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September 27, 2025Open Access

Automated Checking of Highway Bridge BIM Models Based on Large Language Models

Key Points

The automated framework achieves 79.5% accuracy in rule extraction from design specifications.
BIM attributes are compared with rules, resulting in 84.4% precision for checking compliance.
The study proposes a more efficient alternative to manual review processes in bridge engineering.
This approach has potential implications for improving engineering practices in compliance-checking.

Abstract

Building Information Modeling (BIM) is widely used in highway bridge engineering, making compliance with complex design specifications crucial. Existing checks rely heavily on manual review, which is time-consuming and inefficient. This study proposes an automated framework using large language models (LLMs) to parse unstructured design specifications and extract structured rules with 79.5% accuracy, stored in a knowledge graph. IFC-formatted BIM component attributes are then compared with these rules to check structural completeness and compliance, achieving 84.4% precision. The results indicate that the framework offers an effective solution for automated rule extraction and has the potential to improve compliance-checking efficiency and accuracy in engineering practice.

Automated Checking of Highway Bridge BIM Models Based on Large Language Models

Key Points

Abstract

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