Crack　development　is　increasingly　intensified　and　causes　pavement　damage　in　recent　decades　under　extreme　weather　events.　Although　various　auto-　or　semi-auto　crack　classification　algorithms　have　been　proposed,　most　of　them　require　manual　extraction　of　image　features,　which　is　considerably　labor-intensive,　compromising　classification　accuracy　and　efficiency.　Moreover,　collecting　original　images　for　model　training　is　difficult　due　to　various　limitations.　This　study　proposes　a　Generative　Adversarial　Networks　(GAN)-based　method　for　data　augmentation　of　the　collected　crack　digital　images　and　a　modified　deep　learning　network　(i.e.,　VGG)　for　crack　classification.　Firstly,　according　to　the　characteristics　of　collected　data,　a　GAN-based　image　generation　model　is　established　to　expand　the　training　dataset.　Then,　an　improved　VGG　model　is　built　based　on　the　most　efficient　model　via　comparisons　of　several　mainstream　feature　extraction　networks.　Finally,　comparison　studies　of　classification　performance　are　performed　for　different　classification　models　(i.e.,　the　improved　VGG　and　other　traditionally　used　ones)　and　datasets　(i.e.,　generated　by　GAN-based　and　traditional　methods).　The　model　trained　by　the　dataset　expanded　by　GAN　has　a　higher　accuracy　rate　and　lower　loss　values　than　traditional　methods.　The　improved　VGG　model　trained　by　the　validation　set　performs　similarly　to　the　training　set.　Compared　to　the　original　VGG　model,　the　accuracy　of　crack　prediction　of　the　improved　VGG　model　is　increased　by　5.9%　(i.e.,　96.30%),　and　the　F1-score　is　also　increased　by　5.78%　(i.e.,　96.23%).　Trained　by　the　same　test　set　expanded　by　GAN,　the　improved　VGG　model　has　a　higher　recall　and　F1-score　than　GoogLeNet,　ResNet18,　and　AlexNet.　The　novel　integrated　GAN　and　modified　VGG　model　shows　satisfactory　efficiency　for　classifying　pavement　cracks.