Bridge　hangers,　such　as　those　in　suspension　and　cable-stayed　bridges,　suffer　from　cumulative　fatigue　damage　caused　by　dynamic　loads　(e.g.,　cyclic　traffic　and　wind　loads)　in　their　service　condition.　Thus,　the　identification　of　damage　to　hangers　is　important　in　preserving　the　service　life　of　the　bridge　structure.　This　study　develops　a　new　method　for　condition　assessment　of　bridge　hangers.　The　tension　force　of　the　bridge　and　the　damages　in　the　element　level　can　be　identified　using　the　Bayesian　optimization　method.　To　improve　the　number　of　observed　data,　the　additional　mass　method　is　combined　the　Bayesian　optimization　method.　Numerical　studies　are　presented　to　verify　the　accuracy　and　efficiency　of　the　proposed　method.　The　influence　of　different　acquisition　functions,　which　include　expected　improvement　(EI),　probability-of-improvement　(PI),　lower　confidence　bound　(LCB),　and　expected　improvement　per　second　(EIPC),　on　the　identification　of　damage　to　the　bridge　hanger　is　studied.　Results　show　that　the　errors　identified　by　the　EI　acquisition　function　are　smaller　than　those　identified　by　the　other　acquisition　functions.　The　identification　of　the　damage　to　the　bridge　hanger　with　various　types　of　boundary　conditions　and　different　levels　of　measurement　noise　are　also　studied.　Results　show　that　both　the　severity　of　the　damage　and　the　tension　force　can　be　identified　via　the　proposed　method,　thereby　verifying　the　robustness　of　the　proposed　method.　Compared　to　the　genetic　algorithm　(GA),　particle　swarm　optimization　(PSO),　and　nonlinear　least-square　method　(NLS),　the　Bayesian　optimization　(BO)　performs　best　in　identifying　the　structural　damage　and　tension　force.