摘要
传统基于热力学的混凝土结构温度场分析方法存在着假设过多、参数取值困难、计算能耗过大的缺点．为研究轨道板竖向温度梯度分布规律，结合轨道板温度场的长期观测数据，建立误差反向传播的多层人工神经网络，选用易于取得的气象参数作为训练样本，对轨道板竖向温度梯度进行预测，并采用实测数据验证其准确性．在此基础上研究了日温差、日照时数和风速对轨道板竖向温度梯度的影响规律．研究表明：采用日温差、日平均风速和日照时数3种气象参数作为训练样本，所建立的4-16-1结构人工神经网络预测结果最大误差为2．0℃，平均相对误差为0．38％，可准确预测轨道板竖向温度梯度，且具有较好的鲁棒性；各气象参数与轨道板竖向温差之间存在着复杂的非线性映射关系，总体而言，日照越强，风速越高，轨道板竖向温度梯度越大；对我国中部地区而言，轨道板竖向温度梯度为-2～10℃．
The traditional thermodynamics-based analysis methods of the temperature fields of concrete structures are characterized by too many assumptions and excessive energy consumption in calculation, and with these methods it is difficult to obtain parameter values. In order to know more about the vertical temperature gradient distribution in track plate, a multi-layer artificial neural network based on error back propagation is established by using the long-term observation data of the temperature field of track plate. Then, the meteorological parameters easy to be obtained are used as the training samples to predict the vertical temperature gradient of track plate, and the predic- tion accuracy is verified by the measured data. On this basis, the influences of the daily temperature difference, the sunshine hours and the wind speed on the vertical temperature gradient of track plate are discussed. The results show that （ 1 ） when the artificial neural network of a 4-16-1 structure is established with the daily temperature difference, the daily average wind speed and the sunshine hours as the training samples, the network has a strong robustness and can accurately predict the vertical temperature gradient of track plate with a maximum error of 2.0℃ and an average relative error of 0.38% ; （2） there is a complex nonlinear relationship between each meteorological parameter and the vertical temperature difference of track plate ; （3） generally speaking, the stronger the sunshine is and the higher the wind speed is, and the greater the vertical temperature gradient of track plate will be; and （4） the vertical temperature gradient of track plate is -2- 10℃ in the central region of China.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第12期9-13,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
高速铁路基础研究联合基金重点支持项目(U1334203)
中国博士后科学基金资助项目(2014M552158)
中国铁路总公司科技研究开发计划课题(2014T003-D)
关键词
轨道工程
无砟轨道
轨道板
温度场
人工神经网络
track engineering
unballasted track
track plate
temperature fields
artificial neural networks
