The　paper　proposes　an　efficient　augmented　integral　method　for　probability　distribution　evaluation　of　performance　functions.　In　the　proposed　method,　the　performance　function　is　augmented　by　adding　an　auxiliary　random　variable,　whose　probability　density　function　(PDF)　and　cumulative　distribution　function　(CDF)　are　formulated　as　the　integrations　of　the　original　performance　function　with　respect　to　basic　random　variables.　The　optimal　auxiliary　random　variable　is　determined　to　provide　an　accurate　estimation　of　the　integrations　by　investigating　the　geometric　properties　of　integrands　and　a　distribution　parameter　optimization　approach　based　on　moment　analysis.　According　to　the　convolution　formula,　the　relationship　between　the　PDFs　of　the　augmented　performance　function　and　the　original　performance　function　is　clarified.　Then,　the　PDF　of　the　original　performance　function　is　calculated　by　solving　an　unconstrained　optimization　problem　that　is　established　using　the　convolution　formula.　Finally,　four　numerical　examples　are　investigated　to　demonstrate　the　efficiency　and　accuracy　of　the　proposed　method　for　structural　reliability　analysis.　The　results　indicate　that　the　proposed　method　can　evaluate　the　probability　distribution　of　performance　functions　accurately　and　efficiently,　even　when　the　performance　functions　are　strongly　nonlinear　and　implicit.