Prediction of Rail Wear Under Different Railway Track Geometries Using Artificial Neural Networks

Abstract

The geometry of the railway track affects rail wear significantly. If the rail wear can be predicted and considered during the alignment design phase, the problems it causes can be mitigated at the source by optimizing the values and combinations of railway track geometry parameters. However, the relationship between railway track geometry and rail wear remains unclear. It is hard to acquire rail wear data for different alignments with varying geometric parameters during the alignment design phase. This study develops a PSO-ANN model to establish the mapping relationship between railway track geometry and rail wear, enabling prediction of rail wear based on track geometry parameters. The model achieves prediction accuracies of 96.70% for inner rail wear and 98.13% for outer rail wear. Compared with the conventional ANN model, the PSO-ANN model reduces the prediction errors by 22.54% for inner rail wear and 55.69% for outer rail wear. Sobol sensitivity analysis is conducted to analyze the influence of the track geometry parameters on rail wear, revealing that inner rail wear is mainly affected by curve radius, transition curve length, and superelevation, while outer rail wear is predominantly influenced by curve radius.