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A Methodology for Using ChatGPT to Improve BIM-based Design Data Evaluation System

BIM기반 설계데이터 평가 시스템 개선을 위한 ChatGPT활용 방법론

  • 유은상 (한양대학교 대학원 스마트시티공학과) ;
  • 김구택 ((주)코스펙이노랩) ;
  • 안용한 (한양대학교 ERICA 건축공학과) ;
  • 최중식 (강원대학교 건설융합학부 건축학전공)
  • Received : 2024.06.18
  • Accepted : 2024.06.20
  • Published : 2024.06.30

Abstract

This study proposes a new methodology to increase the flexibility and efficiency of the design data evaluation system by combining Building Information Modeling (BIM) technology in the architectural industry, OpenAI's interactive artificial intelligence, and ChatGPT. BIM technology plays an important role in digitally modeling and managing architectural information. Since architectural information is included, research and development are underway to review and evaluate BIM data according to conditions through program development. However, in the process of reviewing BIM design data, if the review criteria or evaluation criteria according to design change occur frequently, it is necessary to update the program anew. In order for designers or reviewers to apply the changed criteria, requesting a program developer will delay time. This problem was studied by using ChatGPT to modify and update the design data evaluation program code in real time. In this study, it is aimed to improve the changing standards and accuracy by enabling programming non-professionals to change the design regulations and calculation standards of the BIM evaluation program system using ChatGPT. In this study, in the BIM-based design certification automation evaluation program, a program in which the automation evaluation method is being studied based on the design certification evaluation manual was first used. In the design certification automation evaluation program, the programming non-majors checked the automation evaluation code by linking ChatGPT, and the changed calculation criteria were created and modified interactively. As a result of the evaluation, the change in the calculation standard was explained to ChatGPT and the applied result was confirmed.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 2024년도 지원으로 수행되었음(과제번호 : RS-2021-KA163269).

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