Quantifying shared corridor wheel loading variation using wheel impact load detectors

A sustained increase in heavy axle loads and cumulative freight tonnages, coupled with increased development of high speed passenger rail, is placing an increasing demand on railway infrastructure. Some of the most critical areas of the infrastructure in need of further research are track components used in high speed passenger, heavy haul, and shared infrastructure applications. In North America, many design guidelines for these systems use historical wheel loads that may not necessarily be representative of those seen on rail networks today. Without a clear understanding of the nature of these loads, it is impossible to adequately evaluate the superstructure to make design improvements. Therefore, researchers at the University of Illinois at Urbana-Champaign (UIUC) are conducting research to lay the groundwork for an improved and thorough understanding of the loading environment entering the track structure. Wheel impact load detectors (WILDs) have been used in North America for decades to identify bad-acting wheels that could damage the rail infrastructure or result in a rolling stock failure. The WILD measures vertical and lateral rail loads imparted by the wheel at the wheel-rail interface, along with other pertinent information related to the specific wheel, car, and train passing the instrumented site. This information can be used to identify and classify trends in the loading features and other characteristics of the rolling stock. These trends not only provide a clearer picture of the existing loading environment created by widely varied traffic characteristics, but can be used in future design and maintenance planning of infrastructure according to the anticipated traffic. This paper will discuss the current trends in wheel loads across the North American rail network while investigating the effects of speed on dynamic and impact loads. Ultimately this work should lead to useful distinctions of loads for improved design methodologies that are specific to the intended type of traffic traversing a given route or network.