BIM Knowledge Expert Agent Research Based on LLM and RAG

Tae-Wook Kang; Seung-Hwa Park

doi:10.13161/kibim.2024.14.3.022

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2024 Vol.14, Issue 3 Preview Page Next Page

Research Article

BIM Knowledge Expert Agent Research Based on LLM and RAG LLM과 RAG 기반 BIM 지식 전문가 에이전트 연구

30 September 2024. pp. 22-30

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Abstract

Recently, LLM (Large Language Model), a rapidly developing generative AI technology, is receiving much attention in the smart construction field. This study proposes a methodology for implementing an knowledge expert system by linking BIM (Building Information Modeling), which supports data hub functions in the smart construction domain with LLM. In order to effectively utilize LLM in a BIM expert system, excessive model learning costs, BIM big data processing, and hallucination problems must be solved. This study proposes an LLM-based BIM expert system architecture that considers these problems. This study focuses on the RAG (Retrieval-Augmented Generation) document generation method and search algorithm for effective BIM data retrieval, with the goal of implementing an LLM-based BIM expert system within a small GPU resource. For performance comparison and analysis, a prototype of the designed system is developed, and implications to be considered when developing an LLM-based BIM expert system are derived.

Keywords

BIM

IFC

LLM

Knowledge

Expert

RAG

Process

Retriever

키워드

BIM

IFC

대형언어모델

지식

전문가

검색증강생성

프로세스

검색자

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Information

Publisher :Journal of KIBIM
Publisher(Ko) :한국BIM학회
Journal Title :Journal of KIBIM
Journal Title(Ko) :한국BIM학회논문집
Volume : 14
No :3
Pages :22-30
Received Date : 2024-08-22; 2024-09-07
Accepted Date : 2024-09-12
DOI :https://doi.org/10.13161/kibim.2024.14.3.022

Journal of KIBIM ISSN:2288-1697(Print) 2288-1697(Online) 한국BIM학회논문집

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