BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrieval
This work addresses the problem of inefficient BIM information retrieval for the construction industry, reducing engineering efforts and training data needs, though it is incremental as it adapts existing GPT technology to a specific domain.
The paper tackles the challenge of retrieving information from building information models (BIMs) by introducing BIM-GPT, a prompt-based virtual assistant framework that uses GPT models to interpret natural language queries, achieving 83.5% and 99.5% accuracy in classification tests with no data and 2% data in prompts, respectively.
Efficient information retrieval (IR) from building information models (BIMs) poses significant challenges due to the necessity for deep BIM knowledge or extensive engineering efforts for automation. We introduce BIM-GPT, a prompt-based virtual assistant (VA) framework integrating BIM and generative pre-trained transformer (GPT) technologies to support NL-based IR. A prompt manager and dynamic template generate prompts for GPT models, enabling interpretation of NL queries, summarization of retrieved information, and answering BIM-related questions. In tests on a BIM IR dataset, our approach achieved 83.5% and 99.5% accuracy rates for classifying NL queries with no data and 2% data incorporated in prompts, respectively. Additionally, we validated the functionality of BIM-GPT through a VA prototype for a hospital building. This research contributes to the development of effective and versatile VAs for BIM IR in the construction industry, significantly enhancing BIM accessibility and reducing engineering efforts and training data requirements for processing NL queries.