Evaluation of Railway Ballast Permeability Using Machine Vision–Based Degradation Analysis

Railway ballast degrades progressively as a result of accumulated traffic primarily through abrasion and particle breakage. Degraded ballast may cause reduced lateral and longitudinal stability, ineffective drainage, and excessive settlement of track structures, all of which would adversely affect the performance of ballasted track. Traditional methods of ballast degradation assessment involve time-consuming field sampling and laboratory sieve analysis; moreover, determining the level of track performance deterioration at which ballast maintenance is best considered still remains challenging. This paper investigates the permeability of railway ballast through laboratory testing and provides insight into its field drainage capacity under degraded condition using an innovative approach of field imaging. Constant head permeability tests were conducted on clean and degraded ballast samples which indicated nonlinear power-curve trends, especially for clean ballast, of unit flow amount with its hydraulic gradient. Imaging-based degradation analysis using machine vision technology was also performed on clean and degraded in-service ballast to correlate Fouling Index (FI) from laboratory sieving with Percent Degraded Segments (PDS) obtained from the recently developed image segmentation algorithm. Accordingly, a new Permeability Index (PI) is introduced in this paper to define ballast permeability in the form of a bilinear model developed from the machine vision–based ballast degradation analysis. Based on the findings of this study, a two-stage ballast cleaning process for determining the timeframe of ballasted track maintenance considering its drainage capacity is proposed.