Railway track condition change detection: A data-driven approach to track geometry and component degradation modeling

The railroad track system is a critical transportation asset that is responsible for transferring wheel loads from rolling stock to the roadbed. To ensure safe and efficient operations, U.S. Class I railroads conduct frequent inspections, generating a substantial amount of track health data. With the growing adoption of data science tools, these datasets offer new opportunities for more sophisticated maintenance and safety analyses. While many railroads leverage data trending for geometry prediction, they often overlook the impact of evolving component condition and unrecorded maintenance, which are crucial for accurate degradation modeling. This thesis evaluates the relationship between track geometry degradation and ballast profiles across curved and tangent track segments using data from a primary corridor on a U.S. Class I railroad. A stochastic approach revealed a significant correlation between degradation, initial profile conditions, and the Ballast Health Index (BHI), with faster deterioration observed in areas with poor initial geometry and high BHI values. Additionally, cross-correlation was employed to address the challenge of identifying unrecorded maintenance activities, proving effective in detecting track changes and improving data quality metrics for linear degradation models. These findings provide a quantifiable method for assessing degradation under varying conditions, enhancing maintenance prioritization and demonstrating that cross-correlation can identify maintenance events and support track monitoring and maintenance planning.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Venancio Da Silva Ramos, Jose Augusto

Contributors dc:contributor

• Edwards, John Riley

• Railroad
• Track Degradation
• Track Geometry
• Track Components
• Big Data
• Ballast.

Statement dc:rights

• Copyright 2024 Jose Augusto Venancio da Silva Ramos

Language dc:language

eng, en

Handle dc:identifier

https://hdl.handle.net/2142/127517

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F127517