hsing Zhang, Jian Machine Learning Computer Vision and Pattern Recognition AI-based 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 intelligent design pipeline that integrates 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 on CAD drawings. Second, a two-stage generation framework incorporating generative AI (TGAI), inspired by the human drawing process, is designed to simplify the complexity of the design problem. Third, for the 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, timesteps, and physical conditions, by integrating well-designed attention modules. Additionally, a new evaluation system including objective and subjective measures is designed to evaluate models' performance, complementing the evaluation system in the traditional methods. The quantitative results demonstrate that PCDM significantly surpasses recent SOTA techniques across both measures. The qualitative results highlight PCDM's superior capabilities in generating high-perceptual-quality 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 generation and generalization abilities. |
| title | Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.04052 |