Abstract
Continuous Welded Rail (CWR) improves ride quality and reduces maintenance costs but introduces significant challenges in managing axial rail stresses, which can lead to lateral buckling and derailments. Buckling risk is influenced by multiple track parameters, including lateral strength, torsional resistance, misalignment, curvature, and longitudinal stiffness. Existing tools such as CWR-SAFE assess buckle risk at discrete locations using dynamic buckling theory but have limited scalability due to uncertainty in track characteristics and reliance on localized data. This research builds upon the earlier development of the Illinois Buckle Risk Model (IBRM) to enable segment-level risk assessment by aggregating high-resolution data from modern inspection systems. Aggregation rules were developed for buckled and adjoining regions based on experimentally validated dynamic buckling theory and data from previous field experiments. The IBRM methodology defines representative lengths for buckled and adjoining regions, computes conservative estimates of lateral strength and torsional resistance, and integrates longitudinal stiffness using weighted subregion analysis. The proposed approach was applied to a Class I railroad subdivision using data from autonomous geometry and 3D laser scanning systems. Results highlight the influence of ballast condition, rail anchor patterns, and spiking configurations on track strength and the output metric of Buckling Safety Margin (BSM). Class I railroad case studies demonstrate how variations in track parameters affect the allowable temperature increase for different track sections, including quantification of the impact of track surfacing (tamping) on buckle risk. The IBRM methodology relies on existing models of buckle risk mechanics to provides a novel, scalable framework to identify and mitigate buckled-track derailment risks and prioritize track maintenance to strengthen the track system against buckling. Its flexible, data-driven structure enables continuous refinement and improved predictive accuracy as new track condition and strength data become available.