This study presents a methodology for detecting delaminations in carbon fiber reinforced polymer (CFRP)-jacketed concrete structures by infrared thermography. Four specimens with artificial delaminations were evaluated through passive experiments under different weather conditions including winter, summer, sunny, and rainy conditions. The test parameters considered for the artificial delaminations in the specimens included size, depth, surface cover mortar, and the water content in the delamination void. The methodology detected delamination regions by boundary recognition based on the differences in surface temperature variations during a period. It could detect delaminations more efficiently and accurately than visual assessments based on thermal images. Furthermore, a few delaminations that were undetectable by thermal images were detected after image processing with the proposed methodology. In addition, the accuracy of the results was significantly affected by the time period for testing and the data-collection intervals. We discuss the recommended values obtained by parametric analysis and implement an application example using the proposed method and deep learning based on the experimental data.
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