Quantification of Loading Environment and Flexural Demand of Prestressed Concrete Crossties under Shared Corridor Operating Conditions

Abstract

Optimization of the design of railway infrastructure and its components requires a comprehensive understanding of the loading demands that are expected. Currently, many design guidelines for track components use historical wheel loads and calculate bending moments based on broad assumptions. However, tools are available to accurately quantify and characterize the variability each load has on a particular component. This is particularly important in shared use rail corridors where higher speed passenger services operate on the same infrastructure as heavy axle load (HAL) freight trains. Each traffic type generates unique demand variabilities, and these should be incorporated into a holistic approach to optimized track design. Therefore, researchers at the University of Illinois at Urbana-Champaign (UIUC) are conducting field research aimed at the characterization of field conditions on Amtrak’s Northeast Corridor through the use of wheel impact load detector (WILD) data and concrete crosstie surface strain gauges. Results from this experimentation show high variability of loads resulting from varied types of train operations and significant differences in impact load ratios. Finally, laboratory measured flexural capacity for the concrete crossties showed a conservative design with a potential margin of improvement in reduction of residual capacity (i.e., factor of safety).

Use of Field Flexural Demand Data for Reliability-Based Analysis and Design of Concrete Railroad Sleepers

“…To further improve the demand model, data collected at discrete field sites could be extrapolated to consider a variety of other support conditions that could be present over the entire railway network. Such an extrapolation was documented by Quirós-Orozco et al (2018) in conjunction with the redesign of Amtrak's concrete sleeper for the Northeast Corridor (NEC). The study of support condition variability is non-trivial, and the cost of obtaining a holistic understanding of an entire rail corridor would be substantial.…”

“…The best input into an SRA, or probabilistic design methodology, for the flexural capacity of concrete sleepers are field data representative of the actual flexural demands. Such a methodology has been developed (Edwards et al, 2017b) and deployed by researchers at UIUC to answer a variety of engineering questions related to sleepers and the field moment demands placed on them (Wolf et al, 2016b;Canga Ruiz, 2018;Edwards et al, 2018;Quirós-Orozco et al, 2018;Canga Ruiz et al, 2020).…”

“…Additionally, a portion of this differential may be due to the discrete nature of key inputs in the sleeper design and manufacturing process (e.g., an integer number of wires can be used, finite grades of prestressing steel, etc.). Beyond this safety factor, results from previous field experimentation have indicated that there is significant excess design capacity (actual capacity exceeding service demand), highlighting the need for optimization of sleepers and the selection of a probabilistic design method (Wolf et al, 2016a;Edwards et al, 2017bEdwards et al, , 2018Quirós-Orozco et al, 2018). This is especially true for rail transit sleepers (Edwards et al, 2018).…”

“…In the United States, exceptions to the normal method of design include the application of structural reliability analysis (SRA) to light rail transit sleepers as presented by Canga Ruiz et al (2020) and research aimed at using field data, and other best practices, to design the next generation of concrete sleepers for Amtrak's Northeast Corridor (NEC) (Quirós-Orozco et al, 2018). Additionally, reliability analysis methods were developed at the University of Wollongong in Australia (Remennikov et al, 2012).…”

Quantification of the Effect of Train Type on Concrete Sleeper Ballast Pressure Using a Support Condition Back-Calculator

“…Concrete surface strain gauges applied using a method described by Edwards et al (2017) have successfully quantified the bending strains experienced by concrete sleepers in both laboratory and revenue service field experiments (Quirós-Orozco et al, 2018;Edwards et al, 2019;Canga Ruiz et al, 2020). Bending strains can then be converted into bending moments when laboratory-derived calibration factors are applied using the method described by Edwards et al (2017).…”