Cover Thickness Prediction for Steel Inside Concrete by Sub-Terahertz Wave Using Deep Learning

Deep learning techniques are increasingly being incorporated into the inspection and maintenance of social infrastructure. In this study, we show that when supervised deep learning was applied to imaging data obtained from sub-THz waves, the average recall exceeded 80% for all cover thicknesses of steel plate inside concrete and more than 90% for rebar inside concrete with a cover thickness of up to 20 mm. Unsupervised deep learning enabled the classification for both steel plate and rebar, even at large cover thicknesses. These results are expected to improve the exploration depth, which has been limited in previous studies.