Smart Structures and Systems

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Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model

  • Cho, Soojin (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Yun, Chung-Bang (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Sim, Sung-Han (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST))
  • Received : 2014.11.05
  • Accepted : 2015.02.12
  • Published : 2015.03.25
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Abstract

Recently, an indirect displacement estimation method using data fusion of acceleration and strain (i.e., acceleration-strain-based method) has been developed. Though the method showed good performance on beam-like structures, it has inherent limitation in applying to more general types of bridges that may have complex shapes, because it uses assumed analytical (sinusoidal) mode shapes to map the measured strain into displacement. This paper proposes an improved displacement estimation method that can be applied to more general types of bridges by building the mapping using the finite element model of the structure rather than using the assumed sinusoidal mode shapes. The performance of the proposed method is evaluated by numerical simulations on a deck arch bridge model and a three-span truss bridge model whose mode shapes are difficult to express as analytical functions. The displacements are estimated by acceleration-based method, strain-based method, acceleration-strain-based method, and the improved method. Then the results are compared with the exact displacement. An experimental validation is also carried out on a prestressed concrete girder bridge. The proposed method is found to provide the best estimate for dynamic displacements in the comparison, showing good agreement with the measurements as well.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

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