Disc　cutters　serve　as　the　primary　device　in　shield　machines　for　rock　breaking　during　tunnel　construction.　Assessing　the　wear　state　of　disc　cutters　is　crucial　for　making　timely　replacement　decisions.　Several　researchers　have　successfully　predicted　disc　cutter　wear　with　acceptable　accuracy　using　Machine　Learning　(ML).　However,　ML　models　are　often　project-specific.　The　model　trained　on　one　project　cannot　be　applied　to　the　other　project　if　the　two　projects　have　significant　deviations,　resulting　in　a　waste　of　resources　and　effort.　To　address　this　issue,　we　propose　a　domain-adversarial-based　transfer　learning　method　to　improve　the　generalization　performance　of　ML　models.　In　particular,　we　integrate　the　Domain-Adversarial　Neural　Network　(DANN)　with　the　Transformer.　The　proposed　model　makes　domain　discrimination　and　regression　prediction　for　input　parameters.　The　domain-adversarial　mechanism　makes　the　extracted　features　from　input　parameters　share　many　commonalities　and　confuses　data　from　different　domains,　which　can　improve　the　generalization　performance　of　the　model.　The　hyperparameter　λ　of　the　proposed　model　is　used　to　balance　the　importance　of　domain　discrimination　and　regression　prediction.　We　validate　the　effectiveness　of　the　proposed　method　in　the　second　subsea　tunnel　in　Qingdao,　China.　The　south　and　service　tunnels　of　the　project　are　in　similar　strata　conditions　but　have　a　significant　difference　in　tunnel　diameter.　They　share　many　commonalities　in　the　wear　characteristics　of　disc　cutters.　We　set　the　service　tunnel　as　the　source　domain　and　the　south　tunnel　as　the　target　domain.　The　model　is　trained　and　tested　in　the　service　tunnel,　learning　wear　characteristics　under　different　strata.　Then,　the　pre-trained　model　is　transferred　to　the　south　tunnel　and　fine-tuned　with　limited　data　to　adapt　to　wear　characteristics　under　different　shields.　Finally,　the　fine-tuned　model　is　used　to　predict　the　wear　values　of　the　target　data　in　the　south　tunnel.　The　domain-adversarial-based　Transformer　model　outperforms　FCN,　LSTM　and　Transformer　without　the　domain-adversarial　mechanism　and　requires　even　limited　target　project　data,　unlike　traditional　models,　which　require　extra　training　data.　The　proposed　method　can　be　applied　in　the　early　stage　of　related　projects　when　data　are　scarce.　©　2025　Elsevier　Ltd