This study introduces GAT-CAMDA, a novel framework for structural health monitoring (SHM) of composite materials under temperature-induced variability, leveraging the powerful feature extraction capabilities of Graph Attention Networks (GATs) and advanced domain adaptation techniques. By combining Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) losses with domain-discriminative adversarial layer, the framework achieves precise alignment of feature distributions across temperature domains, ensuring robust damage detection without relying on labelled target data. A bespoke data augmentation process extrapolates damage behaviour across unmeasured temperature conditions, addressing the scarcity of damaged-state observations. Hyperparameter optimisation via Optuna not only identifies optimal settings to enhance model performance, achieving a classification accuracy of 95.83% on a benchmark dataset but also illustrates hyperparameter significance for explainability. Additionally, the GAT architecture’s attention demonstrates the importance of various sensors, enhancing transparency and reliability in damage detection. The dual use of Optuna serves to refine model accuracy and elucidate parameter impacts, while GAT-CAMDA represents a significant advancement in SHM, enabling precise, interpretable, and scalable diagnostics across complex operational environments.
Rezazadeh et al. (Fri,) studied this question.