Deep learning-based reconstruction of missing long-term girder-end displacement data for suspension bridge health monitoring

Wang, Zhi-wei; Lu, Xiao-fan; Zhang, Wen-ming; Fragkoulis, Vasileios C.; Beer, Michael; Zhang, Yu-feng

doi:10.1016/j.compstruc.2023.107070

Cited by 12 publications

(1 citation statement)

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“…This method reconstructed the missing data of GED in the Jiangyin bridge SHM system. This method has high recovery accuracy [20]. Wang C and Zhang P presented a combined method of autoregressive model and matrix factorization method to reach the tasks of data imputation and prediction.…”

Section: Introductionmentioning

confidence: 99%

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

Hao,

Liu,

et al. 2024

Meas. Sci. Technol.

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Structure health monitoring (SHM) systems play a crucial role in understanding the condition of structures. However, owing to various uncertain factors, sensor data may be missing, which poses a great challenge to capture the real-time dynamic characteristics of the bridge. To address this problem, this paper proposes a model that integrates time varying filtering based empirical mode decomposition (TVFEMD), convolutional neural network (CNN), and gated recurrent unit (GRU) to forecast and fill in the missing data. The proposed model initially employs TVFEMD to decompose the signal into several intrinsic mode functions (IMFs) of different frequency bands. Subsequently, CNN is utilized to extract data features for each IMF, followed by prediction through GRU. The model linearly combines the prediction results obtained from each IMF to obtain the actual prediction result. Simulation data and measured data from the large railway bridge are utilized in this research to validate the model’s efficacy. The analysis results demonstrate a significant improvement in prediction performance compared to traditional models, showcasing strong generalization ability and robustness. In conclusion, the model proposed in this paper has a high utilization value in health monitoring data recovery.

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Section: Introductionmentioning

confidence: 99%