Analysis and prediction of temperature distribution of steel trusses for kilometer-scale rail-cum-road bridge
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摘要: 基于某千米级公铁两用桥温度观测结果，研究主梁钢桁架不同位置在不同季节下的温度分布特征，得到千米级公铁两用桥的时空温度场特性。基于结构和大气温度变化特性，对千米级公铁两用桥温度场进行全面分析，并通过3种神经网络预测模型对比找出最适合桥梁温度数据精准预测的模型。结果表明：钢桁架的温度场存在显著的时间及空间差异性，主梁温度明显滞后于大气温度，LSTM和CNN神经网络可以对温度数据进行较高精度的预测。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.
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Key words:
- bridge engineering /
- steel truss /
- temperature distribution /
- temperature prediction /
- neural network
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图 1 桥梁跨中横断面温度传感器布置图
Figure 1. Layout of temperature sensor in cross-section of bridge span
图 4 钢桁架局部与大气温度滞后关系
Figure 4. Hysteresis relation between local steel truss temperature and atmospheric temperature
图 5 钢桁架局部与大气温度温差关系
Figure 5. Difference relationship between steel truss and atmospheric temperature
表 1 夏季和冬季钢桁架顶板测点温度日统计参数
Table 1. Daily statistical parameters of temperatures at the top plate of steel truss in summer and winter
℃ 季节 传感器 最大值 最小值 平均值 温差 夏季 WD-08-19 50.2 27.0 36.1 23.2 WD-08-21 49.5 28.2 37.9 21.3 WD-08-23 47.0 27.1 37.5 19.9 WD-08-24 42.9 25.4 33.7 17.5 冬季 WD-08-19 19.0 6.2 10.8 12.8 WD-08-21 17.0 6.2 10.2 10.8 WD-08-23 14.3 4.6 9.4 9.7 WD-08-24 12.6 4.5 8.5 8.1 
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表 2 夏季和冬季钢桁架底板测点温度日统计参数
Table 2. Daily statistical parameters of temperatures at the bottom plate of steel truss in summer and winter
℃ 季节 传感器 最大值 最小值 平均值 温差 夏季 WD-08-14 35.2 30.0 32.6 2.6 WD-08-12 35.0 29.6 32.3 2.7 WD-08-07 34.8 29.5 32.1 2.7 WD-08-09 34.1 29.2 31.7 2.4 冬季 WD-08-14 35.2 30.0 32.6 2.6 WD-08-12 35.0 29.6 32.3 2.7 WD-08-07 34.8 29.5 32.1 2.7 WD-08-09 34.1 29.2 31.7 2.4
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表 3 3种模型预测精度
Table 3. Predictive accuracy of three models
预测模型 MAE值 RMSE值 LSTM神经网络 0.015 0.026 CNN 0.016 0.028 BPNN 0.066 0.114
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