Abstract:

The traditional thermodynamics-based analysis methods of the temperature fields of concrete structuresare characterized by too many assumptions and excessive energy consumption in calculation,and with these methodsit is difficult to obtain parameter values.In order to know more about the vertical temperature gradient distributionin track plate,a multi-layer artificial neural network based on error back propagation is established by using thelong-term observation data of the temperature field of track plate.Then,the meteorological parameters easy to beobtained 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,thesunshine hours and the wind speed on the vertical temperature gradient of track plate are discussed.The resultsshow that (1) when the artificial neural network of a 4-16-1 structure is established with the daily temperaturedifference,the daily average wind speed and the sunshine hours as the training samples,the network has a strongrobustness 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 meteorologicalparameter and the vertical temperature difference of track plate; (3) generally speaking,the stronger the sunshineis 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.

Key words: track engineering, unballasted track, track plate, temperature fields, artificial neural networks