To　reduce　the　runtime　and　ensure　enough　computation　accuracy,　this　paper　proposes　a　structural　reliability　assessment　method　by　the　use　of　sensitivity　analysis　(SA)　and　support　vector　machine　(SVM).　The　sensitivity　analysis　is　firstly　applied　to　assess　the　effect　of　random　variables　on　the　values　of　performance　function,　while　the　small-influence　variables　are　rejected　as　input　vectors　of　SVM.　Then,　the　trained　SVM　is　used　to　classify　the　input　vectors,　which　are　produced　by　sampling　the　residual　variables　based　on　their　distributions.　Finally,　the　reliability　assessment　is　implemented　with　the　aid　of　reliability　theory.　A　10-bar　planar　truss　is　used　to　validate　the　feasibility　and　efficiency　of　the　proposed　method,　and　a　performance　comparison　is　made　with　other　existing　methods.　The　results　show　that　the　proposed　method　can　largely　save　the　runtime　with　less　reduction　of　the　accuracy;　furthermore,　the　accuracy　using　the　proposed　method　is　the　highest　among　the　methods　employed.　©　2014　Shao-Fei　Jiang　et　al.