System identification based on selective sensitivity analysis: A case-study

Dynamic measurements are frequently applied to assess structural reliability in the context of health monitoring or design evaluation. The system identification procedure involved requires the solution of an inverse problem, which usually leads to rather ill-conditioned formulations. An experimental strategy based on selective sensitivity provides a way out of this dilemma. In this approach, the measured response of a structure is only sensitive to a few model parameters (to be identified), but insensitive to the others. These few essential parameters are identified by applying selectively sensitive excitations. It is shown that the updating algorithms for selective sensitive excitations are always better conditioned than those for any other excitation combination. In a laboratory application, the methodology is applied to a simple structural model. With only a very limited prior knowledge of the structure, a robust converging mathematical procedure is presented. It is shown that the selective sensitive excitations can be experimentally realized in a convenient approach which overcomes the limitation of this method as previously reported in the literature. Different identification methods are applied on the given structure. Capabilities to identify structural modifications and limitations of these approaches are discussed. (C) 2012 Elsevier Ltd. All rights reserved.