Smart Structures and Systems

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Wireless operational modal analysis of a multi-span prestressed concrete bridge for structural identification

  • Whelan, Matthew J. (Clarkson University) ;
  • Gangone, Michael V. (Clarkson University) ;
  • Janoyan, Kerop D. (Department of Civil and Environmental Engineering, Clarkson University) ;
  • Hoult, Neil A. (Department of Civil Engineering, Queens University) ;
  • Middleton, Campbell R. (Structural Engineering with the Department of Engineering, University of Cambridge) ;
  • Soga, Kenichi (Civil Engineering with the Department of Engineering, University of Cambridge)
  • Received : 2009.10.14
  • Accepted : 2010.01.18
  • Published : 2010.07.25
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Abstract

Low-power radio frequency (RF) chip transceiver technology and the associated structural health monitoring platforms have matured recently to enable high-rate, lossless transmission of measurement data across large-scale sensor networks. The intrinsic value of these advanced capabilities is the allowance for high-quality, rapid operational modal analysis of in-service structures using distributed accelerometers to experimentally characterize the dynamic response. From the analysis afforded through these dynamic data sets, structural identification techniques can then be utilized to develop a well calibrated finite element (FE) model of the structure for baseline development, extended analytical structural evaluation, and load response assessment. This paper presents a case study in which operational modal analysis is performed on a three-span prestressed reinforced concrete bridge using a wireless sensor network. The low-power wireless platform deployed supported a high-rate, lossless transmission protocol enabling real-time remote acquisition of the vibration response as recorded by twenty-nine accelerometers at a 256 Sps sampling rate. Several instrumentation layouts were utilized to assess the global multi-span response using a stationary sensor array as well as the spatially refined response of a single span using roving sensors and reference-based techniques. Subsequent structural identification using FE modeling and iterative updating through comparison with the experimental analysis is then documented to demonstrate the inherent value in dynamic response measurement across structural systems using high-rate wireless sensor networks.

Keywords

References

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