Integration of Digital Image Correlation and finite element analysis for enhanced rail track modeling and modulus estimation

Abstract

Understanding vertical rail deflection is essential for evaluating track support conditions to minimize derailments and optimize maintenance strategies. The objective of this study is to integrate multi-point Digital Image Correlation (DIC) data from vertical rail deflections for developing more refined track models that reflect in-situ conditions. Utilizing a field-deployable DIC monitoring system, rail deflection profiles were captured from two field case studies with varying substructure characteristics to estimate rail deflections and overall track moduli. The field data were used to calibrate finite element (FE) models in ABAQUS. A parametric study was conducted to evaluate the effects of subgrade modulus, ballast modulus, ballast thickness, and subgrade depth on vertical track deflection. Results demonstrated a nonlinear but consistent decrease in rail deflection with increasing subgrade and ballast modulus. Ballast thickness and subgrade depth showed complex influences due to their role in modifying load path flexibility. A minimum ballast modulus of 25,000 psi was found necessary to maintain deflections within AREMA guidelines. The results also validated the Pasternak foundation model as a better representation of the observed track deflection behavior. This integrated approach enables more precise estimation of track stiffness, especially in transition zones, and supports the development of refined, data-driven models for rail infrastructure assessment. These findings can inform targeted maintenance, refined modeling approaches, and data-driven infrastructure assessments.