Transfer Learning-Enhanced Residual Attention Temporal Network for Structural Damage Identification

Accurate structural damage identification under limited data availability and measurement noise remains a persistent challenge in structural health monitoring (SHM). This study proposes TLCA-RATNet, a transfer learning-enhanced residual attention temporal network for vibration-based damage state classification under noisy and small sample conditions. RATNet integrates adaptive threshold residual denoising, residual attention, and bidirectional gated recurrent unit (BiGRU)-based temporal modeling to suppress noise, emphasize damage-sensitive features, and capture global temporal dependencies. The current implementation is formulated as a single-task classifier, in which local feature refinement and global temporal representation are jointly optimized end-to-end through a unified damage classification objective. Transfer learning further initializes the target domain model using knowledge learned from a data-rich source structure, while regularized fine-tuning reduces overfitting on limited target samples. Experiments were conducted on a six-story lumped-mass shear structure, a three-story physical frame, and the IASC-ASCE SHM benchmark structure, using 10–145 training samples per damage class and additive-noise conditions ranging from 0 to 30 dB signal-to-noise ratio. On Dataset 2 at 5 dB, TLCA-RATNet achieved an accuracy of 89.86%, exceeding LSTM and CNN-BiGRU by 9.94 and 13.10 percentage points, respectively. On Dataset 3 at 0 dB, it achieved 86.00% accuracy, outperforming CNN-BiGRU by 10.72 percentage points. In the limited sample transfer experiment on Dataset 2, transfer learning increased the accuracy from 93.05% to 100.00%, representing a gain of 6.95 percentage points over training from scratch. These results indicate that TLCA-RATNet provides a data-efficient and noise-robust approach for damage state screening and rapid model adaptation in SHM applications with scarce labeled data and noisy measurements.