Cross-domain Structural Damage Identification Using Frequency Guided Cycle-Consistent Generative Adversarial Network

Discrepancies between Finite Element Model (FEM) simulations and actual measurements result in substantial domain mismatches, posing a significant challenge for model-driven structural damage identification. Existing cross-domain damage identification methods commonly suffer from misalignment between domain-crossing features and damage-related features, as well as unstable network training, thereby limiting their effectiveness. To address these issues and achieve both efficient domain adaptation and high-precision damage identification, this study proposes a Frequency-Guided Cycle-Consistent Generative Adversarial Network (FG-CycleGAN), integrated with a Residual Neural Network (ResNet). First, frequency cosine similarity is introduced into the adversarial training process to quantify spectral discrepancies between generated and measured samples, ensuring the preservation of damage-relevant features during the cross-domain transformation. Subsequently, ResNet is employed to extract essential features from the samples generated by FG-CycleGAN and map them to corresponding structural damage states. To validate the approach, a damage identification experiment is conducted on a steel truss model. Comparative analysis reveals that conventional Adversarial Discriminative Domain Adaptation (ADDA) yields a relatively low F1-score of 0.35, while the basic CycleGAN achieves 0.92. In contrast, the proposed FG-CycleGAN further improves performance, attaining an F1-score of 0.99. The results confirm that FG-CycleGAN not only outperforms existing methods in terms of accuracy but also offers a robust framework for cross-domain structural damage identification.