2025 Volume 6 Issue 3 Pages 1148-1158
Recent research has explored the automation of hammering-based nondestructive testing for reinforced concrete structures using machine learning techniques. However, many of these studies have not been directly validated against internal defects in actual structures.
In this study, hammering sound data were collected from large-scale specimens in which corrosion- induced cracks were reproduced through electrolytic degradation. Unsupervised anomaly detection using three types of autoencoders was applied, and the results were compared with expert judgments and core sampling.
The findings suggest that applying continuous wavelet transform to hammering sound data enables the identification of critical deep defects that may be overlooked by experienced inspectors. Furthermore, the proposed method achieved highly accurate anomaly detection without extensive hyperparameter tuning or expert labeling, indicating its potential to improve the efficiency of nondestructive inspection.
