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Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

Author
Zhong, H.; Sun, L.; Turmo, J.; Xia, Y.
Type of activity
Presentation of work at congresses
Name of edition
IABSE Congress Ghent 2021: Structural Engineering for Future Societal Needs
Date of publication
2021
Presentation's date
2021-09
Book of congress proceedings
IABSE Congress Ghent 2021: Structural Engineering for Future Societal Needs
First page
555
Last page
560
Publisher
International Association for Bridge and Structural Engineers (IABSE)
Project funding
Smart structural BIM Models for the efficient management of infrastructures
Repository
http://hdl.handle.net/2117/352698 Open in new window
Abstract
In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of t...
Citation
Zhong, H. [et al.]. Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach. A: International Association for Bridge and Structural Engineering Symposium. "IABSE Congress Ghent 2021: Structural Engineering for Future Societal Needs". International Association for Bridge and Structural Engineers (IABSE), 2021, p. 555-560.
Keywords
Bridge structural health monitoring, Data quality assessment, Frequency distribution, One-dimensional convolutional neural network
Group of research
EC - Construction Engineering

Participants

  • Zhong, Huaqiang  (author and speaker )
  • Sun, Limin  (author and speaker )
  • Turmo Coderque, Jose  (author and speaker )
  • Xia, Ye  (Corresponding author)