On continuous health monitoring of bridges under serious environmental variability by an innovative multi-task unsupervised learning method

Design of an automated and continuous framework is of paramount importance to structural health monitoring (SHM). This study proposes an innovative multi-task unsupervised learning method for early assessment of damage in large-scale bridge structures under long-term monitoring. This method entails three main tasks of data cleaning, data partitioning, and anomaly detection. The first task includes discarding missing data and providing outlier-free samples by developing an approach based on the well-known DBSCAN algorithm. Accordingly, this approach enforces the DBSCAN to generate two clusters, one of which contains outlier-free samples and the other one comprises outlier data. In the second task, the outlier-free samples are fed into spectral clustering to partition them into local clusters. Subsequently, a cluster with the maximum cumulative local density is selected as the optimal partition whose features are extracted as the representative data. Finally, local empirical measures under the theory of empirical learning are used to compute anomaly indices for SHM. Long-term modal frequencies of two full-scale bridges are incorporated to verify the proposed method alongside comparative analyses. Results prove that this method can effectively detect damage by providing discriminative anomaly scores and mitigating the negative influences of severe environmental variability.