Smart Structures and Systems

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Temperature distribution prediction in longitudinal ballastless slab track with various neural network methods

  • Hanlin Liu (Mining College, Guizhou University) ;
  • Wenhao Yuan (School of Civil Engineering & Traffic Engineering, Shenzhen University) ;
  • Rui Zhou (School of Civil Engineering & Traffic Engineering, Shenzhen University) ;
  • Yanliang Du (School of Civil Engineering & Traffic Engineering, Shenzhen University) ;
  • Jingmang Xu (MOE Key Laboratory of High-Speed, Railway Engineering, Southwest Jiaotong University) ;
  • Rong Chen (MOE Key Laboratory of High-Speed, Railway Engineering, Southwest Jiaotong University)
  • Received : 2023.04.10
  • Accepted : 2023.08.15
  • Published : 2023.08.25
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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.

Keywords

Acknowledgement

The authors gratefully acknowledge support from the National Key Technologies Research and Development Program "Transportation Infrastructure" "Reveal the list and take command" project (No.2022YFB2603301), National Natural Science Foundation (No. 52278311 and 52008264), the Shenzhen Science and Technology Program under the grant (Nos. GJHZ20220913143006012, GJHZ20200731095802007 and KQTD20180412181337494), Guizhou University talentproject [2022]68, in part by the Foundation of Key L aboratory of Large Structure Health Monitoring and Control in Hebei Province under Grant KLLSHMC2108,MOE Key Laboratory of High-Speed, Railway Engineering,Southwest Jiaotong University: 2021 Open Fund, the Ministry of Education. the Project of Science and t echnology research and development of China RailwayCo., Ltd (No. K2022G038) and the Guangdong Province Natural Science Foundation (No. 2023A1515030148 and 2022A1515010665).

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