Automatic detection of subsurface defects of concrete slabs by domain adaptation algorithm

Locating and imaging internal defects in concrete is a challenging task. The traditional impact-echo (IE) method offers only qualitative analysis of defects and requires operation by skilled technicians, often leading to misjudgments and significant consumption of resources. To address these limitations, this study develops an automatic rapid detection system for concrete subsurface defects based on a deep learning model. The system is embedded with the unsupervised domain adaptation (DA) network in which the time-frequency images of IE signals are input, producing a two-dimensional (2D) defect contour map based on the damage probability value. Three types of subsurface defects including delamination, voids and pipes are expected to be detected in the concrete slabs. The system can effectively acquire and process IE signals on the concrete slab along a preset path, and the recognition accuracy, precision, recall, and f1-score reached 98.1 %, 92 %, 79.2 %, and 85.1 %, respectively. This study also establishes a semi-analytical formula linking natural frequency ( f I ), area-to-depth squared ratio ( A / h² ), and concrete property variables to estimate the defect depth. The mean square error (MSE) between the estimated value and actual value of the defect depth is only 3.64×10 −4 . Overall, the proposed detection system provides quantitative evaluation of concrete subsurface defects with excellent accuracy and efficiency, aiding in the fast repair and maintenance strategies of concrete infrastructure.