Investigation on identification of structural anomalies from polluted data sets using an unsupervised learning method

Abstract

Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters, so monitoring is required. Data collected by structural health monitoring (SHM) systems are easily affected by many factors, such as temperature, sensor fluctuation, sensor failure, which can introduce a lot of noise, increasing the difficulty of structural anomaly identification. To address this problem, this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder (CIDAE), a denoising autoencoder-based deep learning model for SHM of civil infrastructure. As a case study, the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation. Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted. It is concluded that CIDAE is superior to traditional methods.

Graphical abstract

Keywords

structural health monitoring / deep learning / anomaly detection

References

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51991395, 51991391, and U1811463) and the S&T Program of Hebei, China (No. 225A0802D).

Competing interests

The authors declare that they have no competing interests.

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