Graph Neural Network-Based Structural Health Monitoring of Intelligent Laminated Damping Systems
Keywords:
structural health monitoring; graph neural networks; laminated damping; smart structures; sensor networks; infrastructure governance; robustness; fairness; edge intelligence; digital twinAbstract
The integration of structural health monitoring (SHM) with intelligent laminated damping systems presents a significant opportunity to enhance the resilience, safety, and longevity of critical civil, aerospace, and mechanical infrastructures. This paper investigates the system-level design and deployment of graph neural network (GNN) architectures for the real-time assessment and prognostics of such smart composite structures. Intelligent laminated damping systems combine viscoelastic or constrained-layer damping treatments with embedded sensor arrays, generating spatially distributed, high-dimensional dynamic data that pose unique challenges for conventional signal processing. GNNs, by virtue of their relational inductive bias, are inherently suited to model the complex topological dependencies among sensor nodes, damage-sensitive features, and laminate layup configurations. The paper provides a comprehensive examination of the architectural choices, integration patterns, and trade-offs involved in building GNN-driven monitoring frameworks. Emphasis is placed on the fusion of physics-informed representations, the translation of laminate-scale phenomena into graph abstractions, and the orchestration of data pipelines from edge sensing to cloud analytics. Beyond technical design, the discussion extends to life-cycle governance, sustainability of monitoring infrastructures, adversarial robustness of learned models, algorithmic fairness in multi-asset management, and policy implications for the adoption of intelligent SHM systems. Case illustrations from aerospace skins, wind turbine blades, and bridge retrofits are used to ground the analysis. The work articulates a forward-looking perspective on how GNN-based monitoring can evolve from isolated prototypes to trusted, scalable, and equitable decision-support layers within the next generation of adaptive structural systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
