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Smart Structures and Systems
  Volume 32, Number 2, August 2023 , pages 83-99
DOI: https://doi.org/10.12989/sss.2023.32.2.083
 


Temperature distribution prediction in longitudinal ballastless slab track with various neural network methods
Hanlin Liu, Wenhao Yuan, Rui Zhou, Yanliang Du, Jingmang Xu and Rong Chen

 
Abstract
    The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.
 
Key Words
    ballastless track-bridge structure; field measuring platform; meteorological factors; neural network methods; temperature prediction
 
Address
1) Hanlin Liu:
Mining College, Guizhou University, Guiyang, Guizhou Province, China;
(2) Wenhao Yuan, Rui Zhou, Yanliang Du:
School of Civil Engineering & Traffic Engineering, Shenzhen University, Shenzhen, Guangdong Province, China;
(3) Hanlin Liu, Rui Zhou, Jingmang Xu, Rong Chen:
MOE Key Laboratory of High-Speed, Railway Engineering, Southwest Jiaotong University, Chengdu, Sichuan Province, China.
 

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