Research On Rail Deformation Caused By Frost Heave For High-speed Ballast-less Railway In Seasonal Frozen Region

Frost heaves of the subgrade in regions with seasonal freezing can cause changes in the geometric dimensions of a railway track(known as track deformation),which reduces the smoothness of the track and even poses a hidden danger to train operation.Northeast China is located in a seasonally frozen area,and there are obvious frost heaves and thaw settlements every year.High-speed railways have extremely high requirements for track irregularities.Therefore,for the operation of high-speed railways,grasping the change of track deformation with temperature in the process of frost heave can not only provide a basis for the railway public works department to control the changes in the geometric dimensions of the track in the seasonal freezing area,but also greatly reduce the hidden dangers of driving safety.Aiming at the ballast-less high-speed railway,this paper uses Track Geometry Measurements,atmospheric temperature data and 13 subgrade frost heave monitoring data,with each subgrade frost heave as the object,studies the relationship between subgrade frost heave and track deformation,and determines the track deformation as Trapezoidal change process,and analyzes the characteristics of the trapezoidal change process.According to the trapezoidal process of track deformation,the relationship between the frozen depth of subgrade and the amount of frost heave was quantified,and establishes the trapezoidal function relationship of track deformation depended temperature at the position of frost heave.Based on the Bayesian change point identification method and Markov Chain Monte Carlo method proposes a method to estimate the parameters in the trapezoidal function.The established functional relationship and parameter estimation method are applied to a high-speed railway,and the errors in forecasting track deformation are analyzed using the trapezoidal relationship estimated from three years of data.The results show that the trapezoidal functional relationship between track deformation and temperature is consistent with the actual changes in the process.In the absence of maintenance operations,the p-value in fitting the predicted track deformation value and the actual value to y = x was less than0.05.This shows that the established relationship is valid and the method of estimating the functional parameters is reliable.Cluster analysis is used on the coefficients in the trapezoidal function relationship of track deformation depended temperature.Since the K-means clustering algorithm is greatly affected by the initial solution,this paper uses the K-means clustering algorithm based on Particle Swarm to implement clustering analysis.By adding the Particle Swarm algorithm,this algorithm makes the clustering algorithm have better global search ability.According to the obtained clustering results,it further shows that the functional relationship of track deformation depended temperature established in this paper must be based on the frost heave of each subgrade as the object.38 pictures,3 tables,66 references.