An Adversarial-Based Method for Knowledge Transfer Across Bridges

Data-driven machine learning methods for bridge health assessment often face limitations due to the scarcity of labeled data, particularly related to damaged conditions, which are often difficult, expensive, or even impossible to obtain in practice. To address this challenge, transfer learning offers a promising solution by leveraging knowledge gained from one structure (or domain), with sufficient labeled data, to identify damage on a different but related structure where labeled data are limited or unavailable. In this study, we propose a domain adversarial neural network-based method to enhance damage classification performance across different bridge structures. Using the natural frequencies collected over long-term monitoring campaigns, the machine learning model is trained with an adversarial strategy to learn damage-sensitive and domain-invariant features for improving generalization to new structures with minimal additional data collection. To validate the methodology, transfer learning results are analyzed by first considering two post-tensioned concrete bridges, the Z24 bridge and the S101 bridge, and afterwards their finite element models, where different damage scenarios in terms of localization and severity are simulated. Furthermore, the performance of the proposed method is compared with different transfer learning approaches previously applied in the same structural health monitoring context. The improvement of damage classification results via transfer learning highlights the potential of domain-adversarial learning to advance scalable monitoring strategies for bridge networks.