Data-driven clay-fouled ballast permeability assessment using analytical-numerical and machine learning approaches

Abstract

The occurrence of ballast contamination or fouling frequently results in a sudden decline in the capacity of railway ballasted tracks. Considering the various sources of ballast fouling, clay is the most severe one for causing a drastic reduction in the drainage capacity of the ballast layer. In the current study, we utilized a large-scale flume test to measure the water height along the cross-section of the clay-fouled ballast. Subsequently, an analytical–numerical (A-N) approach was developed to simulate the movement of water through porous media under steady-state conditions, while also considering the flow regime. This A-N approach was validated using the results of flume tests. Finally, the validated A-N approach was employed to generate a dataset and develop machine learning models for predicting water height. The characterized machine learning models included random forest regression (RFR), support vector machine (SVM), and extreme gradient boosting (XGBoost). Various variables, such as ballast gradation, fouling ratio, bed slope, rainfall rate, and water height on the side ditch, were incorporated into the machine learning models to reveal the contribution of each individual variable. Results show that for clean ballast, the incorporation of a nonlinear model between flow velocity and hydraulic gradient in the A-N approach is crucial to properly estimate the experimental measurements. However, a comparison of the water height measured via the flume test and the water level estimated based on the A-N approach confirms the suitability of the linear model, i.e., Darcy's law, for the water flow regime through clay-fouled ballast. According to the machine learning results, particularly those from the XGBoost model, which was characterized as the elite model, the rainfall rate and the fouling index emerged as the most influential variables affecting the water height in the clay-fouled ballast layer of the railway track.