LLM 기반 BIM 모델링 평가 및 피드백 프레임워크의 설계와 실증적 효과

• 영문명: Empirical Validation of wise‑BIM: A Large‑Language‑Model- Driven Framework for BIM Modeling Evaluation and Feedback
• 발행기관: 한국BIM학회
• 저자명: 김도영(Do Young Kim) 조선주(Sun Joo Cho)
• 간행물 정보: 『KIBIM Magazine』15권 3호, 57~64쪽, 전체 8쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2025.09.30

국문 초록

This study proposes wise-BIM, a BIM modeling evaluation and feedback framework that embeds large language models (LLMs), and empirically validates its effectiveness in civil-engineering design and checking scenarios. The framework comprises three stages: (A) structuring decision nodes, (B) defining purpose-centered elements, and (C) a RAG-prompt conversational feedback loop. By integrating static resources with dynamic reasoning, wise-BIM provides contextualized guidance to modelers without training on project files. In a minimum-viable expert study across representative tasks, the LLM-RAG condition, compared with manual work, improved goal refinement, reduced error-detection time, decreased rework cycles, lowered cognitive workload (NASA-TLX), reduced clarification requests (Clarify count), and increased usability (SUS). We also observed model-specific trade-offs between communication efficiency and the depth of goal elaboration, informing practical model-selection strategies. These findings indicate that conversational feedback by LLMs can mitigate the adaptability and explainability limitations of rule-based automation in BIM and provide a human-in-the-loop pathway for quality control during modeling.

목차

1. 서 론

2. 이론적 고찰

3. 방법론

4. 토의 및 시사점

5. 결 론

References

키워드

대규모 언어 모델 (LLM) 검색 기반 생성 (RAG) 의사결정 지원 오류 탐지 시스템 사용성 척도 (SUS) 토목 인프라 1)정회원 BIM Large Language Model (LLM) Retrieval‑Augmented Generation (RAG) Decision Support Error Detection NASA‑TLX System Usability Scale (SUS) Civil Infrastructure