Non-Parametric Identification of Structural Nonlinearity with Limited Input and Output Measurements

Abstract:

In this paper, a technique is proposed for non-parametric identification of structural nonlinearity with limited input and output measurements. The identification algorithm is based on the classical Kalman estimator for the displacement and the velocity responses and the recursive least square estimation for the unmeasured excitation and the restoring force. Two different models are used to simulate nonlinear structures: One is a 4-storey shear-frame structure with excitation on the top floor and the nonlinearity occurs at the bottom floor. The other is also a 4-storey shear-frame structure with both excitation and the nonlinearity at the top floor. Two numerical examples are carried out for the two kinds of models. Bouc-Wen hysteretic models are used to simulate the nonlinear impact. The simulation results demonstrate the efficiency of the proposed technique with limited output measurements.

• Periodical: Advanced Materials Research (Volumes 243-249)
• Pages: 5403-5407
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.243-249.5403
• Online since: May 2011
• Authors: Ying Lei, Yan Wu
• Keywords: Kalman Estimator, Least Square Estimation, Nonlinear Restoring Force Identification, Non-Parametric Identification, Unknown Output, Unkown Input

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