Machine learning methods to predict ballast breakage in railway tracks

Ballast breakage can be estimated using ballast breakage index (BBI), is one of the critical parameters for assessing the performance of ballast, yet accurately predicting BBI remains challenging. In this study predictive models for BBI under repetitive loading were developed using machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). This study utilizes comprehensive 20-year dataset, incorporating input parameters such as loading characteristics, particle size distribution, angularity, initial physical state, and the stress state applied by the test apparatus. While examining the performance based on RMSE, MAPE and R ², ANN model marginally outperformed SVM and RF models. The results demonstrate the capability of ANN in elucidating nonlinear relationships, providing accurate breakage predictions without requiring extensive laboratory testing. This study highlights the potential of advanced soft-computing tools for assessing ballast performance in railway tracks, bridging the gap between laboratory insights and field applications.