Techno Press
You logged in as Techno Press

Structural Monitoring and Maintenance
  Volume 7, Number 2, June 2020, pages 109-124
DOI: http://dx.doi.org/10.12989/smm.2020.7.2.109
 


Deep learning-based recovery method for missing structural temperature data using LSTM network
Hao Liu, You-Liang Ding, Han-Wei Zhao, Man-Ya Wang and Fang-Fang Geng

 
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.
 
Key Words
    structural health monitoring (SHM); structural temperature; deep learning; LSTM network; missing data recovery
 
Address
You-Liang Ding, Han-Wei Zhao and Man-Ya Wang: School of Civil Engineering, Southeast University, Nanjing 210096, China;
Key Laboratory of C&PC Structures of the Ministry of Education,
Southeast University, Nanjing 210096, China
Hao Liu: School of Civil Engineering, Southeast University, Nanjing 210096, China;
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, HKSAR
Fang-Fang Geng: School of Architecture Engineering, Nanjing Institute of Technology, Nanjing 211167, China
 

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2021 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: info@techno-press.com