In　order　to　avoid　sampling　being　immersed　in　low-probability　areas　and　to　raise　sampling　efficiency,　the　population　Monte　Carlo　(PMC)　sampling　algorithm　was　improved　and　then　combined　with　the　approximate　Bayesian　calculation　(ABC)　and　stochastic　response　surface　(SRS)　to　propose　a　probabilistic　damage　identification　method.　Firstly,　PMC　algorithm　was　embedded　in　ABC,　and　sample　variance　in　each　iteration　step　was　used　to　perturb　a　particle　swarm,　and　obtain　adaptive　weight　coefficients.　An　error　function　was　constructed　to　measure　the　similarity　between　simulated　and　measured　samples,　and　replace　the　likelihood　function.　Then　the　explicit　expression　for　structural　stochastic　response　was　established　using　SRS　to　greatly　improve　the　calculation　efficiency　of　response　statistical　features.　Finally,　obtained　statistical　values　of　parametric　posterior　probability　distribution　were　taken　as　damage　indexes.　According　to　indexes＇　changes　before　and　after　damage,　damage　locations　and　degrees　were　judged.　Damages　of　a　test　reinforced　concrete　beam　under　a　single　working　condition　and　multiple　working　conditions　were　identified,　respectively.　It　was　shown　that　the　proposed　method　can　be　used　to　effectively　improve　the　calculation　efficiency　of　Bayesian　inference　process　under　the　condition　of　ensuring　parametric　posterior　distribution＇s　estimation　accuracy.　©　2020,　Editorial　Office　of　Journal　of　Vibration　and　Shock.　All　right　reserved.