Evaluation of bridge decks with overlays using impact echo, a deep learning approach

In this paper, the feasibility of using deep learning models (DLMs) for evaluation of bridges with overlay systems is investigated. Several laboratory-made concrete specimens with artificial subsurface defects and overlay systems (bonded and debonded) made of cement and asphalt overlay materials were tested using impact echo (IE). One-dimensional (1D) and two-dimensional (2D) convolutional neural networks (CNNs) were developed, trained, and tested on the IE data. The proposed 1D CNN was the most successful in detecting debonding and subsurface defects; it achieved an average accuracy of 0.68 on the cement overlay specimens and 0.58 for asphalt overlay specimens. Maps of the defects and debonding were generated using the DLMs and were compared to the conventional method for analyzing the IE data. The 1D CNN produced the most accurate defect maps while successfully detected sound, debonded, and defected regions, particularly on the specimens with cement overlay.