The structural identification problem is formulated in a multi-objective context that allows the simultaneous minimization of the various objectives related to the fit between measured and model predicted data. Thus, the need for using arbitrary weighting factors for weighting the relative importance of each objective is eliminated. The set of admissible solutions are known in multi-objective optimization terminology as Pareto optimal solutions and constitute acceptable compromise solutions that cannot be improved in any objective without causing degradation in one other objective. The strength Pareto evolutionary algorithm is used to obtain the set of Pareto solutions. The use of the proposed methodology is illustrated by identifying the Pareto front and the corresponding set of optimal solutions for a model of a scaled laboratory structure using experimentally obtained modal data.
