Unified Network-Based Representation of BIM Models for Embedding Semantic, Spatial, and Topological Data
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911232856948736 |
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| author | Han, Jin Lu, Xin-Zheng Lin, Jia-Rui |
| author_facet | Han, Jin Lu, Xin-Zheng Lin, Jia-Rui |
| contents | Building Information Modeling (BIM) has revolutionized the construction industry by providing a comprehensive digital representation of building structures throughout their lifecycle. However, existing research lacks effective methods for capturing the complex spatial and topological relationships between components in BIM models, which are essential for understanding design patterns and enhancing decision-making. This study proposes a unified network-based representation method that integrates the "semantic-spatial-topological" multi-dimensional design features of BIM models. By extending the IFC (Industry Foundation Classes) standard, we introduce local spatial relationships and topological connections between components to enrich the network structure. This representation method enables a more detailed understanding of component interactions, dependencies, and implicit design patterns, effectively capturing the semantic, topological, and spatial relationships in BIM, and holds significant potential for the representation and learning of design patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22670 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unified Network-Based Representation of BIM Models for Embedding Semantic, Spatial, and Topological Data Han, Jin Lu, Xin-Zheng Lin, Jia-Rui Computational Engineering, Finance, and Science Machine Learning Building Information Modeling (BIM) has revolutionized the construction industry by providing a comprehensive digital representation of building structures throughout their lifecycle. However, existing research lacks effective methods for capturing the complex spatial and topological relationships between components in BIM models, which are essential for understanding design patterns and enhancing decision-making. This study proposes a unified network-based representation method that integrates the "semantic-spatial-topological" multi-dimensional design features of BIM models. By extending the IFC (Industry Foundation Classes) standard, we introduce local spatial relationships and topological connections between components to enrich the network structure. This representation method enables a more detailed understanding of component interactions, dependencies, and implicit design patterns, effectively capturing the semantic, topological, and spatial relationships in BIM, and holds significant potential for the representation and learning of design patterns. |
| title | Unified Network-Based Representation of BIM Models for Embedding Semantic, Spatial, and Topological Data |
| topic | Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2505.22670 |