논문 상세보기
pp.17-24
With the increasing number of aging buildings, the importance of structural safety inspections has grown significantly. Traditional methods for inspecting welding defects, such as visual inspection and magnetic testing, rely heavily on human expertise, making them time-consuming, costly, and subjective. To address these limitations, thermographic technology has been introduced as a non-contact alternative, significantly reducing both time and cost. Furthermore, by incorporating AI, an objective and automated evaluation of welding defects can be achieved. In this study, we propose an AI-based thermographic approach for detecting welding defects. To validate the applicability of this method, a Mock-up Test was conducted. Specifically, 12 types of welding specimens with 4 welding part were prepared, generating a dataset of 6,500 thermographic images. Among 7 regression algorithms tested, RF and EXT were selected due to their superior performance. By ensemble learning these two models, we developed a robust welding defect measurement algorithm. To further verify its effectiveness, we applied the developed algorithm to 2 real projects, evaluating its applicability using 450 thermographic images. The results of this study demonstrate the feasibility of AI and thermographic technology in welding defect detection, highlighting its potential to enhance the efficiency and reliability of structural safety inspections in aging infrastructures.
1. 서론
2. 용접부 결함 평가 알고리즘
2.1 열화상 기술
2.2 실험체 계획 및 제작
2.3 열화상 데이터 구축 실험
2.4 변수 분석
2.5 머신러닝 모델 분석 결과
3. Mock-up Test
3.1 고정형 열화상 카메라 Mock-up Test
3.2 드론 탑재 열화상 카메라 Mock-up Test
4. 결론
감사의 글
References
