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

2025-06-25

AI Summary

To address the time-consuming and error-prone nature of manual BIM code compliance checking, this paper proposes a semi-automated review method based on fusion of multiple large language models (LLMs)—GPT, Claude, Gemini, and Llama—deeply integrated into the Revit platform for the first time. The method leverages natural language processing to parse building codes and automatically generate Python scripts that invoke the Revit API to execute rule-based validation. It supports flexible adaptation to diverse regulatory frameworks and intelligent identification of non-compliant elements. Evaluated on residential and office projects, the approach significantly reduces review time and achieves high accuracy in detecting critical compliance issues—such as room dimensions and material specifications—while generating structured, actionable reports. This work overcomes the rigidity of traditional rule engines, establishing a scalable, interpretable, AI-augmented paradigm for BIM compliance auditing.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Automating BIM code compliance to reduce manual effort and errors
• Integrating LLMs with Revit for semi-automated regulation checks
• Streamlining violation detection in room dimensions and materials

Innovation

• LLM-driven semi-automated BIM compliance checking
• Integration of LLMs with Revit software
• Automated violation identification and reporting