Piers　are　usually　the　most　vulnerable　components　in　a　bridge　structure　and　generally　undergo　excessive　deformation,　which　will　lead　to　damage　and　even　whole　structural　collapse.　This　paper　investigates　the　probabilistic　seismic　deformation　capacities　of　reinforced　concrete　piers　under　different　limit　states　for　two　engineering　demand　parameters,　i.e.,　the　drift　ratio　and　displacement　ductility.　Based　on　sample　data　from　the　UW-PEER　database,　a　penalized　generalized　additive　model　is　used　for　predictor　variable　selections　and　to　determine　whether　the　mechanism　of　each　predictor　on　the　seismic　capacity　is　linear　or　nonlinear.　The　influence　of　a　predictor　that　illustrated　a　nonlinear　pattern　is　modeled　by　a　Gaussian　process,　and　Bayesian　semiparametric　regression　is　conducted　in　the　R　environment　to　obtain　posteriori　estimations　of　the　capacity　measures.　The　results　indicate　that　the　ratios　of　the　model　predictions　to　the　experimental　observations　are　all　around　1.0,　which　proves　the　unbiasedness　of　the　models.　Compared　with　previous　seismic　capacity　models,　the　prediction　of　seismic　capacity　measures　shows　higher　accuracy,　lower　dispersion,　and　better　portrayal　of　uncertainties.　The　proposed　model　based　on　Bayesian　semiparametric　regression　provides　a　performance　improvement　in　the　seismic　capacity　evaluation　of　the　bridge　structures,　which　can　be　used　for　the　subsequent　bridge　seismic　fragility　and　risk　assessment.