Deep Learning-Based Anomaly Detection to Classify Inaccurate Data and Damaged Condition of a Cable-Stayed Bridge

Abstract

Cables of cable-stayed bridges are gradually damaged by weather conditions, vehicle loads, and corrosion of materials. Stayed cables are an essential factor closely related to the stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to tension capacity lost. Therefore, it is necessary to develop structural health monitoring (SHM) techniques that check the cable conditions. Besides, the sensor network system development has contributed to the state analysis, such as damage detection and structural deformation, by allowing us to collect large-scale SHM data. However, the collected SHM data might include abnormal data due to device malfunctioning or unexpected environmental inconstancies. Furthermore, since data anomalies interfere with accurate structural evaluation, we need to identify anomalies and treat them appropriately in the data preprocessing stage. However, the cause of anomalies may be either temporary errors or actual structural deformation. Anomalies caused by structural damage or sensor device failure are informative data that must not be replaced or deleted. In this paper, we distinguish between anomalies as inaccurate data and anomalies related to the state of structures or sensor devices and propose a framework to identify each of them. We train a Long Short Term Memory (LSTM) network based Encoder-Decoder architecture that processes multivariate time series and learn temporal correlation. The trained LSTM network discovers anomalies by calculating anomaly scores. We determine the anomalies emerging intermittently as errors and correct the erroneous data. If the anomalies persist, we recognize the data as generated by bridge damage or sensor device failure. We evaluate the proposed technique with cable tension data from an actual cable-stayed bridge.

Keywords

• SHM
• LSTM
• anomaly detection
• time series
• deep learning