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Joint recovery method for multi-channel bridge monitoring data considering spatiotemporal correlation
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Jingzhou XIN1, 2, Weitong YANG1, 2, Jianting ZHOU1, 2, Qizhi TANG1, 2, Chaoying ZHOU1, 2, Hong ZHANG1, 2
Journal of Vibration Engineering | 2025, 38(3) : 558 - 566
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Journal of Vibration Engineering | 2025, 38(3): 558-566
Joint recovery method for multi-channel bridge monitoring data considering spatiotemporal correlation
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Jingzhou XIN1, 2, Weitong YANG1, 2, Jianting ZHOU1, 2, Qizhi TANG1, 2, Chaoying ZHOU1, 2, Hong ZHANG1, 2
Affiliations
  • 1.State Key Laboratory of Mountain Bridge and Tunnel Engineering，Chongqing Jiaotong University，Chongqing 400074，China
  • 2.School of Civil Engineering，Chongqing Jiaotong University，Chongqing 400074，China
Published: 2025-03-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.03.012
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Bridge health monitoring data often encounter missing values due to sensor failures and other factors. Existing data recovery methods have not effectively utilized the temporal and spatial correlations in the data. In this paper，a multi-channel recovery method for bridge monitoring data based on temporal and spatial correlations is proposed. The original data is preprocessed using a Kalman filter to eliminate random errors. The preprocessed data is divided into training and testing sets，and training samples are constructed using a sliding window approach with masking. The data recovery issue is formulated as a time series prediction issue. Besides，an end-to-end LSTM network architecture is trained to leverage the temporal and spatial correlations in the historical data of the sensors which enables the recovery of missing data. The proposed method is validated using the measured deflection and cable force data from a suspension bridge，and the performance of single-channel and multi-channel data recovery is discussed. Compared to the traditional RNN models，results show that the proposed method achieves a 22% improvement in accuracy when the data missing rate is 60%. Moreover，the method effectively utilizes the temporal and spatial correlations among different channels，enabling simultaneous recovery of data from multiple channels.

bridge health monitoring  /  bridge engineering  /  data recovery  /  long short-term memory neural network  /  spatiotemporal correlation
Jingzhou XIN, Weitong YANG, Jianting ZHOU, Qizhi TANG, Chaoying ZHOU, Hong ZHANG. Joint recovery method for multi-channel bridge monitoring data considering spatiotemporal correlation[J]. Journal of Vibration Engineering, 2025 , 38 (3) : 558 -566 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.03.012
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.03.012
  • Receive Date：2023-08-26
  • Online Date：2026-02-11
  • Published：2025-03-10
Article Data
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History
  • Received：2023-08-26
  • Revised：2024-01-23
Funding
Affiliations
    1.State Key Laboratory of Mountain Bridge and Tunnel Engineering，Chongqing Jiaotong University，Chongqing 400074，China
    2.School of Civil Engineering，Chongqing Jiaotong University，Chongqing 400074，China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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