Imaging techniques for defect detection of fiber reinforced polymer-bonded civil infrastructures

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Original languageEnglish
Article numbere2555
Journal / PublicationStructural Control and Health Monitoring
Issue number8
Online published8 Apr 2020
Publication statusPublished - Aug 2020


The present article provides a state-of-the-art review of imaging techniques used for defect detection of fiber reinforced polymer (FRP)-bonded civil engineering structures. Compared to the conventional techniques by equipping a structure with stress wave sensors, the contactless imaging techniques feature efficient instrumentation, convenient data acquisition, and rapid evaluation in the procedures of nondestructive testing (NDT). Recently, a substantial progress in utilizing electromagnetic waves for development of imaging NDT techniques (e.g., synthetic aperture radar, infrared thermography, laser shearography, and laser reflection technique) with the purpose to identify debonding or delamination of FRP-bonded structural systems has been made. As another electromagnetic wave-based imaging technique, X-ray computed tomography is promising for exploration of the damage evolution in this structural system, despite little application in structural health monitoring of real infrastructures. Apart from these imaging techniques, more recently, there have been computer-aided motion magnification techniques for defect identification. The motion magnification technique only requires a digital camera and a computer with vision algorithm, which can amplify the motion of defect region and render it visible in a video. This advanced imaging technique achieves high-resolution measurement, simultaneous full-field inspection, and straightforward defect identification in the scene. In this review, both Eulerian motion magnification and phase-based motion magnification techniques for structural visual inspection are presented and discussed. Furthermore, the present article recommends the combination of imaging NDT techniques with artificial intelligence approaches (e.g., deep learning algorithms) to realize the automated and efficient defect detection towards FRP-bonded civil infrastructures.

Research Area(s)

  • civil infrastructure, defect identification, fiber reinforced polymer, health diagnostics, imaging technique, nondestructive testing