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%.