DETECTION OF A DEBONDING PART USING DEEP LEARNING AND LASER ULTRASONIC VISUALIZATION TESTING

Abstract

In this research, a non-destructive inspection method for a debonding part of CFRP-concrete structures is investigated by using the laser ultrasonic visualizaiton testing (LUVT) and a deep learning. In general, time-stepping images (or movies) of ultrasonic wave propagation in the laser radiating surface can be obtained by using LUVT. At that time, LUVT inspectors have to give an evaluation on with and without defect by visual judgement from such LUVT time-stepping images. However, considering the common concern about the shortage of non-destructive inspectors in near future and the complex ultrasonic wave elds from the anisotropic property of CFRPs, AI alternation for this visual judgement of inspectors might decrease loads of themselves. Therefore, in this research, some CFRP-concrete structure specimens are prepared, LUVT inspections for them are carried out, and some images for them are obtained. Then, a deep learning is implemented in order to construct an AI for a debonding part of CFRP-concrete structures. As learning and testing results, it is concluded that a debonding part can automatically be detected by LUVT if the CFRP thinckness is thin.