Challenges in developing confidence intervals on modal parameters estimated for large civil infrastructure with stochastic subspace identification

This paper examines current methods for estimating uncertainty and confidence intervals on modal parameters estimated using stochastic subspace identification. It is found that most perturbation methods have been limited to estimating variance and not higher moments of the identified modal parameters. It is shown that the modal parameters may exhibit non-normal distributions and in such cases the variance is insufficient in estimating confidence intervals. A current perturbation method is extended to estimate higher moments but it is shown that the inaccuracy in estimating the covariance of the covariance function limits the accuracy of this approach. A residual bootstrapping approach is then investigated; however, it is found with both numerical and measured data that the residuals are not all reduced to white series. It is also found that the excitation of the suspension bridge investigated is non-Gaussian distributed. These are serious difficulties in applying a residual bootstrapping procedure reliably. Based on these investigations, some current challenges in obtaining accurate confidence intervals on estimated modal parameters for large civil infrastructure are summarized.