In　the　progressive　collapse,　the　structural　topology　changed　dynamically　as　failures　propagate.　Representing　reinforced　concrete　(RC)　frames　with　graph　data　leverages　the　topology　of　beam-column　joints　and　structural　components,　enabling　graph　neural　networks　(GNNs)　to　predict　the　failure　propagation.　Graph　convolutional　networks　(GCNs),　a　specific　type　of　GNN,　offer　superior　computational　efficiency　for　graph　data　to　traditional　GNNs.　However,　three　challenges　must　be　addressed　for　GCNs　to　rapidly　assess　collapse　regions:　(1)　insufficient　data,　(2)　lack　of　graph　representations　for　structural　collapse,　and　(3)　absence　of　tailored　GCN　architectures.　To　address　these　gaps,　this　study　focused　on　RC　frames　with　the　following　initiatives:　(1)　an　efficient　and　accurate　method　for　generating　progressive　collapse　data　was　developed;　(2)　joint-based　representation　and　componentbased　representation　were　established,　accompanied　by　a　method　for　converting　RC　frames　into　these　representations;　(3)　two　GCN　architectures　were　designed　to　identify　failure　paths　and　predicting　collapse　regions.　The　performance　of　the　GCN　models　using　various　convolutional　layer　configurations　was　evaluated　on　the　two　graph　representations,　and　the　CGR-based　model　using　SAGEConv　layers　exhibited　the　best　performance　with　an　accuracy　and　F1　score　of　0.9839　and　0.8853,　respectively.　The　proposed　method　exhibited　over　a　99　%　improvement　in　calculation　efficiency　and　approximately　a　75　%　reduction　in　memory　usage　compared　with　traditional　FEM.　Moreover,　it　captured　the　contribution　of　each　component　to　progressive　collapse　resistance　with　better　accuracy　than　previous　approaches.　Finally,　this　method　was　employed　to　predict　the　internal　and　external　collapse　regions　of　a　real-world　structure　by　incorporating　a　physics　engine-based　approach　from　prior　studies.