Advances in automatic identification of road subsurface distress using ground penetrating radar: State of the art and future trends

Affected by soil erosion and material deterioration, road subsurface is prone to distress such as cavities, water-rich, and cracks. Ground penetrating radar (GPR), as a real-time geophysical survey method that uses electromagnetic radiation to image the subsurface, offers promising non-destructive solutions to road subsurface health monitoring. However, the interpretation of GPR signals is non-intuitive and obscure in terms of distress identification, whose performance is also limited by the heterogeneous road condition. In conjunction with knowledge diagram analysis, a state-of-the-art review is applied to summarize the advances in the automatic identification of road subsurface distress (RSD). The algorithms based on the single-channel waveform (A-scan), two-dimensional profile (B-scan), and three-dimensional data (C-scan) are elaborated from the perspectives of rule-based recognition algorithm, machine learning algorithm, and deep learning algorithm. In comparison to analytical methods, the emerging deep learning models have a powerful ability to extract complex features from multi-dimensional GPR radargrams, enhancing the efficiency and accuracy of road subsurface distress detection. Recommendations for model selection are compiled from existing literature together with empirical evidence. The most significant variables that influence the model selections are thought to be the type of identified RSD, training sample quality and quantity, prior knowledge, and computational cost. Some challenges, such as insufficient training samples and diverse road structures, are presented. Future trends are concluded to draw the implications for GPR research.