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Smart Structures and Systems
  Volume 15, Number 1, January 2015, pages 169-189
DOI: http://dx.doi.org/10.12989/sss.2015.15.1.169
 


Substructural parameters and dynamic loading identification with limited observations
Bin Xu and Jia He

 
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.
 
Key Words
    substructural identification; limited observations; unknown dynamic loadings; weighted adaptive iteration algorithm; extended Kalman filter method with a weighted global iteration
 
Address
Bin Xu: College of Civil Engineering, Hunan University, Changsha, Hunan, China
Jia He: College of Civil Engineering, Hunan University, Changsha, Hunan, China;
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
 

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