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Lite RGB-based measurement method for ballast fouling index prediction through subsampling

Measurement, ISSN: 0263-2241, Vol: 234, Page: 114813
2024
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Article Description

Ballasted track is the major form of freight railroads. Despite various requirements for material selection, ballast will degrade due to wear and the infiltration of fine materials. These fine particles will cause ballast fouling and degraded track performance. The fouling condition is directly related to both ballast performance and track maintenance planning. It can be quantitatively assessed using the Fouling Index (FI), calculated as the sum of the percentages of materials passing through No. 4 (4.75 mm) and No. 200 (75 µm) sieves. Traditional fouling assessment methods rely on labor-intensive sieve analysis. While non-destructive techniques such as Ground Penetration Radar, Impulse Response, Surface Wave, and SmartRock offer alternatives, they require specialized equipment and skilled technicians for accurate interpretation. Deep learning models have also been employed, trying to assess ballast fouling conditions by segmenting particles. However, these models struggle to account for fine materials. Recently, the FI was found to be linearly correlated with the variance of RGB (Red, Green, Blue) intensities in fouled ballast images, and this correlation could be used to assess ballast fouling conditions. Improving upon the previously developed FI prediction model and making it more practical, this study proposes an enhanced and lite RGB-based model that predicts the FI through a simple random subsampling approach. The optimal subsample size is determined both experimentally and analytically. Based on the results obtained from this study, the correlation between FI and the variance of RGB intensities in fouled ballast images is further confirmed. The proposed lite model can achieve the same accuracy in FI prediction but requires fewer than 1 % of the data points needed by previous methods, which demonstrates its promising potential for ballast fouling condition assessment practice.

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