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.

BIM-GPT 是一种基于 BIM 和 GPT 技术的基于提示的 VA 框架，可用于从建筑信息模型中有效地提取信息，提高 BIM 可访问性，降低工程努力和处理自然语言查询所需的训练数据要求和难度。