| Title |
Development of a Generative AI-Based Support System for Risk Assessment and Visual Materials at Construction Sites |
| Authors |
이강혁(Lee, Kanghyeok) ; 조재홍(Cho, Jaehong) ; 김영환(Kim, Yeonghwan) ; 강상혁(Kang, Sanghyeok) |
| DOI |
https://doi.org/10.12652/Ksce.2026.46.4.351 |
| Keywords |
위험성평가, 인공지능, 검색증강생성, 안전관리, 위험인자 Safety management, Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), Risk assessment, Hazard factors |
| Abstract |
The construction industry records the highest proportion of occupational fatalities among all industries, and risk assessment has been made legally mandatory to strengthen on-site safety management and prevent industrial accidents. However, risk assessments are often conducted in a perfunctory manner due to limitations in manpower and time, and evaluation consistency may be compromised by reliance on the subjective judgment of individual safety managers. To improve the effectiveness of risk assessment and enhance workers' risk awareness, this study constructed a vector database from accident case and legal data using the Text-Embedding-3-Large model. Relevant cases were retrieved through retrieval-augmented generation (RAG)-based similarity search, and a multimodal generative AI-based support system was developed to automatically generate risk assessment sheets after verifying the relevance of accident cases using GPT-4o. The system also integrates legal information search, additional accident case search, and accident case-based visual material generation using the gpt-image-1 model. A survey conducted with nine safety managers with risk assessment experience yielded scores of 4.56 for educational effectiveness, 4.33 for efficiency, 4.11 for satisfaction, 3.89 for reliability, and 3.78 for effectiveness, confirming its potential for improvement over conventional methods. Respondents also noted that the system reduces repetitive documentation burden by enabling rapid access to accident cases and legal information through simple inputs, and that accident case-based visual materials were effective in improving understanding of hazard factors and supporting worker safety education. The proposed system can serve as a tool to enhance the practical effectiveness of risk assessment and support worker safety education at construction sites, and future research should expand its application to a wider range of trades and construction environments. |