Exploring Non-Destructive Testing in Reinforced Concrete Structures: An In-Depth Review

Abstract

This review article examines nine Non-Destructive Testing (NDT) methods that have been selected for their effectiveness in detecting critical defects in reinforced concrete (RC) structures. It discusses the existing gaps, future technological possibilities, and challenges associated with the implementation of NDT in RC structures. This scoping review is limited to the application of NDT to RC structures. The article emphasizes the strategic integration of NDT methods while taking into account factors such as the age of the concrete, pre-stressing, geometry, and accessibility. It also highlights recent developments in NDT, identifying gaps and potential future technologies, as well as challenges in applying NDT, while comparing AI-enhanced NDT techniques to traditional methods. General guidelines for selecting the appropriate NDT methods are provided, along with key factors to consider when choosing NDT techniques. The limitations of current NDT methods are outlined, including restricted penetration depth, signal distortion, inadequate 3D imaging, and the lack of universal testing standards. Additionally, the review suggests that the integration of artificial intelligence and machine learning technologies can enhance accuracy and enable real-time monitoring. To ensure effective predictive maintenance and reliability, the article recommends standardizing NDT protocols, investing in training, and using interpretable AI with Internet of Things integration.