| Title |
A Study of Multi-Artificial Intelligence Agent-Based Risk Assessment in Construction Sites - A Qualitative Approach to Practical Competency - |
| Authors |
Byunghee Yoo ; Sungwon Ahn ; Chanbum Ryan Ahn |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2025.26.5.013 |
| Keywords |
Artificial Intelligence; Risk Assessment; Safety Management; Multi-Artificial Intelligence Agents |
| Abstract |
Risk assessment in construction projects is a critical process for preventing accidents and ensuring worker safety. However, traditional risk assessment methods predominantly rely on the expertise and intuition of safety managers, leading to variability in assessments due to differences in individual experience and judgment. Additionally, newly implemented safety regulations have increased the demand for structured and objective risk evaluation frameworks, further highlighting the limitations of conventional approaches. In response, this study proposes a Multi-Artificial Intelligence (AI) Agent-based System that integrates multimodal data by combining textual and visual inputs to systematically identify hazards, infer potential risk scenarios, assess severity and frequency, and propose mitigation strategies. The system employs multiple specialized AI agents to conduct hazard identification, risk assessment, and develop mitigation measures, thereby reducing dependency on human expertise while enhancing consistency and comprehensiveness. The results show that the AI agents performed comparably to safety managers with over 20 years of experience in risk identification and inference, surpassing them in the number of identified risk factors. However, variability was observed in the proposed mitigation strategies and the overall validity of risk assessments, indicating areas for further refinement. These findings suggest that AI-driven risk assessment systems can serve as valuable decision-support tools, particularly for less experienced safety managers, while complementing expert judgment in ensuring construction site safety. |