Defect Detection of Bridge based on Impact-Echo Signals and Long Short-Term Memory

Byoung-Doo Oh, Hyung Choi, Young Jin Kim, Won Jong Chin, Yu-Seop Kim

http://doi.org/10.5626/JOK.2021.48.9.988

PSC box girder bridges comprise various elements, of which the tendon is the most important. The tendon is exceedingly vulnerable to defects such as corrosion. So, to protect it, it is inserted into the duct and then poured concrete. However, because the inner diameter of the duct is exceedingly narrow, there is a high probability that a cavity can occur with even a minor mistake. Thus, defects are identified through the professional interpretation of signals obtained using non-destructive testing. However, this requires high cost and much time. Additionally, it is difficult to accurately identify the internal state of the concrete structure. Thus, this study intends to apply Long Short-Term Memory. In this case, the Impact-Echo method, which is often used for concrete structures, is used as an input feature. And the characteristics of the structure (concrete thickness, depth of duct, distance between the hitting point and the measuring point) are used for learning. And, the frequency component of the IE signals is additionally used. As a result, the proposed model in this study can confirm approximately 88.56% accuracy.