ABSTRACT
Structural model updating is an inverse problem according to which a model of a structure, usually a finite element model, is adjusted so that either the calculated time histories, frequency response functions, or modal parameters best match the corresponding quantities measured or identified from the test data. This inverse process aims at providing updated models and their corresponding uncertainties based on the data. These updated models are expected to give more accurate response predictions to future loadings, as well as allow for an estimation of the uncertainties associated with such response predictions. In practice, the inverse problem of model updating is usually ill-conditioned due to insensitivity of the response to changes in the model parameters, and nonunique [17-20] because of insufficient available data relative to
the desired model complexity. Additional difficulties associated with the development of an effective model updating methodology include: (1) model error present due to the fact that the chosen class of structural models is unable to exactly model the actual behavior of the structure; (2) measurement noise in the dynamic data, especially for higher modes; (3) incomplete set of observed DOFs (degrees of freedom) due to the limited number of sensors available and the limited accessibility throughout the structure; (4) incomplete number of contributing modes due to limited bandwidth in the input and the dynamic response.
