Deep learning models for bridge deck evaluation using impact echo

Abstract

Impact echo (IE) is a common nondestructive evaluation (NDE) method to detect subsurface defects in concrete bridge decks. The conventional approach for analyzing the IE data requires user expertise to define analysis parameters that could hinder broad field implementation. In this paper, the feasibility of using deep learning models (DLMs) for autonomous subsurface defect detection in bridge decks using IE has been investigated. A set of eight laboratory-made reinforced concrete bridge specimens with artificial defects were constructed at the Federal Highway Administration Advanced Sensing Technology NDE laboratory. A total number of 2016 of IE data was collected from these specimens. A one-dimensional (1D) convolutional neural network (CNN), and a 1D recurrent neural network using bidirectional long-short term memory units, were developed and applied on the IE data. In addition, two-dimensional (2D) world renowned CNNs were applied on the 2D representatives of the IE data, i.e., spectrograms. The proposed 1D CNN was the most accurate model achieving an overall accuracy of 0.88 by classifying 0.70 of the defects and 0.95 of the sound regions correctly. The proposed 1DCNN was superior to previous machine learning models that were previously used for IE classification. The results of this study showed the feasibility and the potentials of the DLMs for subsurface defect detection.