With the increasing service time of high-speed railways,various track defects continuously emerge,particularly severe structural damage caused by elevated temperatures,which would affect the service conditions of ballastless track.Establishing a reliable early warning model for track service conditions and developing a method for identifying such conditions are among the essential measures for ensuring the operation safety of railway transport.For the purpose of this study,an online monitoring system was set up along a railway for on-line monitoring of the track environment and slab temperature,and the value at the probability of 0.3%for temperature gradient occurrence was taken as the threshold for slab temperature gradient warnings.Then an AI algorithm decision tree was employed to construct the early warning model for track structure service conditions.The findings indicate that:(1)The decision tree model can effectively predict track structure service conditions,enabling the determination of slab temperature gradient quality levels.(2)The accuracy of early warning for track service conditions is strongly correlated with the quantity of samples in the database,and enhancing the richness of monitoring sample database will enable a more comprehensive and precise prediction of abnormal track structure conditions,ensuring timely track maintenance and repair.
high-speed railwayservice conditionsdecision treetemperature gradientearly warning model
NETL
NSTL
万方数据
