Fuzzy Inference Model for Railway Track Buckling Prediction

Abstract

The application of rail buckling models is often limited by uncertain information with respect to track properties, and many conventional models are poorly suited to network-wide or even regional application. Here, a methodology using fuzzy sets is presented that, when trained using buckling data can use inputs of track properties to predict the minimum buckling temperature increase for a particular track. An investigation of the impact of the size of training data and the influence of key track parameters on the minimum buckling temperature increase was conducted, and it was found that a high level of influence stems from the sleeper spacing and fastener torsional resistance parameters. The model was shown to give a low prediction error even for small dataset sizes of training data. The results of this work show the efficacy of a fuzzy sets based model when applied to track buckling prediction data, giving both a low error and rapid calculation times. The approach has potential for application for a wider array of variables, such as track geometry and vehicle dynamics, and is not limited to the study of track buckling owing to the flexibility of the fuzzy inference methodology.