| Abstract: | For the identification of multi-degree-of-freedom structures, it is not practical to identify all of the parameters included in the structures because enormous computation time is required and because identifiability may not be possible. In this paper, a localized identification approach through substructuring is formulated in the frequency domain. A technique of spectral smoothing is incorporated in the approach to deal with noise-corrupted data. The proposed approach can be used to identify the structural parameters in any part of interest in a structure. The numerical investigations for a lumped mass-spring-dashpot system indicate that faster convergence and higher accuracy are achieved and the noise influences on the identified results are reduced greatly by spectral smoothing. The approach also applies to whole-structure identification if the required records available and the numerical example shows that higher accuracy results are obtained with less cpu time and more poorly guessed initial values as compared with the general complete-structure identification. |
