Identification of unknown inputs considering structural parametric uncertainties. (25th June 2019)

• Record Type: Journal Article
• Authors: Yang, Ning; Wang, Jia-xiang; Rao, Yong-pin; Lei, Ying
• Is Part Of: International journal of lifecycle performance engineering. Volume 3:Number 2(2019)
• Page Start: 187
• Page End: 209
• Publication Date: 2019-06-25
• Subjects: excitation identification -- Kalman filter -- unknown input -- structural parametric uncertainties -- probability model -- interval model; Structural analysis (Engineering) -- Periodicals; Buildings -- Performance -- Periodicals; 624.1705
• Languages: English
• ISSNs: 2043-8648

Abstract

Due to the inevitable uncertainties in structural parameters and difficulty in measuring external excitations, it is necessary to consider the effects of structural parametric uncertainties in identifying unknown inputs to structures. In this paper, considering structural parametric uncertainties, two excitation identification approaches are proposed accounting for the different scenarios of sensor deployments. The first algorithm is based on the improved Kalman filter with unknown input (KF-UI) recently proposed by the authors, in which acceleration responses are measured at the locations where unknown inputs applied. The second method is based on modal Kalman filter with unknown input (MKF-UI) to consider the scenario that acceleration responses at the locations of unknown inputs are unmeasured. For the uncertainties of structural parameters, probability model or interval model are studied, respectively. Numerical examples are performed and Monte Carlo simulation is applied in comparison to validate the effectiveness and accuracy of the unknown input identification.