Large Language Model-Driven Code Compliance Checking in Building Information Modeling

TL;DR

This paper presents a semi-automated system using large language models integrated with BIM software to improve the efficiency and accuracy of building code compliance checking, reducing manual effort and errors.

Contribution

It introduces a novel LLM-driven approach that interprets building codes and automates compliance checks within BIM environments, enhancing current manual processes.

Findings

Reduced compliance checking time and effort

Improved accuracy in violation detection

Automated generation of compliance reports

Abstract

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.