Attention-based spatiotemporal graph models for full-field acceleration reconstruction

Abstract Structural health monitoring (SHM) often suffers from sensor sparsity, making full-field response estimation challenging. We propose a physics-informed spatio-temporal graph network that represents the structure as a complete graph with 34-dimensional node descriptors and 17-dimensional edge attributes. A dual-stream encoder integrates graph attention networks (spatial features) with bidirectional recurrent units (temporal features), followed by cross-attention to propagate information from observed to unobserved nodes. Validated on the IASC-ASCE SHM benchmark with 50% sensor coverage, the model achieves R 2 = 0.52 and correlation ρ = 0.73 in the test split, demonstrating consistent reconstruction of dominant structural dynamics under sparse sensing.