Keywords: structural health monitoring, multi-model updating, frame prototype, recursive, sensitivity analysis, parametric system identification..

An invaluable tool for structural health monitoring and damage detection, parametric system identification using model-updating is an inverse problem, affected by several kinds of modelling assumptions and measurement errors. By minimizing the discrepancy between the measured data and the simulated response, traditional model-updating techniques identify one single optimal model that behaves similarly to the real structure. As a result of several sources of error, this mathematical optimum may be far from the true solution and lead to misleading conclusions about the structural state. Instead of the mere location of the global minimum, therefore it preferred that several alternatives are generated, capable of expressing near-optimal solutions while being as different as possible from each other in physical terms. The work presented in this paper accomplishes this goal through a new recursive, direct search, multi-model updating technique, where multiple models are first created and separately solved for the respective minimum, and then a selection of quasi-optimal alternatives is retained and classified using data mining and a clustering algorithm. The main novelty of the approach consists of the recursive strategy adopted for minimizing the objective function, where convergence speed towards optimality is increased by sequentially changing only selected subsets of parameters, depending on their respective influence on the error function. The methodology is applied to the finite-element model updating of a large-scaled frame prototype.

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