Restoring method of structural abnormal monitoring data based on GRU neural network

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (9) : 328-338.

JU Hanwen1, DENG Yang1,2, LI Aiqun1,2

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Abstract

There are usually a large number of anomaly monitoring data in structural health monitoring systems. To ensure the integrity and practicability of data, it is necessary to restore anomaly monitoring data. Most studies on restoring anomaly data based on deep learning usually used single input dimension and unidirectional prediction to build models. This paper proposed a restoration method of structural anomaly monitoring data based on a Gated Recurrent Unit (GRU) neural network. The advantage of deep learning neural network to deal with the complex nonlinear mapping problem was fully utilized in this method by optimizing and reconstructing GRU neural network. The configurations of input and output of neural network were optimized by using the correlations of temperature and time series. Meanwhile a bidirectional sequence prediction method by using the information before and after anomaly data sequences was proposed to improve the prediction and restoration accuracy. At last, the proposed method was verified based on the strain, crack, and temperature monitoring data of an ancient city wall. The reconstructed GRU neural network model was used to restore the anomaly data sequences, and the restoration accuracy was compared with Long and Short-Term Memory (LSTM) neural network and Back Propagation (BP) neural network. The results show that compared with the neural network model of single input dimension and unidirectional prediction, the reconstructed GRU neural network has better prediction accuracy. And the prediction accuracy of the reconstructed GRU neural network is also significantly better than that of LSTM neural network and BP neural network. After anomaly data sequences are restored, the linear correlation of structural temperature and responses including strain and crack width gets greatly enhanced. The proposed method has a great ability to restore structural monitoring data with temperature correlation.

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structural health monitoring / data restoration / deep learning / neural network / temperature

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JU Hanwen1, DENG Yang1,2, LI Aiqun1,2. Restoring method of structural abnormal monitoring data based on GRU neural network[J]. Journal of Vibration and Shock, 2023, 42(9): 328-338

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