The　service　quality　of　the　subbase　may　affect　the　overall　road　performance　during　its　service　life.　Thus,　monitoring　and　prediction　of　subbase　strain　development　are　of　great　importance　for　civil　engineers.　In　this　paper,　a　method　based　on　the　time-series　augmentation　was　employed　to　predict　the　subbase　strain　development.　The　time-series　generative　adversarial　network　(TimeGAN)　model　was　implemented　to　perform　the　augmentation　of　time-series　data　based　on　the　original　monitored　data.　The　augmented　data　was　trained　through　deep　learning　network　to　learn　the　feature　correlation　of　the　subbase　strain.　The　effectiveness　of　TimeGAN　on　the　prediction　accuracy　was　evaluated　through　the　Attention-Sequence　to　Sequence　(Attention-Seq2seq)　model,　and　temporal　convolution　network-adaptively　parametric　rectifier　linear　units　(TCN-APReLU)　model.　Results　indicated　that　the　TimeGAN　network　could　capture　sufficient　information　from　the　time-series　monitored　data　of　subbase　strain　development　so　that　the　corresponding　augmented　data　matches　well　with　the　original　data,　which　improves　the　prediction　accuracy.　It　is　also　discovered　that　the　combination　of　TimeGAN　and　TCN-APReLU　appropriately　predict　the　subbase　strain　development　based　on　the　original　monitored　data.