This paper presents the surrogate-based Bayesian model updating of an historical masonry bell tower. The finite element model of the structure is updated on the basis of the modal properties experimentally identified thanks to a vibration test. Vibration-based structural identification allows to obtain an enhanced knowledge of the structural behaviour. This is especially important for ageing structures that are sensitive to dynamic actions induced by earthquakes or wind. In a general context, model updating results are highly affected by several uncertainties, regarding both the experimental measures and the model. Stochastic approaches to model updating, as the one based on Bayes’ theorem, enable to quantify the uncertainties associated to the updated parameters and, consequently, to increase the reliability of the identification.

The major drawback of Bayesian model updating, that in some cases limits its use, is the high computational effort requested to compute the posterior distribution of parameters. For this reason, the authors propose to integrate the classical procedure with a surrogate model. Surrogate models have been widely applied in several engineering contexts since they allow to approximate a complex function, not analytically definable, with a simpler function, determining a substantial saving of time. In this regard, a key aspect is surely the quality of the approximation, that must be ensured by a proper tuning. In the paper, a Gaussian surrogate is employed for the approximation of the posterior distribution of parameters and the performances of the proposed method are compared to those of an available Bayesian numerical method.