It is important to distinguish between two common defects, fatigue cracks and reflective cracks, and determine their locations (the top and bottom) in the highway pavement because they require individually targeted treatment measures. Ground Penetrating Radar (GPR) has the potential to detect cracks in the highway pavement due to the change of the electromagnetic properties of highway-pavement media, arising from the existences of cracks. By using a theoretical analysis and a numerical simulation, we compare the characteristics of corresponding radargrams, including the amplitude variation of diffracted waves with various models of presetting cracks inside the layered homogeneous media. For those fatigue cracks and reflective cracks extending to the road surface, the amplitude curves of direct ground wave can intuitively indicate the locations of the top of the cracks and qualitatively compare the width of these cracks. Furthermore, we find that the shape and pattern of diffraction hyperbolas of both types of cracks with bottoms at different locations are quite similar, but their amplitudes are significantly different. To be specific, for those cracks with the same width, the amplitude of diffracted waves generated by fatigue cracks is slightly higher than that generated by reflective cracks at the interface between the asphalt surface and the semi-rigid base layer. In contrast, the amplitude of the former is significantly lower than the latter at the interface between the semi-rigid base and the roadbed. We applied these findings to the interpretation of the field GPR data of a highway pavement in China, and successfully identified the locations of the cracks and corresponding types. Our model results and field results clearly show the efficiency of our findings in the detection of cracks for highway-pavement rehabilitation.

Deep learning has significantly improved the recognition efficiency and accuracy of ground-penetrating radar (GPR) images. A significant number of weight parameters need to be specified, which requires lots of labeled GPR images. However, obtaining the ground-truth subsurface distress labels is challenging as they are invisible. Data augmentation is a predominant method to expand the dataset. The traditional data augmentation methods, such as rotating, scaling, cropping, and flipping, would change the GPR signals’ real features and cause the model’s poor generalization ability. We proposed three GPR data augmentation methods (gain compensation, station spacing, and radar signal mapping) to overcome these challenges by incorporating domain knowledge. Then, the most state-of-the-art model YOLOv7 was applied to verify the effectiveness of these data augmentation methods. The results showed that the proposed data augmentation methods decrease loss function values when the training epochs grow. The performance of the deep learning model gradually became stable when the original datasets were augmented two times, four times, and eight times, proving that the augmented datasets can increase the robustness of the training model. The proposed data augmentation methods can be used to expand the datasets when the labeled training GPR images are insufficient.

Cracks are a common form of road distress that can significantly impact pavement integrity. Accurate detection of the attribute characteristics of cracks, including the type, location (top and bottom), width, and orientation, is crucial for effective repair and treatment. This study combines numerical simulations with filed data to investigate how the amplitudes of ground-penetrating radar (GPR) early-time signals (ETSs) vary with changes in the crack top and width, as well as how variations in the crack bottom impact radar reflected wave amplitude. The results show that when GPR ETSs are mixed with diffracted waves from the crack top, the amplitude change percentage of the ETS at the crack top exhibits a pronounced ‘∨’-shaped dip, which provides a clearer indication of the crack top. Furthermore, a positive correlation exists between crack width and the amplitude change percentage, offering a theoretical basis for quantitatively estimating crack width. On the reflected wave originating from the interface between the semi-rigid base and the subgrade, a pronounced ‘∧’-shaped dip is observed in the trough amplitude change percentage of the reflected wave at the crack bottom. For cracks of the same width, the amplitude of the ‘∧’ vertex from reflective cracks is approximately three times greater than that from fatigue cracks. This discrepancy helps identify the crack bottom and quantitatively diagnose their types. The line connecting the vertices of the ‘∨’ and ‘∧’ shapes indicate the crack’s orientation. Accurate diagnosis of crack properties can guide precise, minimally invasive treatment methods, effectively repairing road cracks and extending the road’s service life.