Machine learning-based detection of transportation infrastructure internal defects using ground-penetrating radar: a state-of-the-art review

Abstract

Early detection of internal defects is crucial to ensure the long-term performance and stability of transportation infrastructures. Researchers and practitioners have developed and various non-destructive testing (NDT) methods for this purpose. Among them, the ground-penetrating radar (GPR) technique has been widely implemented due to its advantages such as large coverage, traffic-speed survey, and rich subsurface information. In addition, machine learning (ML) algorithms have been frequently applied to achieve automatic GPR data interpretations, which is essential for field applications. However, the fundamental concepts, architectures, and appropriate application scenarios of these algorithms are often questionable to practitioners and researchers. This paper presents a state-of-the-art review on ML applications in internal defect detection of transportation infrastructure using GPR. In particular, pavements and bridges were covered. The basic knowledge of GPR working principles and ML algorithms were documented. The critical features of the ML algorithms for each detection task were presented. The drawbacks that may hinder the application of ML algorithms using GPR were indicated, including the insufficiency of labelled GPR data, unavailability of GPR dataset, customized ML architecture, and field validation. Finally, possible transfer learning, integrated robotic platform, and data fusion with other NDT methods were discussed. This review paper is expected to serve as a reference for practitioners to choose appropriate ML algorithms to detect internal defects in transportation infrastructures using GPR.

Applying Machine Learning to Construct a Printed Circuit Board Gold Finger Defect Detection System

“…(4) Transportation Infrastructure Defect Detection: Sui et al [6] conducted a state-of-theart review of ML applications in transportation infrastructure defect detection, using ground penetrating radar (GPR) in particular. (5) Visual Defect Detection using CNN: Jha et al's [7] survey of current approaches in visual defect detection focused on CNNs and pixel-level segmentation techniques.…”