Repetitive identification of structural systems using a nonlinear model parameter refinement approach

Article type: Research Article

Authors: Lin, Jeng-Wen | Chen, Hung-Jen

Affiliations: Department of Civil Engineering, Feng Chia University, 407 Taichung, Taiwan | Graduate Institute of Civil and Hydraulic Engineering, Feng Chia University, 407 Taichung, Taiwan

Note: [] Corresponding author. Tel.: +886 4 24517250 Ext. 3150; Fax: +886 4 24516982; E-mail: jwlin@fcu.edu.tw

Abstract: This paper proposes a statistical confidence interval based nonlinear model parameter refinement approach for the health monitoring of structural systems subjected to seismic excitations. The developed model refinement approach uses the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a least-squares regression setting. When the parameters' confidence interval covers the zero value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model refinement until all the parameters' statistical significance cannot be further improved. This newly developed model refinement approach is implemented for the series models of multivariable polynomial expansions: the linear, the Taylor series, and the power series model, leading to a more accurate identification as well as a more controllable design for system vibration control. Because the statistical regression based model refinement approach is intrinsically used to process a "batch" of data and obtain an ensemble average estimation such as the structural stiffness, the Kalman filter and one of its extended versions is introduced to the refined power series model for structural health monitoring.

Keywords: Repetitive model refinement, statistical confidence interval, power series, health monitoring, Kalman filter

DOI: 10.3233/SAV-2009-0463

Journal: Shock and Vibration, vol. 16, no. 3, pp. 229-240, 2009