The present work addresses the topic of automated calibration of numerical models starting from the experimental characterization of the structure’s dynamic behaviour. The importance of the topic is well known in the literature, especially in cases where it is necessary to have at disposal validated numerical models, necessary for the correct evaluation of the safety of existing buildings. Generally, the calibration problem is developed with a manual approach (manual tuning), with a positive outcome whenever there is a good knowledge of the boundary and internal constraint conditions and the elastic mechanical properties of the construction’s constituent materials. Conversely, the positive outcome is particularly difficult to achieve manually when there are non-homogeneous and/or complex structures, as in the cases of historic masonry structures, which are often the result of constructions carried out at different times, organized in aggregates whose interaction between the portions is not simple to understand. For this purpose, the present work, using commercial software and specially prepared routines, illustrates a semi-automatic procedure, which employs genetic algorithms, suitable for the optimized identification of the numerical model that best represents the structure’s experimental dynamic behaviour. The procedure is presented with reference to two case studies: the Gabbia Tower historic masonry aggregate in Mantua and the bell tower of the Monastery of the Ursuline nuns in Capriolo, Brescia. In the first case, in addition to the experimental dynamic characterization, a good instrumental characterization of the tower’s masonry mechanical properties is available. In the second case, alongside a good experimental dynamic characterization, only a qualitative estimate of the masonry mechanical properties, based on visual inspections, is available. The two case studies allow for testing the validity of the numerical models’ calibration procedure, necessary for their application in the field of safety checks. Finally, for the case studies analysed the work presents an assessment of seismic vulnerability starting from the models identified with the semi-automatic procedure. The seismic vulnerability assessment was obtained using non-linear static analysis following the N2 method.
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