Abstract
The safety monitoring of transportation infrastructure foundation is crucial for the sustainable service of transportation systems. In recent years, the Ground Penetrating Radar (GPR) has become a powerful tool to identify and locate the subgrade distresses according to the different responses of wave characteristics, preliminarily realizing an intelligent nondestructive detection. To solve the problems like small sample size and unbalanced dataset, this study used a deep data augmentation method, e.g. WGAN-GP network, to augment the original limited B-Scan GPR data of subgrade, and then carried out supervised learning for classification task. The detailed computation steps include the image processing, data augmentation and intelligent analysis. First, the dataset was initially enlarged through the traditional methods after noise filtering, gamma transform and other processing methods. Then, the WGAN-GP network was adopted to generate new high-quality B-Scan images. Finally, the intelligent classification of subgrade distresses was realized by ResNet50 model with a satisfactory accuracy of 90.85%.
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