Smart Structures and Systems

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System identification of an in-service railroad bridge using wireless smart sensors

  • Kim, Robin E. (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Moreu, Fernando (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign) ;
  • Spencer, Billie F. (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign)
  • Received : 2014.11.27
  • Accepted : 2015.02.15
  • Published : 2015.03.25
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Abstract

Railroad bridges form an integral part of railway infrastructure throughout the world. To accommodate increased axel loads, train speeds, and greater volumes of freight traffic, in the presence of changing structural conditions, the load carrying capacity and serviceability of existing bridges must be assessed. One way is through system identification of in-service railroad bridges. To dates, numerous researchers have reported system identification studies with a large portion of their applications being highway bridges. Moreover, most of those models are calibrated at global level, while only a few studies applications have used globally and locally calibrated model. To reach the global and local calibration, both ambient vibration tests and controlled tests need to be performed. Thus, an approach for system identification of a railroad bridge that can be used to assess the bridge in global and local sense is needed. This study presents system identification of a railroad bridge using free vibration data. Wireless smart sensors are employed and provided a portable way to collect data that is then used to determine bridge frequencies and mode shapes. Subsequently, a calibrated finite element model of the bridge provides global and local information of the bridge. The ability of the model to simulate local responses is validated by comparing predicted and measured strain in one of the diagonal members of the truss. This research demonstrates the potential of using measured field data to perform model calibration in a simple and practical manner that will lead to better understanding the state of railroad bridges.

Keywords

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