Analysis and prediction of temperature distribution of steel trusses for kilometer-scale rail-cum-road bridge

ZHENG Gankang; HE Yuelei; MENG Xiaoliang; WAN Leshan

doi:10.12299/jsues.24-0018

Volume 38 Issue 4

Dec. 2024

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Article Navigation > Journal of Shanghai University of Engineering Science > 2024 > 38(4): 458-464

ZHENG Gankang, HE Yuelei, MENG Xiaoliang, WAN Leshan. Analysis and prediction of temperature distribution of steel trusses for kilometer-scale rail-cum-road bridge[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 458-464. doi: 10.12299/jsues.24-0018

Citation:

ZHENG Gankang, HE Yuelei, MENG Xiaoliang, WAN Leshan. Analysis and prediction of temperature distribution of steel trusses for kilometer-scale rail-cum-road bridge[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 458-464. doi: 10.12299/jsues.24-0018

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Analysis and prediction of temperature distribution of steel trusses for kilometer-scale rail-cum-road bridge

doi: 10.12299/jsues.24-0018

1.
School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Huai'an High-speed Railway Infrastructure Section of China Railway Shanghai Bureau Group Co., Huai'an 22300, China

Received Date: 2024-01-17
Publish Date: 2024-12-31

Abstract

Abstract

Based on the temperature observation of a kilometer-scale rail-cum-road bridge, temperature distribution characteristics of main girder steel trusses at different locations in different seasons were investigated, and the temporal and spatial temperature field characteristics of the kilometer-scale rail-cum-road bridge were obtained. Based on variation characteristics of the structure and atmospheric temperature, a comprehensive analysis of temperature field of a kilometer-scale rail-cum-road bridge was conducted. The most suitable model for accurate prediction of bridge temperature data was found by comparing three neural network prediction models. The results show that the temperature field of steel trusses have significant difference in time and space; the temperature of main girder rises more slowly than atmospheric temperature; LSTM and CNN neural networks can predict the temperature data with high accuracy.
- bridge engineering,
- steel truss,
- temperature distribution,
- temperature prediction,
- neural network

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References(12)

References

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