The influence of model and measurement uncertainties on damage detection of experimental structures through recursive algorithms

Mehrdad Ebrahimi, Elnaz Nobahar, Reza Karami Mohammadi, Ehsan Noroozinejad Farsangi, Mohammad Noori and Shaofan Li

Reliability Engineering and System Safety, 2023, vol. 239, issue C

Abstract: In this work, we developed a framework for identifying frame-type structures regarding the measurement uncertainty and the uncertainty involved in inherent and structural parameters. The identification process is illustrated and examined on a one-eight-scale four-story moment-resisting steel frame under seismic excitation using two well-known recursive schemes: the Extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) methods. The nonlinear system equations were assessed by applying a first-order instantaneous linearization approach through the EKF method. In contrast, the UKF algorithm employs several sample points to estimate moments of random variables’ nonlinear transformations. A nonlinear transformation is applied to distribute sample points to derive the precise mean and covariance up to the second order of any nonlinearity. Accordingly, it is theoretically expected that the UKF algorithm is more capable of identifying the nonlinear systems and determining the unknown parameters than the EKF algorithm. The capability of the EKF and UKF algorithms was assessed by considering a 4-story moment-resisting steel frame with several inherent uncertainties, including the material behavior model, boundary conditions, and constraints. In addition to these uncertainties, the combination of acceleration and displacement responses of different structural levels is employed to evaluate the capability of the algorithms. The information entropy measure is used to investigate further the uncertainty of a group of established model parameters. As highlighted, a good agreement is observed between the results using the information entropy measure criterion and those using the UKF and EKF algorithms. The results illustrate that using the responses of fewer levels placed in the proper positions may lead to improved outcomes than those of more improperly positioned levels.

Keywords: Uncertainty; Extended Kalman filter; Unscented Kalman filter; Recursive algorithms; Damage detection; Information entropy; Sensors (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832023004453
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:239:y:2023:i:c:s0951832023004453

DOI: 10.1016/j.ress.2023.109531

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().