Using large datasets for finding the correlation between the rate of track settlement and changes in geometry indices

Abstract

The allowable speed limit of railroad tracks is dependent upon maintaining track conditions that will ensure the safety and ride quality. Settlement of railway tracks amplifies the dynamic loads of trains, which results in high maintenance costs and poor ride quality. Direct settlement measurement in the field is costly due to the requirement for installing many gauges along the many kilometers of track. Different datasets from the US passenger tracks consisting of more than 300 million elements were employed to propose a novel method for measuring the rate of settlement from the reported space curve. In this method, Cross Power Spectral Density (CPSD) was employed for determining the quality of data and choosing reliable observations for calculating the settlement. After calculating the rate of settlement for two railway tracks with a total length of 287 miles, its correlation with changes in different geometry indices was investigated. Equations were proposed for predicting the rate of settlement by changes in geometry indices. Results indicate that standard deviation index has the best correlation with the rate of settlement. Track quality index (TQI) is sensitive to high-frequency noise and does not yield highly accurate results when there is noise in the dataset.