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An early detection of structural damage is an important goal of any structural health monitoring system. In particular, the ability to detect damages on-line, based on vibration data measured from sensors, will ensure the reliability and safety of the structures. Innovative data analysis techniques for the on-line damage detection of structures have received considerable attentions recently. The problem is quite challenging, in particular when the structure is nonlinear. In this paper, we proposed a new data analysis method, referred to as the sequential nonlinear least square estimation (SNLSE), for the on-line identification of nonlinear structural parameters. This new approach has significant advantages over the extended Kalman filter (EKF) approach in terms of the stability and convergence of the solution as well as the computational efforts involved. Further, an adaptive tracking technique recently proposed has been implemented in the proposed SNLSE to identify on-line the time-varying system parameters of nonlinear structures. The accuracy and effectiveness of the proposed approach has been demonstrated using a nonlinear elastic structure and nonlinear hysteretic structures. Simulation results indicate that the proposed approach is capable of tracking on-line the changes of structural parameters leading to the identification of structural damages.
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Jann N. Yang, Hongwei Huang, and Silian Lin "On-line damage identification of nonlinear structures", Proc. SPIE 5765, Smart Structures and Materials 2005: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, (17 May 2005);

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