Yang, J., Yang, F., Zhang, L., Li, R., Jiang, S., Wang, G., Zhang, L., & Zeng, Z. (2021). Bridge health anomaly detection using deep support vector data description. Neurocomputing, 444, 170–178. https://doi.org/10.1016/j.neucom.2020.08.087
As an extremely important part of trac arteries, bridge structure plays an essential role in national economic construction, social development and smart city. Thus the monitoring of the bridge structure health are increasingly concerned by the bridge industry scholars and engineering people at home and abroad. In this paper, we propose a deep learning framework to evaluate the safety of the bridge structural state. More specically, the proposed system generates a learnable transformation which attempts to map
most of the data network representations into a hypersphere characterized of minimum volume. During inference, mappings of normal examples fall within the learned hypersphere, whereas mappings of anomalies fall outside the hypersphere. The whole system is end-to-end trainable and outperforms
other advanced methods in real-world dataset.
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
This work was supported by the Science and Technology Planning Project of Yunnan Province (2017IB025), the Science and Technology Research Program of Chongqing Municipal Education Commission of China (KJQN201800705, KJQN201900726), and the Breeding Program of National Natural Science Foundation in Chongqing435 Jiaotong University (PY201834).