Stochastic　model　updating　must　be　considered　for　quantifying　uncertainties　inherently　existing　in　real-world　engineering　structures.　By　this　means　the　statistical　properties,　instead　of　deterministic　values,　of　structural　parameters　can　be　sought　indicating　the　parameter　variability.　However,　the　implementation　of　stochastic　model　updating　is　much　more　complicated　than　that　of　deterministic　methods　particularly　in　the　aspects　of　theoretical　complexity　and　low　computational　efficiency.　This　study　attempts　to　propose　a　simple　and　cost-efficient　method　by　decomposing　a　stochastic　updating　process　into　a　series　of　deterministic　ones　with　the　aid　of　response　surface　models　and　Monte　Carlo　simulation.　The　response　surface　models　are　used　as　surrogates　for　original　FE　models　in　the　interest　of　programming　simplification,　fast　response　computation　and　easy　inverse　optimization.　Monte　Carlo　simulation　is　adopted　for　generating　samples　from　the　assumed　or　measured　probability　distributions　of　responses.　Each　sample　corresponds　to　an　individual　deterministic　inverse　process　predicting　the　deterministic　values　of　parameters.　Then　the　parameter　means　and　variances　can　be　statistically　estimated　based　on　all　the　parameter　predictions　by　running　all　the　samples.　Meanwhile,　the　analysis　of　variance　approach　is　employed　for　the　evaluation　of　parameter　variability　significance.　The　proposed　method　has　been　demonstrated　firstly　on　a　numerical　beam　and　then　a　set　of　nominally　identical　steel　plates　tested　in　the　laboratory.　It　is　found　that　compared　with　the　existing　stochastic　model　updating　methods,　the　proposed　method　presents　similar　accuracy　while　its　primary　merits　consist　in　its　simple　implementation　and　cost　efficiency　in　response　computation　and　inverse　optimization.　©　2012　Elsevier　Ltd.