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Proceedings Paper

Time domain nonlinear SMA damper force identification approach and its numerical validation
Author(s): Lulu Xin; Bin Xu
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Paper Abstract

Most of the currently available vibration-based identification approaches for structural damage detection are based on eigenvalues and/or eigenvectors extracted from vibration measurements and, strictly speaking, are only suitable for linear system. However, the initiation and development of damage in engineering structures under severe dynamic loadings are typical nonlinear procedure. Studies on the identification of restoring force which is a direct indicator of the extent of the nonlinearity have received increasing attention in recent years. In this study, a date-based time domain identification approach for general nonlinear system was developed. The applied excitation and the corresponding response time series of the structure were used for identification by means of standard least-square techniques and a power series polynomial model (PSPM) which was utilized to model the nonlinear restoring force (NRF). The feasibility and robustness of the proposed approach was verified by a 2 degree-of-freedoms (DOFs) lumped mass numerical model equipped with a shape memory ally (SMA) damper mimicking nonlinear behavior. The results show that the proposed data-based time domain method is capable of identifying the NRF in engineering structures without any assumptions on the mass distribution and the topology of the structure, and provides a promising way for damage detection in the presence of structural nonlinearities.

Paper Details

Date Published: 2 April 2012
PDF: 9 pages
Proc. SPIE 8409, Third International Conference on Smart Materials and Nanotechnology in Engineering, 84090W (2 April 2012); doi: 10.1117/12.923331
Show Author Affiliations
Lulu Xin, Hunan Univ. (China)
Bin Xu, Hunan Univ. (China)

Published in SPIE Proceedings Vol. 8409:
Third International Conference on Smart Materials and Nanotechnology in Engineering
Jinsong Leng; Yoseph Bar-Cohen; In Lee; Jian Lu, Editor(s)

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