Finite Element Model updating of a real structure based on a multi-objective optimization approach

The assessment of the seismic vulnerability of the structures is important to define the seismic risk of an area and to plan actions to mitigate the risk. An accurate assessment of the structural seismic vulnerability is firstly based on a model that describes faithfully the real structure. The definition of such model is a hard task, in spite of the computational resources that are nowadays available. Indeed, the real values of some characteristics of the existing structures, the boundary conditions and the loads are unknown and generate uncertainty in the numerical model [1] reducing the quality of results of the seismic analyses. Further, the structural characteristics and the boundary conditions can change during the life time of the structures, so the numerical model has to be modified regarding to the real current state of the structure. In order to overcome the difficulty to define an accurate FEM, field test data are used to update it. Several indirect methods were proposed in literature to update a FEM [1], but only few of them were applied to a model of a real structure. This kind of model updating methods is formulated as an optimization problem. The single objective optimization approach is the most commonly used in literature, but some scholars have already highlighted its limits [2, 3]. This paper uses an approach based on the Multi-Objective optimization using Genetic Algorithm [5] to update a FEM of a real masonry building located in Vienna and built at the beginning of the previous century. This FEM updating approach reduces the difference between the analytical and experimental modal characteristics of the buildings. The analytical modal characteristics are obtained from FEM analyses, while the experimental ones are calculated from the results of forced vibration tests [4]. A sensitivity analysis of the model parameters to variation of the modal characteristics is carried out. This analysis guides the selection of the parameters and the formulation of the objective functions of the optimization problem.