Reliability　updating　of　structures　is　computationally　demanding,　especially　when　the　likelihood　function　involves　inverse　analysis　and　needs　to　be　updated　multiple　times.　In　this　study,　a　novel　efficient　approach　for　updating　the　failure　probability　based　on　the　method　of　moments　and　information　reuse　is　proposed.　Instead　of　updating　the　failure　probability　using　newly　obtained　posterior　distributions,　the　proposed　method　transforms　the　estimate　of　posterior　failure　probability　into　a　computation　of　the　reliability　index　based　on　the　posterior　moments　of　the　limit-state　function　at　each　update.　When　calculating　these　posterior　moments,　the　terms　containing　the　likelihood　function　are　separated　from　the　limit-state　function　analysis　and　then　the　bivariate　dimension-reduction　method　(BDRM)　in　combination　with　the　point　estimate　method　(PEM)　[1,2]　is　used　to　conduct　the　calculation.　In　this　way,　the　results　evaluated　in　the　first　updating　time　can　be　reused　to　update　the　probability　of　failure　in　the　subsequent　updates,　avoiding　the　need　to　reconduct　limit-state　function　analysis　and　inverse　analysis　using　the　posterior　samples,　thus　improving　computational　efficiency.　Four　numerical　examples　are　presented　to　validate　the　efficiency　and　accuracy　of　the　proposed　method,　and　the　results　indicate　that　the　proposed　method　has　obvious　computational　advantages　for　multiple　reliability　updating　problems　without　loss　of　accuracy.　©　2023　Elsevier　Ltd