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

arXiv: https://arxiv.org/abs/2506.20551 · HTML: https://mlsanity.com/paper/2506.20551/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI](https://mlsanity.com/paper/2311.04052/) (2023)
- [Using Large Language Models for the Interpretation of Building Regulations](https://mlsanity.com/paper/2407.21060/) (2024)
- [Interactive Design by Integrating a Large Pre-Trained Language Model and Building Information Modeling](https://mlsanity.com/paper/2306.14165/) (2023)
- [Towards French Smart Building Code: Compliance Checking Based on Semantic Rules](https://mlsanity.com/paper/1910.00334/) (2019)
- [Strategically using Applied Machine Learning for Accessibility Documentation in the Built Environment](https://mlsanity.com/paper/2107.14506/) (2021)

## Built on by (most cited first, 10 of 10)

- [Text-to-Layout: A Generative Workflow for Drafting Architectural Floor Plans Using LLMs](https://mlsanity.com/paper/2509.00543/) (2025)
- [StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows](https://mlsanity.com/paper/2607.14896/) (2026)
- [Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models](https://mlsanity.com/paper/2509.17283/) (2025)
- [Automatic Building Code Review: A Case Study](https://mlsanity.com/paper/2510.02634/) (2025)
- [Developing an AI Assistant for Knowledge Management and Workforce Training in State DOTs](https://mlsanity.com/paper/2603.03302/) (2026)
- [Towards an automated AI-based framework for floor plan compliance checks for residential buildings](https://mlsanity.com/paper/2607.00015/) (2026)
- [Neuro-Symbolic AI for LEED compliance: Document-Centric Benchmarking, Deterministic Numeric Checking, and When Multimodal Hurts](https://mlsanity.com/paper/2607.15647/) (2026)
- [ARCHER: Agentic Rule and Compliance Harness for Executable Regulations](https://mlsanity.com/paper/2607.25566/) (2026)
- [Training-Free Agentic Computer Vision for Structural Component Detection in 2D Structural Framing Plans](https://mlsanity.com/paper/2608.17237/) (2026)
- [K-OPSD: Verifiable On-Policy Self-Distillation for Post-Training Vision-Language Models on AEC Drawings](https://mlsanity.com/paper/2609.34082/) (2026)

## Abstract (arXiv)

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.