Deep learning–based nondestructive evaluation of reinforcement bars using ground‐penetrating radar and electromagnetic induction data

Abstract

This paper proposes a nondestructive evaluation method based on deep learning using combined ground‐penetrating radar (GPR) and electromagnetic induction (EMI) data for autonomic and accurate estimation of the cover thickness and diameter of reinforcement bars. A real‐time object detection algorithm—You Only Look Once–version 3 (YOLO v3)—is adopted to automatically identify the reinforcement bar reflected signals from radargrams, with which the range of the cover thickness is roughly predicted. Subsequently, EMI data, accompanied with the cover thickness range, are imported to a one‐dimensional convolutional neural network (1D CNN), pretrained by calibrated EMI and GPR data, to simultaneously estimate the cover thickness and reinforcement bar diameter. Testing with the on‐site GPR data shows that YOLO v3 is superior to Single Shot Multibox Detector method in GPR hyperbolic signal identification. Testing of 1D CNN with the EMI and GPR data collected in an in‐house sand pit experiment shows that the estimation accuracy of the cover thickness and reinforcement bar diameter is, respectively, 96.8% and 90.3% with a permissible error of 1 mm. Further, an experiment with concrete specimens demonstrates that among the 22 estimated values (including the reinforcement bar diameter and cover thickness), there are 17 values accurately estimated, while the inaccurately estimated values have an error up to 2 mm. The experimental results show that the proposed method can autonomically evaluate the reinforcement bar diameter and cover thickness with a high accuracy.

Pre-demolition concrete waste stream identification: Classification framework

“…Using data from both ground-penetrating radar (GPR) and electromagnetic induction (EMI), [130] Non-destructive detection of steel reinforcement corrosion in concrete structures has been achieved using techniques such as ground penetrating radar [131]. Current inspection practices require a large amount of time for inspection and can pose a safety risk to inspectors.…”

Multiattribute multitask transformer framework for vision‐based structural health monitoring

“…In recent years, there is an increasing trend of using machine learning (ML) in structural health monitoring (SHM) (Lin et al., 2022; Rafiei & Adeli, 2017b, 2018; Soleimani‐Babakamali et al., 2022). In particular, applying deep learning (DL) in vision/image‐based SHM (Chun et al., 2022; Jang et al., 2019; Li et al., 2022; Pan & Yang, 2022; Zhang & Lin, 2022; Zhao et al., 2022) indicates a significant performance improvement over traditional computer vision (CV) methods, for example, edge detection based on extracted vision features (Cha et al., 2017). However, many studies (Cha et al., 2017; Dorafshan et al., 2018; Xu et al., 2018) are mainly concerned with the existence of structural damage in the images and directly treat the problem as a single‐attribute classification where each image only has one label, for example, damaged or undamaged.…”

Review of artificial intelligence-based bridge damage detection

“…Single sensing data may be limited by the sensing technology and the fusion of multiple data can further improve the identification of damage. Li et al 119 used YOLO v3 to identify the reflected signals of the reinforcement in the scanned images and to obtain a rough thickness range of the overburden. The obtained thickness range was then fed into a 1D convolutional neural network along with the (Electromechanical impedance) EMI data to estimate the overburden thickness and reinforcement diameter.…”