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Title |
Concrete Crack Detection Inside Finishing Materials Using Lock-in Thermography
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Authors |
이명훈(Myung-Hun Lee) ; 우욱용(Ukyong Woo) ; 최하진(Hajin Choi) ; 김종찬(Jong-Chan Kim) |
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DOI |
https://doi.org/10.11112/jksmi.2023.27.6.30 |
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Keywords |
콘크리트 균열; 열화상 카메라; 마감재; 비파괴; 비접촉 Concrete crack; Thermal imaging camera; Finishing material; Non-destructive; Non-contact |
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Abstract |
As the number of old buildings subject to safety inspection increases, the burden on designated institutions and management entities that are responsible for safety management is increasing. Accordingly, when selecting buildings subject to safety inspection, appropriate safety inspection standards and appropriate technology are essential. The current safety inspection standards for old buildings give low scores when it is difficult to confirm damage such as cracks in structural members due to finishing materials. This causes the evaluation results to be underestimated regardless of the actual safety status of the structure, resulting in an increase in the number of aging buildings subject to safety inspection. Accordingly, this study proposed a thermal imaging technique, a non-destructive and non-contact inspection, to detect cracks inside finishing materials. A concrete specimen was produced to observe cracks inside the finishing material using a thermal imaging camera, and thermal image data was measured by exciting a heat source on the concrete surface and cracked area. As a result of the measurement, it was confirmed that it was possible to observe cracks inside the finishing material with a width of 0.3mm, 0.5mm, and 0.7mm, but it was difficult to determine the cracks due to uneven temperature distribution due to surface peeling and peeling of the wallpaper. Accordingly, as a result of performing data analysis by deriving the amplitude and phase difference of the thermal image data, clear crack measurement was possible for 0.5mm and 0.7mm cracks. Based on this study, we hope to increase the efficiency of field application and analysis through the development of technology using big data-based deep learning in the diagnosis of internal crack damage in finishing materials.
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