This paper proposes a statistical confidence interval based model updating approach for the health diagnosis of structural systems subjected to seismic excitations. The proposed model updating 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 “null” value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model updating until all the parameters’ statistical significance cannot be further improved. This newly developed model updating approach is implemented for the developed 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.

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