Work

Leveraging large language models for BIM-based automated compliance checking

Year: 2025

Type: article

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.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2025.106707

Authors: Odin Iversen, Lizhen Huang

Institution: Norwegian University of Science and Technology

Language: English

Topic: BIM and Construction Integration

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $5,490

Funders: Norges Teknisk-Naturvitenskapelige Universitet, Norges Forskningsråd