华南理工大学学报（自然科学版）2025，Vol.53Issue(6)：25-33,9.DOI:10.12141/j.issn.1000-565X.240200
基于机器学习的高速铁路斜拉桥钢箱梁温度模式研究
Research on Temperature Model of Steel Box Girder of High-Speed Railway Cable-Stayed Bridge Based on Machine Learning
摘要
Abstract
To investigate the temperature patterns of steel box girders in long-span cable-stayed bridges on high-speed railways,this study utilized measured temperature data from the Yuxi River Bridge on the Shangqiu-Hefei-Hangzhou High-Speed Railway,along with database resources.By employing machine learning techniques,the re-search explored the influence of various meteorological factors on the temperature behavior of steel box girders,as well as the temporal and spatial distribution characteristics of the temperature field.By establishing machine learning mo-dels that map various meteorological factors to the uniform temperature of the steel box girder,the superiority,inferio-rity,and applicability of each model were analyzed,and the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder was obtained.A comprehensive study on the vertical distribution pattern of the temperature of the steel box girder was conducted using machine learning methods and exponential fitting.The results show that the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder from high to low is:air temperature,cumulative radiation,air pressure,humidity,radiation intensity,wind direc-tion,horizontal visibility,wind speed,and precipitation,with the temperature importance far exceeding other meteoro-logical factors.Among them,the atmospheric temperature 2 to 3 hours ago has the greatest impact on the uniform tem-perature of the steel box girder,reflecting a lag of 2 to 3 hours in the impact of atmospheric temperature changes on the uniform temperature of the steel box girder.Neural networks,random forests,and XGBoost models can all accurately predict the uniform temperature of the steel box girder,with the neural network model performing better overall.The negative temperature gradient in the steel box girder exhibits lower sensitivity to meteorological factors and is more strongly correlated with the internal heat transfer characteristics of the structure itself.The exponential function can accurately fit the vertical distribution of the maximum positive temperature gradient in steel box girders,with its pa-rameters determinable through machine learning methods.Each parameter holds distinct physical significance.The research findings provide valuable reference for predicting temperature fields and understanding distribution patterns in the steel box girders of long-span cable-stayed bridges on high-speed railways.关键词
高速铁路/钢箱梁/温度模式/机器学习/气象因素Key words
high-speed railway/steel box girder/temperature mode/machine learning/meteorological factor分类
交通工程引用本文复制引用
刘文硕,钟明锋,周博,吕方舟..基于机器学习的高速铁路斜拉桥钢箱梁温度模式研究[J].华南理工大学学报（自然科学版）,2025,53(6):25-33,9.基金项目
国家自然科学基金项目(52278234) （52278234）
中国国家铁路集团有限公司科技研究开发计划课题(L2021G007)Supported by the National Natural Science Foundation of China(52278234) （L2021G007）
