Advancing railway track health monitoring: Integrating GPR, InSAR and machine learning for enhanced asset management

Railway track health monitoring and maintenance are crucial stages in railway asset management, aiming to enhance the train operation quality and service life. For this aim, various inspection means (using diverse non-destructive testing techniques) have been applied, however, these means are mostly not able to monitor whole railway track...

Machine learning approach to railway ballast degradation prognosis considering crumb rubber modification and parent rock strength

Parent rock strength and crumb rubber modification are two critical mechanical parameters that significantly decide the ballast layer degradation subjected to train dynamic loading. Using machine learning to predict ballast degradation considering these two parameters is helpful for deciding ballasted track maintenance cycle. In the current...

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

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...