LAUSR

Abstract In the last few years, increasing attention has been given to obtaining reliable results in system identification as a key step in performing model updating, damage detection and structural health monitoring. Many methods have been developed to measure the uncertainty in modal estimates. Some techniques are based on signal resampling to obtain a larger sample space and consequently better process statistics. Thus, this study proposes a new method to quantify uncertainties using the stochastic subspace identification (SSI) technique combined with bootstrap resampling. To validate and compare the performance of the proposed algorithm, two algorithms previously mentioned in the literature were implemented. The first is based on the “moving block method”, and the second is based on blocks of the projection matrix. The proposed method initially generates random decrement functions from signal segments and later uses these functions as bootstrap resampling blocks in SSI. To validate and compare the performance of the proposed algorithm, numerical simulations and two experimental analyses are performed. The first analysis involves a simply supported steel beam subjected to vibration tests through random impacts, and the second involves a three-dimensional four-story frame subjected to vibrations from random signals generated by an electrodynamic exciter. The results of the proposed technique were more robust and accurate than those of the other methods.