Probabilistic review of wheel profiles based on hollow tread in the U.S. heavy haul rail network

Lee, J., M.S. Dersch, A. de O. Lima and J.R. Edwards. 2023. Probabilistic review of wheel profiles based on hollow tread in the U.S. heavy haul rail network. Journal of Rail and Rapid Transit. 237 (4): 508-516. doi:10.1177/09544097221122030.


Wheel profile is one of the most critical factors that governs the dynamic interaction between railcar wheels and the rail. This paper quantifies and analyzes 15,000 wheel profiles that were randomly selected from a dataset obtained from wayside wheel profile measurement systems from railcars that are in unrestricted interchange in the North American Class I railroad network. Mean dimensions for each of the four critical wheel tread parameters were as follows; 30.5 mm (1.202 in.) for flange height, 35.4 mm (1.395 in.) for flange thickness, 0.483 mm (0.019 in.) for hollow tread, and 37.8 mm (1.488 in.) for rim thickness. Further evaluation focused on the magnitude of hollow tread, one of the important factors that determines wheel rail contact location, contact patch size, and dynamic interaction of the railcar and track. Correlative analysis revealed that hollow tread is linearly related to flange height, flange thickness, and rim thickness. Given this relationship, we categorized wheel profile data into characteristic bins of wheel profiles, and a new classification system based on hollow tread was proposed that should be considered when designing track infrastructure components (e.g., turnout frogs). Combinations of parameters were derived and organized into five wheel-profile classification ranges, based on “most likely” to “least likely” profiles that might be encountered. For the five classifications (Type A to Type E), a range of hollow tread from 0 mm to 5 mm (interval of 1 mm) and corresponding mean values for the three other parameters were applied. This probabilistic analysis and classification of wheel profiles into categories enables better understanding of current North American wheel conditions. Additionally, these data and the proposed analysis method can be leveraged to further optimize wheel profiles and track components, identify relationships with rates of rail surface defects and track degradation, and prioritize maintenance.