Intelligent　construction　has　become　an　inevitable　trend　in　the　development　of　the　construction　industry.　In　the　excavation　project,　using　machine　learning　methods　for　early　warning　can　improve　construction　efficiency　and　quality　and　reduce　the　chances　of　damage　in　the　excavation　process.　An　interpretable　gradient　boosting　based　ensemble　learning　framework　enhanced　by　the　African　Vultures　Optimization　Algorithm　(AVOA)　was　proposed　and　evaluated　in　estimating　the　diaphragm　wall　deflections　induced　by　excavation.　We　investigated　and　compared　the　performance　of　machine　learning　models　in　predicting　deflections　induced　by　excavation　based　on　a　database　generated　by　finite　element　simulations.　First,　we　exploratively　analyzed　these　data　to　discover　the　relationship　between　features.　We　used　several　state-of-the-art　intelligent　models　based　on　gradient　boosting　and　several　simple　models　for　model　selection.　The　hyperparameters　for　all　models　in　evaluation　are　optimized　using　AVOA,　and　then　the　optimized　models　are　assembled　into　a　unified　framework　for　fairness　assessment.　The　comprehensive　evaluation　results　show　that　the　AVOA-CatBoost　built　in　this　paper　performs　well　(RMSE　=　1.84,　MAE　=　1.18,　R2　=　0.9993)　and　cross-validation　(RMSE　=　2.65　±　1.54,　MAE　=　1.17　±　0.23,　R2　=　0.998　±　0.002).　In　the　end,　in　order　to　improve　the　transparency　and　usefulness　of　the　model,　we　constructed　an　interpretable　model　from　both　global　and　local　perspectives.　©　Higher　Education　Press　2024.