The　estimation　of　the　posterior　probability　distribution　of　unknown　parameters　remains　a　challenging　issue　for　model　updating　with　uncertainties.　Most　current　studies　are　based　on　stochastic　simulation　techniques.　This　paper　proposes　a　novel　variational　Bayesian　inference　approach　to　estimate　posterior　probability　distributions　by　using　the　vibration　responses　of　civil　engineering　structures.　An　adaptive　Gaussian　process　modeling　technique　is　used　to　represent　the　＂expensive-to-evaluate＂　likelihood　function,　and　the　unknown　posterior　probability　distribution　is　represented　using　a　Gaussian　mixture　model.　The　evidence　lower　bound　(ELBO)　and　its　gradients　can　be　computed　analytically　using　the　built　Gaussian　process　and　mixture　models.　The　unknown　parameters　in　the　Gaussian　mixture　model　can　be　identified　by　maximizing　the　value　of　ELBO.　The　stochastic　gradient　descent　method　is　applied　to　perform　the　optimization.　Numerical　studies　on　an　eight-story　shear-type　building　and　a　simply　supported　beam　are　conducted　to　verify　the　accuracy　and　efficiency　of　using　the　proposed　approach　for　probabilistic　model　updating　and　damage　identification.　Experimental　studies　on　a　laboratory　steel　frame　structure　are　also　conducted　to　validate　the　proposed　approach.　Results　demonstrate　that　the　posterior　probability　distributions　of　the　unknown　structural　parameters　can　be　successfully　identified,　and　reliable　probabilistic　model　updating　and　damage　identification　can　be　achieved.　(C)　2021　Elsevier　B.V.　All　rights　reserved.