Experimental Determination of Crack Growth in Rails Subjected to Long-Term Cyclic Fatigue Loading

Details

• Creators:
  Allen, David H. ; Dorsett, Garrett ; Kim, Yong-Rak
• Corporate Creators:
  Texas Transportation Institute. University Transportation Center for Railway Safety
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; MxV Rail
• Subject/TRT Terms:
  Computer Algorithms Construction And Maintenance Cracking Crack Propagation Experiments Fracture Mechanics Loads Materials Mechanical Fatigue Predictive Models Railroads Testing

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• Publication/ Report Number:
  UTCRS-TAMU-I4CY24
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  June 1, 2024 – August 31, 2025
• Corporate Publisher:
  Texas Transportation Institute. University Transportation Center for Railway Safety
• Abstract:
  It is well known that one of the most significant causes of train derailments within the U.S. is due to rail fracture [FRA 2023]. Despite this fact, a reliable model for predicting fatigue fracture in rails has not yet been deployed within the U.S. We have recently been developing a multiscale computational algorithm for predicting crack evolution in ductile solids subjected to long-term cyclic loading [Souza et al., 2008, Souza et al., 2009, Souza and Allen, 2009, Souza and Allen, 2012, Allen et al., 2017a,b,c, Little et al., 2018]. In this UTCRS funded project, we performed intricate experiments on rails with internal cracks as a means of both obtaining material properties and validating an advanced computational model under development in our companion proposal entitled Computational Model for Predicting Fracture in Rails Subjected to Long-Term Cyclic Fatigue Loading. Furthermore, with funding provided by MxV Rail, we have recently completed cyclic crack growth experiments on seven bi-axially loaded rails with internal cracks that had previously been in service [Whetstone et al, 2023]. We are, therefore, in this research developing the ability to: a) characterize fracture parameters for deploying our advanced fracture mechanics model; b) utilize these parameters to predict crack growth due to cyclic fatigue in rails; and c) utilize our experimental results obtained over the previous decade of testing to validate our computational predictive methodology. Should this model development prove to be useful, it is our ultimate intention to utilize this new advanced technology as a tool for determining how long rails in which flaws have been detected can be safely retained in service.
• Format:
  PDF
• Funding:
  69A3552348340
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:5a416c6c9e4efc70c100b7b8331fe0eedbf4c2c567604180107cde7b4828167eaccd06d92dd0876541bd3ca03de86be599afe4ae38a3679e8a9fd9d133ca354b
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88687/dot_88687_DS1.pdf
• File Type:
  [PDF - 1.81 MB ]