1. Bridge health anomaly detection using deep support vector data description
- Author
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Shixin Jiang, F. S. Yang, Le Zhang, Jianxi Yang, Likai Zhang, Guiping Wang, Zeng Zeng, and Ren Li
- Subjects
Structure (mathematical logic) ,0209 industrial biotechnology ,Computer science ,business.industry ,Cognitive Neuroscience ,Deep learning ,Inference ,02 engineering and technology ,Hypersphere ,Machine learning ,computer.software_genre ,Bridge (interpersonal) ,Computer Science Applications ,Support vector machine ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Anomaly detection ,Artificial intelligence ,business ,computer - Abstract
As an extremely important part of traffic 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 aboard. In this paper, we propose a deep learning framework to evaluate the safety of the bridge structural state. More specifically, 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.
- Published
- 2021
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