2026 年 7 巻 2 号 p. 1-19
This study presents a signal processing and machine learning approach to improve the detection of water path defects in post-tension prestressed concrete (PC) using phased array ultrasonic test (PAUT). In post-tension PC, inadequate grouting can create water paths inside the duct, enabling water pathways and increasing the risk of leakage and tendon corrosion. To address the difficulty of internal inspection, the ultrasonic signal was processed using parasitic discrete wavelet transform (P-DWT), incorporating real mother wavelets (RMWs) generated from finite element simulations. Two RMWs, representing healthy duct and duct with water paths, were used to enhance the characteristic signal patterns of each structural condition. Feature extraction was used to reduce data complexity and simplify it to be more informative. To evaluate the unlabeled data, semi-supervised learning associated with probability-based classification was then performed using a random forest model. The proposed approach demonstrated the method’s reliability in improving the interpretation of PAUT data for structural evaluation. The proposed methodology achieved 90% correct prediction between the two classes with approximately 90% probability, demonstrating improved interpretability compared to the unfiltered signal.
