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

Soojin Cho, Chung Bang Yun, Sung Han Sim

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

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.

Original languageEnglish
Pages (from-to)645-663
Number of pages19
JournalSmart Structures and Systems
Volume15
Issue number3
DOIs
StatePublished - 1 Mar 2015

Keywords

  • Bridge
  • Data fusion
  • Displacement
  • Finite element model
  • Modal mapping

Fingerprint

Dive into the research topics of 'Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model'. Together they form a unique fingerprint.

Cite this