Text2BIM: An LLM-based Multi-Agent Framework Facilitating the Expression of Design Intentions more Intuitively

Text2BIM: An LLM-based Multi-Agent Framework Facilitating the Expression of Design Intentions more Intuitively

Practical Solutions for Building Information Modeling (BIM)

Using Advanced Language Models

Recent research has shown that large language models (LLMs) can automate wall features in building design software, allowing designers to express their ideas using natural language instead of complex modeling commands.

Introducing Text2BIM

A new architecture, Text2BIM, uses LLM-based agents with specific roles to improve user instructions, develop construction plans, analyze requirements, and optimize coding. This collaborative approach enhances efficiency and effectiveness.

Tool Functionality Improvement

Quantitative and qualitative analysis of user commands in BIM software log files has led to the development of tool functionalities that address common design needs, ensuring the framework meets user preferences.

Implementation in BIM Authoring Tools

The suggested framework has been incorporated into Vectorworks, a BIM authoring tool, using modern frontend and backend technologies, making the tool interface easy to use and understand.

Evaluation and Result

The framework has been tested with different LLMs, producing well-structured building models consistent with user-specified design concepts. The approach focuses on generating regular building models during the early design stage.

Value of Text2BIM

The Text2BIM framework enables designers to express design intent more intuitively, automating repetitive modeling commands and freeing designers from these tasks.

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