基于BP神经网络的混凝土箱梁最大温度梯度预测OA北大核心CSTPCD
Prediction of concrete box-girder maximum temperature gradient based on BP neural network
混凝土箱梁受到太阳辐射、大气温度波动等多种气象因素的综合作用,结构内部会产生显著的非均匀温度分布.截面内温度梯度可能会导致桥梁结构产生过大的温度应力与温度变形,影响桥梁结构的安全性和耐久性.本文旨在探究气象因素对混凝土箱梁温度场的影响机理,并提出一种能精确预测中国多区域混凝土箱梁截面最大温度梯度的方法.首先建立了日照条件下混凝土箱梁温度场计算模型,将2 a以上气象资料作为输入条件,对多个地区混凝土箱梁温度场长期变化进行了仿真模拟,并对混凝土箱梁截面温度梯度的长期变化趋势进行了分析.然后利用主成分分析(PCA)确定了混凝土箱梁截面最大温度梯度预测模型所需的输入参数.最后利用遗传算法优化的BP神经网络建立预测混凝土箱梁竖向、横向温度梯度的网络模型,并与混凝土箱梁截面温度梯度进行比较.结果分析表明BP神经网络模型可以精确地预测混凝土箱梁最大温度梯度,预测值平均绝对误差(AAE)均小于0.9℃,均方根误差(RMSE)均小于1.2℃,决定系数(R2)均大于0.9.基于当地气象条件,本文利用经典的BP神经网络模型所建立的预测模型对中国不同地区的混凝土箱梁截面最大温度梯度均能给出准确的预测,为混凝土箱梁设计和施工阶段的最大温度梯度的计算提供一种高效的方法.
The concrete box-girder is subjected to the combined effects of various time-varying climatic parameters,such as solar radiation and atmospheric temperature fluctuation,which may lead to significant non-uniform temperature field in the bridge structure.Temperature gradients along the cross-section may lead to excessive temperature stresses and temperature deformations,thereby deteriorating the safety and durability of bridge structure.The purpose of this paper was to explore the influence mechanism of climatic parameters on the temperature field of concrete box-girder and propose a novel method for predicting the extreme temperature gradient in concrete box-girder.First,a numerical model was established to simulate the temperature field in concrete box-girder under insolation conditions.The long-term variation of concrete box girder temperature field in respect of different regions in China was simulated using meteorological data for more than 2 years as input conditions.The long-term variation trend of the cross-sectional maximum temperature gradient of concrete box girders was also analyzed.Then,the principal component analysis(PCA)was conducted to determine the input parameters for the prediction of maximum temperature gradient of concrete box girders.Finally,the BP neural network optimized by genetic algorithm was established to predict the vertical and transverse temperature gradients of concrete box girders.The results were compared with the maximum temperature gradients of concrete box girders in respect of different regions in China.The analytical results indicate that the BP neural network models have high accuracy for predicting the maximum temperature gradient in concrete box-girder,with the absolute average errors(AAE)less than 0.8℃,the root-mean-square errors(RMSE)less than 1.2℃,and the coefficient of determination(R2)greater than 0.9.Based on local meteorological conditions,BP neural network prediction model can be used to determine the maximum temperature gradient of concrete box girders in different regions of China.The developed prediction method in this paper can provide an efficient tool for calculating the maximum temperature gradient for the design and construction phases of the concrete box-girder.
王凯;张勇;刘建磊;何旭辉;蔡陈之;黄石基
中国铁道科学研究院集团有限公司 铁道建筑研究所,北京 100081中南大学 土木工程学院,湖南 长沙 410075
交通运输
桥梁工程日照温度作用BP神经网络混凝土箱梁温度梯度
bridge engineeringsolar thermal actionBP neural networkconcrete box girdertemperature gradient
《铁道科学与工程学报》 2024 (002)
837-850 / 14
中国铁道科学研究院集团有限公司基金资助项目(2022YJ174)
评论