Author Type

Graduate Student

Date of Award

Fall 11-30-2025

Document Type

Thesis

Publication Status

Version of Record

Submission Date

December 2025

Department

Civil, Environmental and Geomatics Engineering

Degree Name

Master of Science (MS)

Thesis/Dissertation Advisor [Chair]

M. Arockiasamy

Abstract

Railroad ballast performance is affected by fouling materials that contain plastic and granular properties. The characterization of fouling materials are important factors in assessing track performance and maintenance requirements. This study presents the

hyperspectral reflectance characteristics of the degraded field railroad ballast under varying fouling contents (FCs) and water contents (WCs). The study involves experiments including material selections, preparation of railroad ballast samples, fouling materials, Hyperspectral Imaging (HSI) data acquisition and calibration, methodology for regression using Gaussian Process Regression and Artificial Neural Network (ANN) Regression, discussions, and conclusions. Gaussian and ANN Regression models are applied to generate the actual and predicted reflectances at VNIR wavelength range (400-1000 nm) to the degraded field ballast samples under varying FCs with Moderately Clean (5% FC), Moderately Fouled (15% FC), and Fouled (25% FC) with varying WCs of 0%, 20%, 40%, 60% and 80%. The prediction generalizes more effectively and accounts for varying water and fouling scenarios. The study presents discussions on a prospective framework of reusing screened and recycled degraded ballast with fresh ballast in railroad infrastructure and sustainability. Blending of degraded ballast with virgin ballast will contribute to a sustainable and cost-effective strategy in railroad track maintenance.

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