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.
Highlights
The importance of structural health monitoring (SHM) is increasing to ensure the safety and durability of large structures such as tunnels, buildings, infrastructure, and bridges
EVALUATION OF THE PROPOSED ANOMALY DETECTION METHOD we analyze the performance of the proposed anomaly detection method utilizing tension data from the actual cable-stayed bridge
In the first anomaly detection step, we present that the proposed framework successfully evaluates the bridge condition by the damage detection
Summary
The importance of structural health monitoring (SHM) is increasing to ensure the safety and durability of large structures such as tunnels, buildings, infrastructure, and bridges. The key to SHM is to detect damages within structures and avoid socio-economic losses such as infrastructure collapse. In cable-stayed bridges, the structures tend to be damaged and even collapsed due to various causes such as weather. Damaged cables may deteriorate the bridge condition due to the loss of load-carrying capacity [1]. A strategy is needed to evaluate and analyze the cable condition accurately. The development of the sensor network system allows us to accumulate a vast amount of SHM data. Large-scale time-series data accumulated over time from the SHM system serves as the basis for structural assessment and is broadly
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