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Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model
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Jie LIANG1,2,4, Shijie DENG1,2, Juanjuan REN1,2, Wenlong YE1,2,4, Kaiyao ZHANG5, Dacheng LI3, Ronghe ZHANG3. Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model",
author="Jie LIANG1,2,4, Shijie DENG1,2, Juanjuan REN1,2, Wenlong YE1,2,4, Kaiyao ZHANG5, Dacheng LI3, Ronghe ZHANG3",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2400527"
}
%0 Journal Article
%T Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model
%A Jie LIANG1
%A 2
%A 4
%A Shijie DENG1
%A 2
%A Juanjuan REN1
%A 2
%A Wenlong YE1
%A 2
%A 4
%A Kaiyao ZHANG5
%A Dacheng LI3
%A Ronghe ZHANG3
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
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%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2400527
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T1 - Predicting the temperature of CRTS III ballastless tracks in cold regions based on a TCN-Track model
A1 - Jie LIANG1
A1 - 2
A1 - 4
A1 - Shijie DENG1
A1 - 2
A1 - Juanjuan REN1
A1 - 2
A1 - Wenlong YE1
A1 - 2
A1 - 4
A1 - Kaiyao ZHANG5
A1 - Dacheng LI3
A1 - Ronghe ZHANG3
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2400527
Abstract: The uneven distribution of the temperature field in the track structure, caused by various meteorological factors such as extremely low temperatures and snowfall, leads to significant temperature loads and is the primary cause of damage to China Railway Track System (CRTS) III ballastless tracks in cold regions during service. In this study, to predict the temperature of the track structure accurately, we analyzed meteorological data collected from the Shenyang area and identified the factors that had most effect on the track temperature field. We propose a Temporal Convolutional Network-based (TCN-based) temperature field prediction model for ballastless tracks (TCN-Track), which enhances the ability to extract and fuse local and global features from complex long-term meteorological data. The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data, with a Mean Absolute Error (MAE) ranging from 0.26 to 0.39, a Root Mean Square Error (RMSE) ranging from 0.32 to 0.50, and Correlation Coefficient (R) values ranging from 0.888 to 0.985. Compared with a Long Short-Term Memory (LSTM) model, the MAE of the TCN-Track model was reduced by 89.17% and the RMSE by 88.51%. This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions, aiding in predicting and preventing track damage caused by low temperatures.
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