Experimental validation of Kalman filter-based strain estimation in structures subjected to non-zero mean input

Rajendra P. Palanisamy, Soojin Cho, Hyunjun Kim, Sung Han Sim

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


Response estimation at unmeasured locations using the limited number of measurements is an attractive topic in the field of structural health monitoring (SHM). Because of increasing complexity and size of civil engineering structures, measuring all structural responses from the entire body is intractable for the SHM purpose; the response estimation can be an effective and practical alternative. This paper investigates a response estimation technique based on the Kalman state estimator to combine multi-sensor data under non-zero mean input excitations. The Kalman state estimator, constructed based on the finite element (FE) model of a structure, can efficiently fuse different types of data of acceleration, strain, and tilt responses, minimizing the intrinsic measurement noise. This study focuses on the effects of (a) FE model error and (b) combinations of multi-sensor data on the estimation accuracy in the case of non-zero mean input excitations. The FE model error is purposefully introduced for more realistic performance evaluation of the response estimation using the Kalman state estimator. In addition, four types of measurement combinations are explored in the response estimation: strain only, acceleration only, acceleration and strain, and acceleration and tilt. The performance of the response estimation approach is verified by numerical and experimental tests on a simply-supported beam, showing that it can successfully estimate strain responses at unmeasured locations with the highest performance in the combination of acceleration and tilt.

Original languageEnglish
Pages (from-to)489-503
Number of pages15
JournalSmart Structures and Systems
Issue number2
StatePublished - 1 Feb 2015


  • Data fusion
  • Kalman filter
  • Model error
  • Non-zero mean input
  • Response estimation


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