Large Language Model-Driven Code Compliance Checking in Building Information Modeling
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911021915963392 |
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| author | Madireddy, Soumya Gao, Lu Din, Zia Kim, Kinam Senouci, Ahmed Han, Zhe Zhang, Yunpeng |
| author_facet | Madireddy, Soumya Gao, Lu Din, Zia Kim, Kinam Senouci, Ahmed Han, Zhe Zhang, Yunpeng |
| contents | This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family residential project and an office building project demonstrated the system's ability to reduce the time and effort required for compliance checks while improving accuracy. It streamlined the identification of violations, such as non-compliant room dimensions, material usage, and object placements, by automatically assessing relationships and generating actionable reports. Compared to manual methods, the system eliminated repetitive tasks, simplified complex regulations, and ensured reliable adherence to standards. By offering a comprehensive, adaptable, and cost-effective solution, this proposed approach offers a promising advancement in BIM-based compliance checking, with potential applications across diverse regulatory documents in construction projects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20551 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model-Driven Code Compliance Checking in Building Information Modeling Madireddy, Soumya Gao, Lu Din, Zia Kim, Kinam Senouci, Ahmed Han, Zhe Zhang, Yunpeng Software Engineering Artificial Intelligence This research addresses the time-consuming and error-prone nature of manual code compliance checking in Building Information Modeling (BIM) by introducing a Large Language Model (LLM)-driven approach to semi-automate this critical process. The developed system integrates LLMs such as GPT, Claude, Gemini, and Llama, with Revit software to interpret building codes, generate Python scripts, and perform semi-automated compliance checks within the BIM environment. Case studies on a single-family residential project and an office building project demonstrated the system's ability to reduce the time and effort required for compliance checks while improving accuracy. It streamlined the identification of violations, such as non-compliant room dimensions, material usage, and object placements, by automatically assessing relationships and generating actionable reports. Compared to manual methods, the system eliminated repetitive tasks, simplified complex regulations, and ensured reliable adherence to standards. By offering a comprehensive, adaptable, and cost-effective solution, this proposed approach offers a promising advancement in BIM-based compliance checking, with potential applications across diverse regulatory documents in construction projects. |
| title | Large Language Model-Driven Code Compliance Checking in Building Information Modeling |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20551 |