Earthquake-induced　bridge　damage　can　disrupt　transportation　networks,　potentially　hindering　emergency　response　and　post-disaster　recovery　efforts,　and　posing　public　safety　risks　in　affected　areas.　Rapid　and　accurate　assessment　of　post-earthquake　resilience　of　bridge　networks　is　crucial　for　evaluating　urban　seismic　performance.　Traditional　resilience　assessment　methods,　constrained　by　complex　traffic　distribution　processes,　struggle　to　quickly　evaluate　the　traffic　performance　of　bridge　networks　during　the　post-earthquake　recovery　period.　This　paper　presents　a　two-layer　stacking　ensemble　model　for　predicting　the　functionality　and　resilience　of　bridge　networks.　The　first　layer　integrates　advantages　of　four　base　learners,　including　random　forest　(RF),　artificial　neural　network　(ANN),　convolutional　neural　network　(CNN),　and　extreme　gradient　boosting　(XGBoost).　The　second　layer　completes　regression　of　functionality　based　on　a　support　vector　machine　(SVM).　Bayesian　optimization　and　5-fold　cross-validation　are　employed　for　hyperparameter　tuning　of　the　ensemble　model.　Finally,　the　proposed　model　is　validated　using　the　Sioux-Falls　bridge　network.　Results　demonstrate　that　the　developed　model　provides　rapid　predictions　of　post-earthquake　network　functionality　and　resilience.　Additionally,　this　model　can　guide　post-earthquake　repair　decisions　and　assist　in　optimizing　the　allocation　of　regional　repair　resources.　©　2024