Damage Detection in Bridge via Adversarial-Based Transfer Learning

Abstract

A novel structural damage detection (SDD) method is proposed in this work, which is based on adversarial-based transfer learning to achieve the cross-domain information transfer of damage locations between numerical simulations and real bridge structures. Although the advancement of numerical modeling technology makes it accessible for relatively accurate finite element (FE) models, it is still difficult to meet the needs of practical engineering. The idea of adversarial training is introduced to enable the traditional feature extraction network to obtain the domain independent features between the numerical simulation and the real bridge structure. The dynamic response data from the numerical simulations are labeled with damage, while those from the real structure are unlabeled. To verify the effectiveness of the proposed method, we established a FE model of a simply support beam and regarded it as the benchmark model, and the target model with discrepancies from the benchmark model is obtained by quantitatively increasing the uncertainties. The results of the simulation show that the proposed method can overcome the discrepancy caused by uncertainty to a certain extent compared with the traditional method and obtain a high damage localization accuracy on the target model. In the laboratory experiment, the proposed method still achieves promising results. The primary contributions of this work are twofold: first, it delves deeper into the effectiveness of adversarial training for extracting domain-invariant features, which are crucial for structural damage identification. Second, it provides a quantitative assessment of the performance degradation of traditional methods due to modeling errors and uncertainty. Additionally, it demonstrates the significant performance enhancement achieved by the proposed method.