In　order　to　realize　the　intelligent　inference　of　structural　vulnerability,　Bayesian　networks　(BNs)　have　been　adopted　for　reconstructing　the　analysis　system　of　a　truss　structure.　Firstly,　the　external　load　combination,　the　truss　system　and　its　members　are　defined　as　the　top　parent　nodes,　the　middle　nodes　and　the　bottom　child　nodes　of　the　network,　respectively.　These　nodes　are　connected　by　some　directed　edges　representing　the　causality　between　them.　Therefore,　the　BN　topology　of　the　truss　structure　is　defined.　Secondly,　serious　damage　is　suggested　to　replace　the　commonly-used　assumption　of　conceptual　removal　in　vulnerability　analysis.　Based　on　the　uncertainty　distributions　of　the　parameters　of　members　and　external　loads,　the　samples　are　randomly　drawn　from　the　probability　distributions　for　learning　the　conditional　probability　tables　between　the　different　nodes,　thereby　realizing　the　BN　establishment.　Thirdly,　the　observed　state　of　a　specific　member　is　used　as　the　evidence　into　the　established　BN　for　synchronously　inferring　the　state　probabilities　of　the　other　members,　based　on　which　the　member　importance　coefficient　is　calculated.　The　sum　of　all　the　importance　coefficients　is　further　defined　as　the　system　vulnerability　index.　Finally,　a　member　vulnerability　index　is　proposed　to　predict　the　most　probable　failure　path　of　the　truss　system.　The　numerical　and　experimental　examples　have　demonstrated　that　the　proposed　method　can　effectively　evaluate　the　importance　of　each　member　within　a　truss　system.　The　inferred　failure　path　accords　well　with　the　experimental　observation.　The　estimated　value　of　the　system　vulnerability　index　of　the　experimental　truss　is　far　less　than　the　number　of　members,　and　thus,　it　indicates　the　low　possibility　of　progressive　collapse　of　the　system　due　to　the　damaged　member.　©　2024　Institute　of　Engineering　Mechanics　(IEM).　All　rights　reserved.