Abstract

The presence of fouling contamination in railroad ballast is a growing concern that causes deterioration, poor drainage functionality, and early failure of railroad components. Evaluation and characterization of fouled ballast are crucial for determining and monitoring fouling content (FC) and moisture content (MC) in ballast. However, human inspection, destructive testing, field sampling, and most available nondestructive evaluation methods (NDE) for inspection purposes pose safety risks, undermine the stability of the railroad substructure, or provide limited information about the condition of the ballast. This study presents the results of the spectral characterization of laboratory ballast samples using hyperspectral imaging (HSI) sensor. Nineteen (19) clean, dry-fouled, and wet-fouled samples were prepared and evaluated using HSI. The reflectance of ballast samples with different levels of MC and FC was determined in Visible-Near infrared (VNIR) and Near-infrared (NIR) wavelength range from 400 nm to 1700 nm. Results showed that; the presence of trapped water in ballast pores and free water in ballast voids reduced the reflectance; an increase in the dry fouling content increased the reflectance, and MC in wet-fouled ballast showed a nonlinear relationship with the reflectance or absorbance property. The presence of water was prominent in wavelengths of 1350 nm to 1550 nm. A second-order polynomial fit was adopted for the absorbance and reflectance features plotted against the MC, and the coefficient of determination (R2) values were determined. The R2 for the absorbance and reflectance plots between 900 nm and 1700 nm were 0.7747 and 0.8336 in the sample with 5% FC and 0.8790 and 0.9278 with 15% FC, respectively. For the 1350 nm to 1500 nm wavelength range, the R2 increased by 27% and 13%, respectively, for 5% FC and then 5% and 0%, respectively, for 15% FC. This study shows that absorbance features are more prominent than the reflectance features for determining MC in fouled ballast, with high prospects of detecting, monitoring, and quantifying MC and FC contamination using the HSI method.

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