Track substructure performance monitoring using data collected from smartgrid

The structural soundness of a conventional track is often assessed by a single parameter called “track modulus.” Track modulus is a measure of the vertical deflection of the track's components beneath the rail. However, defining the track substructure's condition based only on track modulus can be misleading, as combinations of different ballast and subgrade conditions might yield the same “track modulus” measurement. For railroaders to be able to make an informed decision on the right maintenance strategy when a low track modulus is present, identification of the defective component between ballast or soil is critical. The railroad industry, therefore, needs an inspection technique that independently highlights the condition of the ballast and the subgrade. Addressing this challenge, our research has devised a system that helps identify the ballast and subgrade condition without disrupting normal train operations. The proposed system is a significant advancement over conventionally employed inspection methods. This new system, called the Smartgrid, uses sensors and strain gauges embedded in a geogrid sheet placed in the ballast-subgrade interface to record data on the stress-strain relationship at this plane. This data is then analyzed using supervised machine-learning techniques such as Logistic Regression and the Support Vector Machine. The ultimate objective of the proposed Smartgrid system is to arm the railroader with the right information on the condition of the two major components of the substructure and facilitate efficient maintenance. The Smartgrid, which has been tested under various conditions, promises a substantial improvement in inspection of the rail substructure.