On Heterogenous Bridge Monitoring Across Various Structural Changes by Hybridized Unsupervised Learning

Yu Wu, Amirhossein Haydarzadeh, Alireza Entezami, Hassan Sarmadi

Ensuring the long-term integrity and health of bridge structures under diverse structural, environmental, and operational conditions remains a persistent challenge within the structural health monitoring (SHM) community. Although machine learning–aided unsupervised anomaly detection offers promising solutions for vibration-based SHM, its generalization and robustness across different bridge types and structural conditions are still limited. A model trained for one bridge often fails to perform effectively on another with distinct material properties, geometries, or boundary conditions. To overcome this limitation, this study introduces a novel hybridized unsupervised learning (HUL) framework for detecting structural changes in heterogeneous concrete bridge systems. The proposed framework employs daily measurement of modal frequencies as the primary dynamic features for vibration-based change detection, performing two essential tasks including data normalization by synergetic integration of Bidirectional Encoder Representation from Transformer (BERT) with statistical regularization of a Generative Adversarial Network (GAN), to suppress environmentally/operationally-induced outliers. At the last stage of HUL the change detection is conducted via applying a statistical anomaly detector on obtained residuals to identify genuine structural changes. Using a masked training strategy within the GAN structure, the generator utilizes a self-attention mechanism to reconstruct input sequences of modal frequencies where a specific portion of time steps are stochastically masked. By minimizing a dual-loss objective combining reconstruction error with adversarial loss, the model learns underlying temporal correlations of the healthy bridge state without requiring labelled damage data. Three concrete bridges, including one numerical and two experimental structures, with distinct configurations are used to validate the proposed method. Moreover, structural variations are categorized into four representative classes: detrimental (damage), innocuous (normal environmental or operational conditions), extreme (severe environmental and operational conditions), and mixed changes. Results indicate that the HUL framework can effectively detect true structural changes while mitigating false alarms arising from environmental and operational variability, thus providing a generalizable and robust approach for concrete bridge monitoring.