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Fractal Analysis of Geometry Data of Railroad Track Condition Assessment [Research Results]

File Language:
English


Details

  • Creators:
    Fateh, Mahmood
  • Corporate Creators:
    United States. Department of Transportation. Federal Railroad Administration
  • Subject/TRT Terms:
  • Publication/ Report Number:
    RR00-06
  • Resource Type:
    Brief
  • Geographical Coverage:
    United States
  • Corporate Publisher:
    United States. Department of Transportation. Federal Railroad Administration
  • Abstract:
    The Federal Railroad Administration sponsored a research project to investigate the application of Fractal Analysis to railway track geometry data and to develop numerical indices based on this analysis for use in track condition assessment, improved safety and efficiency of operations, and diagnosis of the cause of poor track condition. Fractal Analysis is an analytical technique that can be applied to characterize and to quantify irregular patterns that are chaotic and random; as track geometry data are classified. Fractal analysis of geometry data revealed that the mid-chord offsets (MCOs) of the vertical profile data have typically two orders of roughness. This bi-fractal condition results in three fractal parameters to describe the geometry pattern: two independent fractal dimensions (DR1 and DR2) based on the slope of the log-log plots and the location of the breakpoint. Figure 1 is a plot of the basic geometry profile data and the derived fractal parameters (DR1 and DR2). Two fractal analysis software programs were developed during this research: (1) Fractal Track Evaluation (FTEval) used to evaluate the applicability of fractal analysis to track geometry, and (2) Fractal Track Automatic (FTAuto), evolved from the FTEval, which performs fractal analysis on the geometry data for any length of track. The study concluded that the geometric patterns of the track conditions could be studied through fractal analysis.
  • Format:
    PDF
  • Collection(s):
  • Main Document Checksum:
    urn:sha256:83c08208b7e978351938b3792d6748986113e7e7f8efd320e7495d3060e8da4e
  • Download URL:
  • File Type:
    Filetype[PDF - 3.40 MB ]
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