The　quantification　of　uncertainties　in　civil　structures　poses　a　significant　challenge　due　to　the　need　to　update　a　large　number　of　unknown　parameters,　which　can　significantly　increase　the　computational　workload.　A　potential　solution　to　this　problem　is　the　use　of　Approximate　Bayesian　Computation　(ABC)　approaches,　which　have　gained　attention　in　recent　years.　However,　most　existing　ABC　methods　are　limited　in　their　ability　to　handle　a　large　number　of　unknowns.　To　address　this　limitation,　this　study　proposes　a　decentralized　approach　for　uncertainty　quantification　in　civil　structures.　In　this　approach,　the　structure　is　divided　into　several　parts,　and　the　uncertainties　in　each　part　are　estimated　independently.　The　proposed　method　utilizes　the　wavelet　domain　technique　to　eliminate　the　interfacial　force　of　the　target　part　and　employs　a　recently　developed　variational　inference　approach　to　quantify　the　uncertainty　in　the　target　substructure.　A　three-span　beam,　a　2D　truss,　and　an　eight-story　building　were　tested　to　evaluate　the　efficiency　and　feasibility　of　this　approach.　The　results　show　that　uncertainties　in　large　structures　can　be　estimated　and　that　the　proposed　method　is　robust　to　high　levels　of　measurement　noise.