Structural Monitoring and Maintenance

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Deep learning-based recovery method for missing structural temperature data using LSTM network

  • Liu, Hao (School of Civil Engineering, Southeast University) ;
  • Ding, You-Liang (School of Civil Engineering, Southeast University) ;
  • Zhao, Han-Wei (School of Civil Engineering, Southeast University) ;
  • Wang, Man-Ya (School of Civil Engineering, Southeast University) ;
  • Geng, Fang-Fang (School of Architecture Engineering, Nanjing Institute of Technology)
  • Received : 2019.08.13
  • Accepted : 2019.11.15
  • Published : 2020.06.25
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Abstract

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

Keywords

Acknowledgement

The authors gratefully acknowledge the support of the Distinguished Young Scientists of Jiangsu Province (Grant. BK20190013), the National Natural Science Foundation of China (Grants. 51978154 and 51608258).

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