Predicting Ballast Fouling Conditions with the Gaussian Mixture Model

Abstract

Railroad ballast is typically comprised of only large granular particles. However, the degradation of fresh ballast and the arrival of foreign fines result in ballast fouling. Compared with fresh ballast, fouled ballast exhibits reduced resilience and compromised drainage capabilities. To optimize track performance, maintenance activities for the ballast are frequently scheduled based on the fouling severity. An accurate assessment of ballast fouling conditions can enhance maintenance efficiency and reduce costs. Over the years, while many ballast fouling evaluation methods have been developed, their widespread adoption has been hindered by system costs and implementation challenges. This study aims to address this by developing an affordable and easily implemented approach to estimating ballast fouling conditions using the Gaussian Mixture Model (GMM). Initially, images of fouled ballast are characterized by fitting the distributions of each RGB (Red, Green, Blue) channel. Subsequently, two mathematical methods, expectation-maximization and point estimation, are employed to solve the GMM parameters. These derived GMM parameters are then used to backcalculate the sample parameters, facilitating the estimation of ballast fouling conditions. The results of this study reveal a close alignment between the ballast fouling conditions backcalculated with the GMM and those quantified through laboratory sieving analysis. This study thus presents a promising path forward, using images captured from cost-effective cameras to estimate ballast fouling conditions with minimal computational expense.