인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This paper proposes a BIM-SKEY automatic mapping framework for piping spool drawing automation. The framework extracts 24-dimensional expert-defined features consisting of meta- data, geometry, and connector information from Revit family JSON data generated by a Revit API-based add-in. A LightGBM classifier classifies piping components into twelve groups. Model evaluation using Stratified 5-Fold Cross-Validation on 3,815 samples from 180 BIM models achieved 94.84% group classification accuracy without GPU resources, and the end-to-end mapping accuracy up to the SKEY symbol-pattern level reached 91.35%. To improve prac- tical applicability, a confidence-based routing strategy was introduced using the predict_proba output of LightGBM. High-confidence predictions were automatically applied, while uncertain cases were routed to engineer review or manual processing. The classified groups were assigned to SKEY symbol patterns using predefined mapping rules, after which the final SKEY codes were determined from component attributes and the Ez-ISO symbol definition file. A two-layer construction data integration model was also proposed to connect BIM design and construction progress data. Experimental results showed that the proposed framework improved the ISO drawing automation success rate from 58.3% to 91.8% and achieved 94.7% consistency between BIM design data and the construction data model, demonstrating its applicability to industrial BIM environments.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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