Unsupervised structural damage detection and severity assessment via U-GraphFormer

Damage detection in structural health monitoring (SHM) is critical for ensuring the safety and longevity of civil infrastructure. This article presents U-GraphFormer, an unsupervised deep learning model that extends its predecessor, EdgeConvFormer, by incorporating a U-Net-inspired encoder–decoder architecture, sensor-specific temporal attention, and a dynamic spatiotemporal graph learning mechanism. The model performs time-point-wise anomaly detection for early intervention and uses per-segment statistics, mean, and standard deviation of reconstruction errors, for an interpretable severity assessment. Validated in various benchmarks, including the International Association for Structural Control and Monitoring–the American Society of Civil Engineers (IASC–ASCE) steel frame, the artificially damaged steel truss bridge, and an offshore wind turbine bolt-loosening data set, U-GraphFormer shows strong performance. In the ASCE benchmark, the model achieves up to 0.99 precision, 0.98 recall, and 0.98 F1 score for severe damage, with segment-wise alarm accuracy reaching 100% in minor damage scenarios. The model adaptively selects between the base and Gaussian kernel scoring mechanisms based on anomaly subtlety, thereby ensuring high detection reliability. Furthermore, the segment-wise mean and standard deviation of the severity assessment reflect the progression of physical damage and rank faults consistent with the ground truth. Case studies confirm that early detection is achieved in seconds, and the model accurately distinguishes between minor, moderate, and severe damage. With low computational cost, real-time inference, and strong generalizability, U-GraphFormer offers a scalable and practical solution for continuous SHM in real-world applications.