Physics-guided graph Transformer for composite pipeline damage detection

Composite pipelines are widely used in oil and gas transportation owing to their high specific strength and corrosion resistance. However, guided-wave-based damage localization remains challenging due to multimodal wave propagation, noise interference, complex propagation-path relationships, and limited labeled damage locations. To address these challenges, this study proposes a physics-guided graph Transformer (PGGT) framework for composite pipeline damage localization. In the proposed method, guided-wave signals are first preprocessed, and actuator–receiver propagation paths are represented as graph nodes. A physics-guided radius graph is constructed by incorporating the spatial configuration of piezoelectric transducers and guided-wave propagation characteristics, enabling multi-channel guided-wave signals to be mapped into a physically meaningful non-Euclidean graph structure. A convolutional module is employed to extract local temporal features from guided-wave responses, a graph attention module is utilized to model topology relationships among signal-path nodes, and a Transformer module is introduced to capture long-range global dependencies among propagation paths. Comparative experiments are conducted under four training–testing split strategies with unseen damage locations. The proposed PGGT is compared with VGG16, ResNet18, ChebyNet, GCN, GAT, Transformer, and PGGDL in terms of mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrate that PGGT achieves superior localization accuracy and generalization capability under limited labeled data. In the most challenging case, PGGT achieves a MAPE of 0.035 (that is, 3.5%), a MAE of 5.38 mm, and an RMSE of 6.23 mm. The proposed method provides an effective physics-informed learning framework for guided-wave-based damage localization of composite pipelines. Conflict of Interest Statement The authors declare no conflicts of interest.