Structural health monitoring (SHM) of civil structures, especially bridges, using vibration data is a practical approach for ensuring their functionality and integrity. However, long-term vibration monitoring of these structures faces challenges due to seasonal environmental variations. This study proposes an innovative machine learning method to enhance SHM of bridges subjected to environmental variability. Developed from the concept of unsupervised learning, this method combines teacher–student clustering with regional anomaly detection. The proposed dual-model framework features a sophisticated parametric clustering model (i.e., the teacher) that guides a simpler non-parametric clustering model (i.e., the student) to generate localized data. This setup improves data segmentation and produces localized training subsets for anomaly detector modeling. These local data are then used to train a regional one-class support vector machine, which serves as the main anomaly detector for SHM. This model computes anomaly indices by effectively isolating genuine structural anomalies from patterns of environmental variability. The major contributions of this research are twofold: it introduces an unsupervised learning solution for long-term SHM amid significant environmental changes in vibration features and integrates clustering and anomaly detection to develop a new hybrid framework, thereby enhancing the reliability of monitoring programs. Long-term modal frequencies of large-scale bridge structures are used to validate the proposed method, supported by several comparative analyses. Results indicate that the proposed method not only mitigates environmental variability but also provides reliable decision-making.
Mahmoudkelayeh et al. (Mon,) studied this question.