A　probabilistic　fatigue　life　prediction　model　for　RC　beams　under　chloride　environment　is　proposed,　and　the　statistical　uncertainty　is　considered　by　Bayesian　inference　to　determine　and　update　model　parameters.　In　terms　of　the　sparse　fatigue　data,　the　Markov-chain　Monte-Carlo　(MCMC)　method　is　utilized　to　conduct　the　Bayesian　updating.　The　prior　distribution　and　posterior　distributions　are　respectively　determined　by　the　data　in　this　study　and　open　references.　Results　show　that　the　fatigue　life　under　chloride　environment　is　accurately　predicted　by　a　probabilistic　S-N　curve,　in　which　as　update　points　increase,　predictions　get　close　to　tests　and　the　statistical　uncertainty　is　reduced.　©　2023