This　study　investigates　the　application　of　ordinal　regression　models　in　seismic　fragility　curve　modeling,　providing　a　flexible　alternative　to　the　traditional　log-normal　distribution　function.　A　comparative　analysis　is　conducted　among　various　ordinal　regression　approaches,　including　the　traditional　Cumulative　model　as　well　as　alternative　methods　like　Sequential　and　Adjacent　Category　models,　along　with　extensions　that　account　for　category-specific　effects　and　heteroscedasticity.　These　models　are　applied　to　bridge　damage　data　from　the　2008　Wenchuan　earthquake,　using　both　frequentist　and　Bayesian　inference　methods.　Model　diagnostics,　including　surrogate　residuals,　are　performed　to　assess　model　fit　and　performance.　A　total　of　eleven　models　are　examined,　from　basic　forms　to　those　incorporating　category-specific　effects　and　variance　heterogeneity.　The　Sequential　model　with　category-specific　effects,　rigorously　evaluated　using　leave-one-out　cross-validation,　outperforms　the　traditional　Cumulative　probit　model.　The　findings　highlight　significant　differences　in　the　predicted　damage　probabilities,　emphasizing　the　potential　of　more　flexible　fragility　curve　modeling　techniques　to　improve　seismic　risk　assessments.　This　study　underscores　the　importance　of　ongoing　evaluation　and　refinement　of　modeling　techniques　to　enhance　the　predictive　accuracy　and　applicability　of　seismic　fragility　models　in　performance-based　earthquake　engineering.