논문 상세보기
pp.95-103
With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.
2. 균열 깊이 예측 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 Mock-up Test 계획
3.2 알고리즘 적용 결과
4. 결론
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