The　adding　of　industrial　wastes,　including　blast　furnace　slag　and　fly　ash,　to　concrete　materials　will　not　only　improve　the　working　performance,　but　also　significantly　reduce　the　carbon　emissions　and　promote　the　green　development　in　civil　engineering　area.　The　traditional　material　designs　are　mainly　indoor　laboratory-based,　which　is　complex　and　time-consuming.　In　this　study,　a　virtual　material　design　method,　including　deep　data　augmentation　methods　and　deep　learning　methods,　was　employed　to　predict　the　compressive　strength　of　concrete　with　industrial　wastes.　Three　types　of　Generative　Adversarial　Networks　(GANs)　were　employed　to　augment　the　original　data　and　the　results　were　evaluated.　The　test　was　conducted　based　on　a　small　experiment　dataset　from　previous　literature,　comparing　with　traditional　machine　learning　methods.　Test　results　show　that　the　deep　learning　methods　have　the　highest　accuracy　in　compressive　strength　prediction,　increasing　from　0.90　to　0.98　(Visual　Geometry　Group,　VGG)　and　from　0.83　to　0.96　(One-Dimensional　Convolutional　Neural　Network,　1D　CNN)　after　deep　data　augmentation,　where　the　prediction　accuracy　of　Random　Forest　(RF)　and　Support　Vector　Regressive　(SVR)　in　traditional　machine　learning　algorithms　increase　from　0.91　to　0.96　and　from　0.78　to　0.86,　respectively.　In　addition,　a　lightweight　deep　convolutional　neural　network　was　designed　based　on　the　augmented　dataset.　The　results　show　that　the　lightweight　model　can　improve　the　computation　efficiency,　reduce　the　complexity　of　the　model　compared　with　the　original　model,　and　reach　a　great　prediction　accuracy.　The　proposed　study　can　facilitate　the　concrete　material　design　with　industrial　wastes　with　less　labor　and　time　cost　compared　with　traditional　ones,　thus　can　provide　a　cleaner　solution　for　the　whole　industry.　©　2022　Elsevier　Ltd