Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19719
Title: Substructural parameters and dynamic loading identification with limited observations
Authors: Xu, B
He, J
Keywords: Extended Kalman filter method with a weighted global iteration
Limited observations
Substructural identification
Unknown dynamic loadings
Weighted adaptive iteration algorithm
Issue Date: 2015
Publisher: Techno Press
Source: Smart structures and systems, 2015, v. 15, no. 1, p. 169-189 How to cite?
Journal: Smart structures and systems 
Abstract: Convergence difficulty and available complete measurement information have been considered as two primary challenges for the identification of large-scale engineering structures. In this paper, a time domain substructural identification approach by combining a weighted adaptive iteration (WAI) algorithm and an extended Kalman filter method with a weighted global iteration (EFK-WGI) algorithm was proposed for simultaneous identification of physical parameters of concerned substructures and unknown external excitations applied on it with limited response measurements. In the proposed approach, according to the location of the unknown dynamic loadings and the partially available structural response measurements, part of structural parameters of the concerned substructure and the unknown loadings were first identified with the WAI approach. The remaining physical parameters of the concerned substructure were then determined by EFK-WGI basing on the previously identified loadings and substructural parameters. The efficiency and accuracy of the proposed approach was demonstrated via a 20-story shear building structure and 23 degrees of freedom (DOFs) planar truss model with unknown external excitation and limited observations. Results show that the proposed approach is capable of satisfactorily identifying both the substructural parameters and unknown loading within limited iterations when both the excitation and dynamic response are partially unknown.
URI: http://hdl.handle.net/10397/19719
ISSN: 1738-1584
EISSN: 1738-1991
DOI: 10.12989/sss.2015.15.1.169
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