Damage Assessment of Structures with Uncertainty

Evolutionary algorithms are suitable to solve damage identification problems in a multiobjective context. However, the performance of these methods can deteriorate quickly with increasing noise intensities originating numerous uncertainties. In this work, a statistic structural damage detection method formulated in a multiobjective context is proposed, taking into account the uncertainties existing. The presented method is verified by a number of simulated damage scenarios. The effects of noise on damage detection are investigated.