A comprehensive bridge impact analysis system combining heterogeneous graph neural networks (HGNN) for closure-impact prediction with graph autoencoders (GAE/VGAE/HetVGAE) for unsupervised bridge similarity learning. This extends the system with heterogeneous graph variational autoencoders for metapath-based bridge classification.
unsupervised-learning similarity-learning structural-health-monitoring vgae hgnn graph-autoencoder infrastructure-analysis bridge-impact-analysis heterogeneous-graph-neural-networks hetvgae metapath-learning closure-impact-prediction
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Updated
Apr 13, 2026 - HTML