This work presents a novel Graph Neural Network (GNN) based framework for structural damage detection and localization in composite aerospace structures. The sensor network is modeled as a graph whose nodes correspond to the strain measurement points placed on the system, while the edges capture spatial and structural relationships among sensors. Strain mode shapes, extracted via Automated Operational Modal Analysis (AOMA), are used as input features for the GNN. Two architectures are developed: one for binary damage detection and another for damage localization, the latter outputting a spatial probability distribution of damage over the structure. Both networks are trained and validated on synthetic datasets generated from high-fidelity finite element transient simulations performed on a composite wing equipped with 40 strain sensors. The obtained results show strong effectiveness in both detection and localization tasks, thus highlighting the potential of leveraging GNNs for topology-aware Structural Health Monitoring applications. In particular, the proposed framework achieves an AUC of 0.97 for damage detection and a mean localization error of approximately 3% of the wingspan on the synthetic dataset. The performance of the GNN is also compared with a fully connected and a convolutional neural network, demonstrating significant improvements in the localization accuracy.
Priore et al. (Fri,) studied this question.