In　recent　years,　the　surrogate　modeling　technique　has　been　increasingly　employed　for　the　failure　probability　estimation　of　civil　structures.　The　surrogate　modeling　technique　acts　as　a　black　box　that　predicts　the　performance　of　expensive-to-evaluate　problems.　However,　the　uncertainty　of　failure　probability　estimation　due　to　the　discrepancy　between　the　surrogate　model　and　physical　model　has　not　been　well　studied.　This　study　introduces　a　new　surrogate　modeling　technique　for　failure　probability　estimation　of　civil　structures　that　utilizes　a　Bayesian　neural　network.　Specifically,　a　fully　connected　neural　network　is　constructed　to　approximate　the　limit　state　function.　The　hyperparameters　in　the　neural　network　are　obtained　within　the　Bayesian　paradigm,　and　the　probability　distributions　are　estimated　through　Laplace　approximation.　When　the　Bayesian　neural　network　method　is　used　for　reliability　analysis,　the　failure　curve　and　the　corresponding　uncertainty　can　be　estimated.　Numerical　studies　on　2d　truss,　a　simple　support　beam　under　moving　load,　and　dynamic　analysis　of　subway　station　considering　the　soil-structure　interaction　are　conducted　to　validate　the　efficiency　of　the　approach.　Results　with　Monte　Carlo　simulation　and　subset　simulation　are　also　presented.　The　results　demonstrate　the　proposed　method＇s　potential　for　improving　prediction　accuracy　by　factoring　in　the　uncertainty　embedded　in　the　surrogate　model.　Additionally,　it　exhibits　higher　levels　of　efficiency　than　traditional　methods.