Structural health assessment using noise-contaminated minimum dynamic response information

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

A novel nondestructive structural health assessment (SHA) technique, known as the generalized iterative least squares-extended Kalman filter-with unknown input (GILSEKF-UI) method, is being developed at the University of Arizona. The primary objective of the procedure is to detect defects in new, deteriorated or rehabilitated existing structures or just after large natural (strong earthquakes, high wind, etc.) or manmade (blast, explosion, etc.) events. The method is essentially a time domain finite element-based system identification (SI)-based procedure to locate defect at the local element level. Most SI-based SHA approaches use excitation and response information to identify a structure. Excitation information is not available in most cases. Furthermore, outside the control laboratory environment, the collection of excitation information could be so error-prone that the SI concept may not be applicable. It will be desirable if a system can be identified without excitation information. For large complicated real structures, it may not be possible to measure responses at all dynamic degrees of freedom and they always contain noise. Addressing all the issues, a Kalman filter-based algorithm is being developed with considerable success. It can be used for rapid diagnostic purpose as a part of a broader SHA and maintenance strategy.

Original languageEnglish (US)
Title of host publicationFrontier Technologies for Infrastructures Engineering
PublisherCRC Press
Pages383-407
Number of pages25
ISBN (Electronic)9780203875599
ISBN (Print)9780415498753
StatePublished - Jan 1 2009

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Haldar, A. (2009). Structural health assessment using noise-contaminated minimum dynamic response information. In Frontier Technologies for Infrastructures Engineering (pp. 383-407). CRC Press.