Impact of Damping Models in Damage Identification

Several damage identification approaches are based on computational models, and their diagnostics depend on the set of modelling hypotheses adopted when building the model itself. Among these hypotheses, the choice of appropriate damping models seems to be one of the key issues. The goal of this paper is to analyze the impact of a set of damping models on the damage identification diagnostics. The damage identification is built on a Bayesian framework, and the measured data are the modal data associated with the first modes of the structure. The exploration of the posterior density of unknown model parameters is performed by means of the Markov chain Monte Carlo method (MCMC) with Delayed Rejection Adaptive Metropolis (DRAM) algorithm. The analyses are based on experimental dynamic response obtained from an aluminum beam instrumented with a set of accelerometers. The presence of damage/anomaly within the system is physically simulated by placing lumped masses over the beam, considering three different masses and two different placing positions. For the set of cases analyzed, it is shown that the proposed approach was able to identify both the position and magnitude of the lumped masses and that the damping models may not provide an increase of knowledge of some unknown parameters when damping rates are lower than 1%.

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