Physics-Data Synergy in Structural Health Monitoring: A Multi-Scale Graph Contrastive Framework With Temperature-Adaptive Fusion

Operational Structural Health Monitoring (SHM) currently faces a tripartite dilemma: the scarcity of labeled damage data from real-world infrastructure, the masking effects of environmental variability, and the prohibitive computational costs associated with high-fidelity physics simulations. To resolve these conflicts, this article presents a physics-data dual-driven framework that harmonizes finite element analysis (FEA) with self-supervised contrastive learning. The architecture employs a novel dual-matrix strategy: a static, physics-based matrix encoding mechanical constraints derived from modal analysis, and a dynamic monitoring matrix learned via hierarchical InfoNCE objectives across node, subgraph, and…