On model updating of existing structures utilizing measured dynamic responses

Life-cycle performance evaluation of structures relies heavily on the usability of measured data. Typically, measurements are utilized to update the state of knowledge about a structure. This usually leads to a system identification problem. Due to the ill-conditionedness of inverse problems, the actual value of the obtained data may be severely reduced. Selective sensitivity provides a methodology to improve the condition of the inverse problems by transforming the original (large) problem into a sequence of smaller ones. The paper discusses this concept and develops a novel alternative concept for the development of selectively sensitive excitations, which does not require precise knowledge of the system parameters.