A quantitative evaluation of the impact of railway track characteristics on the magnitude of dynamic loads
Creators: Behnia, Danial
Publisher: name: University of Alberta Library
Year: 2024

<p>Canada has one of the largest railway networks globally, with more than 48,000 route kilometers of track. The Canadian railway network is primarily a heavy freight railway network that highlights the importance of the railway industry for the Canadian economy and the requirement for its fast and safe operation. The substantial intensity of dynamic loads can damage the railway track, such as rail breaks and failures in track components. In Canada, the leading causes of derailments are rail breaks and rail component failures. In light of continuing rail failures, it is worth revisiting the understanding of the magnitude of loads that the rail is subjected to.<br>
The current literature needs to address the relationship between dynamic loads and railway track structures, particularly in understanding the magnitude of these loads. Track geometry and stiffness changes are two primary factors contributing to the variability and escalation of dynamic loads. A significant limitation of existing understanding stems from data predominantly gathered under constant track conditions at instrumented sections, focusing on numerous wheel loads without considering variable conditions. As dynamic load factor (ϕ) values find widespread application in the analysis and design of railway tracks, this research centers on evaluating this factor. The ϕ values play a significant role in track structure analysis, design, and selection of rail steel and cross-sectional characteristics (weight).<br>
An extensive study was conducted on a track section of over 340 km in the Canadian Prairies, operated by a North American Class 1 freight railway. This study utilized a train-mounted system comprising the Instrumented Wheelset (IWS) and MRail measurement systems. In contrast to previous investigations that focused on specific track sections (i.e., instrumented section), the measurements used in this research primarily result from variations in track characteristics.<br>
Evaluating the impact of observable track characteristics indicates a noteworthy influence, resulting in dynamic load ranges and ϕ values for the track that exceed those typically estimated through conventional means. This augmentation is particularly pronounced for non-tangent track segments, which include curves, switches, crossings, and bridges. The impact of track surface longitudinal level (in terms of rail profile) on ϕ values revealed a more pronounced effect of the longitudinal level of the rail vertical deviations, train speed, and track conditions on the magnitude of dynamic loads in non-tangent sections compared to tangent sections. Track surface vertical deviations can lead to a 15-36% increase in dynamic load magnitudes within the typical range of rail profile changes (0-20 mm), diverging from prevalent North American railway design practices. <br>
The assessment of subgrade track stiffness (VTDsub) conditions highlighted the significance of the average track conditions range (3.1-4.4 mm), demonstrating a critical association with observable increases in ϕ values. This association can increase dynamic load magnitudes by 20-30%. In curves, heightened subgrade vertical track deflection (VTDsub) conditions, particularly in tracks of average to poor quality, may lead to increased dynamic loads on the lower rail compared to good tracks. The investigation into the effects of transition directions on dynamic load magnitudes indicated that transitions from soft-to-stiff conditions amplify dynamic loads, while transitions from stiff-to-soft conditions attenuate them. Notably, soft-to-stiff transitions exhibited ϕ values approximately 10% higher than those observed in stiff-to-soft transitions. This analysis also highlighted that subgrade track conditions contribute to the effectiveness of the influence of transition direction, potentially diminishing the discrepancy between the two transition scenarios. These quantitative insights pave the way for proactively optimizing maintenance schedules to prevent rail breaks and failures.</p>