Unsupervised anomaly detection for long-span bridges combining response forecasting by deep learning with Td-MPCA

This paper proposes an unsupervised anomaly detection (AD) scheme combining response forecasting by deep learning (DL) and temperature-driven moving principal component analysis (Td-MPCA), by only utilizing the monitoring strain data of intact or baseline structures for training. The major contributions and novelties of the proposed method are as follows. First, the bidirectional long- and short-term time-series network with attention mechanism (BiLSTNet-A) model is carefully designed for response forecasting. It is proved to have better capability to predict the structural response more accurately than several state-of-the-art DL models using the monitoring data of a long-span bridge, even in the presence of drastic temperature changes and incomplete data, because BiLSTNet-A can better capture long- and short-term patterns of time series data simultaneously than these models. Second, an BiLSTNet-A-based unsupervised AD approach is developed as compared with the Td-MPCA method using both simulation and monitoring datasets. The results illustrate that BiLSTNet-A is more robust to temperature loading while Td-MPCA is less influenced by damage location. Finally, an unsupervised structural AD scheme is further developed by combining the proposed BiLSTNet-A-based AD and Td-MPCA methods. The results demonstrate that the proposed scheme can provide more reliable and robust AD results than the two methods used separately, and has potential for application in practical engineering.