Leveraging large language models for BIM-based automated compliance checking
Odin Iversen, Lizhen Huang
Abstract
Current methods of checking regulatory compliance in the architecture, engineering, construction, and operations (AECO) industry are mostly manual, time consuming and error prone. This paper, using design science research (DSR), proposes an artifact that leverages a large language model (LLM) for automated compliance checking (ACC) to directly interpret regulations, extract BIM data, execute checks, and generate detailed reports. For rule interpretation, the artifact achieves high F1-scores (97% for classification, 100% for dependency identification). For building model preparation, it correctly selected data extraction tools in 97% of cases. In rule execution, it demonstrated 97,7% accuracy and significantly outperformed a naive baseline, which highlights the need for a structured framework. Finally, the artifact generated detailed reports that included the LLM’s reasoning. The key finding is that an LLM-based reasoning engine enables a holistic approach that overcomes the manual rule digitization bottleneck in traditional ACC systems.