Development and application of the Illinois Buckle Risk Model (IBRM) using multi-source track condition data

The widespread use of Continuously Welded Rail (CWR) has provided many benefits to the rail industry by reducing the stress state of track infrastructure. One drawback of CWR is its higher propensity to buckle compared to jointed track due to the lack of locations to accommodate axial thermal expansion. Data from the Federal Railroad Administration (FRA) accident database reveal that buckled-track derailments have been a persistent safety concern for U.S. railroads. The FRA initiated an extensive research program in the 1980s to develop and experimentally verify a dynamic buckling theory which culminated in the development of the CWR-SAFE software. The Buckle module of CWR-SAFE accepts quantitative track condition input parameters and assesses the buckling risk of track in terms of its Buckling Safety Margin (BSM). Since the development of CWR-SAFE, there have been notable advancements in track inspection technologies capable of providing high-resolution track health data. The Illinois Buckle Risk Model (IBRM) leverages the outputs from three-dimensional machine vision and track geometry measurements systems into the CWR-SAFE environment to perform buckle risk assessment at an individual crosstie resolution. IBRM uses results from field and laboratory experiments to calibrate inspection system outputs into quantified inputs for CWR-SAFE. The application of IBRM is demonstrated using data collected from a Class I railroad subdivision. IBRM 2.0 extends this framework by aggregating crosstie-level outputs over track segments that correspond to typical track buckle lengths. Critical track strength parameters such as lateral strength, longitudinal stiffness, and torsional resistance are averaged or normalized across these segments to produce a more representative assessment of overall buckle behavior. The use of IBRM 2.0 is also demonstrated using Class I subdivision data and these results are compared to earlier results from IBRM. Two notable features of IBRM are its flexibility and scalability. Localized track conditions can be simulated by adjusting track strength and condition parameters. This adaptability supports the creation of a positive feedback loop, enabling iterative model refinement and improved predictive performance. Furthermore, IBRM can process subdivision-level data within a nominal computational timeframe, making it scalable for network-wide applications. IBRM can efficiently handle large volumes of data from track inspection systems, facilitating comprehensive and proactive track strength assessment across large rail networks.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Thakur, Neeraj

Contributors dc:contributor

• Edwards, John Riley

• Track Buckles
• Track Lateral Strength
• Sun Kink
• Buckle Risk
• Track Maintenance Prioritization
• Machine Vision Application

Statement dc:rights

• Copyright 2025 Neeraj Thakur

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/130065

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F130065