Novel unscented kalman filter for health assessment of structural systems with unknown input

Abdullah Al-Hussein, Achintya Haldar

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


A novel procedure for structural health assessment, denoted as unscented Kalman filter with unknown input (UKF-UI), is proposed using the nonlinear system identification concept. To increase its implementation potential, a substructure concept is introduced, producing a two-stage approach. It integrates the unscented Kalman filter concept and an iterative least-squares technique. The two most important features of the method are that it does not need the information on the time history of the excitation to identify structural systems represented by finite elements, and that it can identify defects in them using only a limited amount of noise-contaminated nonlinear response information. The proposed method is robust enough to detect the locations and severity of defects at different locations in the structure. The defect detection capability increases significantly if the defective member is in the substructure or close to it. The method is conclusively verified with the help of two examples using impulsive and seismic excitations. The superiority of UKF-UI over extended Kalman filter-based procedures is documented. The proposed UKF-UI procedure has high implementation potential and can be used for health assessment of large structural systems.

Original languageEnglish (US)
Article number08215003
Pages (from-to)4015012
Number of pages1
JournalJournal of Engineering Mechanics
Issue number7
StatePublished - Jul 1 2015


  • Damage detection
  • Extended kalman filter
  • Nonlinear system identification
  • Structural health assessment
  • Unknown input
  • Unscented kalman filter

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering


Dive into the research topics of 'Novel unscented kalman filter for health assessment of structural systems with unknown input'. Together they form a unique fingerprint.

Cite this