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Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Qing Ai1,2, Hao Tian2,3,*, Hui Wang1,*, Qing Lang1, Xingchun Huang1, Xinghong Jiang4, Qiang Jing5

1 School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
2 Key Laboratory of Road and Bridge Detection and Maintenance Technology of Zhejiang Province, Hangzhou, 311305, China
3 Zhejiang Scientific Research Institute of Transport, Hangzhou, 310023, China
4 State Key Laboratory of Coal Mine Dynamics and Control, Chongqing University, Chongqing, 400044, China
5 Hong Kong-Zhuhai-Macao Bridge Authority, Zhuhai, 519060, China

* Corresponding Authors: Hao Tian. Email: email; Hui Wang. Email: email

TSP_CMES_45251.pdf

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