Vehicle-Bourne Autonomous Railroad Bridge Impairment Detection Systems

Timber bridges constitute approximately 30% of current railroad bridge inventories in North America. Inspections of these structures usually comprise visual assessments of the condition of individual, observable components of the bridge. Special inspections may call for the field personnel to observe a bridge under load: that is, while a train is crossing. When creating a prioritized list of critical bridges to be replaced or repaired, there is advantage to having complementary measurements of bridge structural behavior under load. The objective of the proposed research program is to develop technology that will facilitate detecting structural impairments in timber railroad bridges using data gathered from rail vehicles that cross the bridges. Such a capability would represent a significant improvement over the current approaches used to maintain timber railroad bridges. The underlying logic behind using rail vehicle measurements to determine bridge fitness can be summarized as follows: Serious structural impairments in timber railroad bridges cause increased bridge motions under loading. Bridge motions comprise one aspect of the overall motions rail vehicles experience when crossing a bridge. The motions of a rail vehicle can be measured by sensors attached to the vehicle. Once appropriate signal processing algorithms are developed, this being a key objective of the proposed research program, it might be possible to infer bridge motions from the measured behavior of a rail vehicle crossing the bridge. Measured motions of a bridge can be compared against threshold values of motions that are considered safe. In order to meet these objectives, a research project is proposed that will incorporate (1) modeling of railcar-bridge system, (2) instrumentation of timber railroad bridges and railcars, (3) large scale experimentation of railcar-bridge systems, and (4) development of an Autonomous Data Reduction and Interpretation System using artificial neural networks to correlate vehicle dynamic response to bridge response and subsequently determine possible structural impairment.


  • English


  • Status: Completed
  • Funding: $135,216
  • Contract Numbers:


  • Sponsor Organizations:

    Research and Innovative Technology Administration

    University Transportation Centers Program
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    Texas A&M University

    College Station, TX  United States  77843
  • Principal Investigators:

    Fry, Gary

  • Start Date: 20140101
  • Expected Completion Date: 20161231
  • Actual Completion Date: 20161231
  • Source Data: RiP Project 36697

Subject/Index Terms

Filing Info

  • Accession Number: 01528386
  • Record Type: Research project
  • Source Agency: University Transportation Center for Railway Safety
  • Contract Numbers: DTRT13-G-UTC59
  • Files: UTC, RIP
  • Created Date: Jun 19 2014 1:00AM