A novel autoencoder for structural anomalies detection in river tunnel operation

Abstract

Anomaly diagnosis is essential to prevent disasters and ensure long-term stable operation of tunnels. However, the diversity and scarcity of abnormal data make it difficult to identify outliers, especially to diagnose structural anomalies from poor-quality data. Therefore, an adaptive loss function improved autoencoder (AdaAE) model is proposed for anomaly detection, which is robust to poor-quality data and can accurately determine the anomaly source of river tunnel. To expand the abnormal dataset, numerical simulation of structure under extreme conditions and Gaussian noise are adopted to construct structural damage data and disturbance data respectively. The proposed model is then instantiated on the prepared dataset. Finally, the reliability and the advantage of the proposed model are verified by ablation study. The research results indicate that the detection ability of AdaAE model is greatly improved to that of the widely used methods. This model is suitable to poor quality dataset, and the accuracy to detect structural anomalies from pollution data sets is more than 90%. As a case study, the AdaAE model is applied to the Wuhan Yangtze River tunnel to detect anomalies of segment strain. This study would play a role in preventing structural diseases and promoting intelligent management during tunnel operation.