Effect of measurement noise and excitation on Generalized Response Surface Model Updating

This paper investigates the robustness of a parameter estimation procedure for nonlinear Finite Element (FE) model updating. Through this procedure, polynomial Response Surface (RS) models are constructed to approximate the response of a nonlinear FE model at every time step of the analysis. Subsequently, the optimization problem of model updating is solved iteratively in time which results in histograms of the updating parameters. With the assumption of White Gaussian measurement noise, it is shown that this parameter estimation technique has low sensitivity to the standard deviation of the measurement noise. In order to validate this, a parametric sensitivity study is performed through numerical simulations of nonlinear systems with single and multiple degrees of freedom. The results show the least sensitivity to measurement noise level, selected time window for model updating, and location of the true model parameters in RS regression domain, when vibration frequency of the system is outside the frequency bandwidth of the load. Further application of this method is also presented through a case study of a steel frame with bilinear material model under seismic loading. The results indicate the robustness of this parameter estimation technique for different cases of input excitation, measurement noise level, and true model parameters.