Abstract

Previously, inspections of concrete bridge decks (CBDs) using infrared thermography under natural conditions from the soffit face (SIRTNC) need to be conducted during optimal time windows so that deteriorations can be both detected and classified. However, it is still impossible within other durations in a day (blind time windows) which motivates the present study to address this problem through a processing method, i.e., pixel-by-pixel statistical analysis but focusing on the data dispersion, unlike past works. Consequently, defects in both the top (deck delaminations) and bottom (soffit delaminations) of CBDs became detectable during blind time windows. Meanwhile, the detectability of deteriorations was also enhanced significantly if a long data collection length was considered. Moreover, the detected delaminations were precisely indicated as deck or soffit defects in CBDs. In addition, the effectiveness of the analysis was proven in essential aspects, including the variation in environment, data collection length, and interval.

Full Text
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