Surrogation and Bayesian updating of a finite element model of the Leaning Tower of Pisa

Numerical models play a crucial role in the study and understanding of cultural heritage structures, serving as valuable tools for predicting their behavior under diverse and prospective scenarios. They are however affected by various uncertainties, which impact can be mitigated through the calibration of model parameters. For heritage structures, where testing is usually restricted to the use of non-destructive techniques, and often unable to directly assess the inherent heterogeneity of the materials, a calibration approach can prove particularly useful to obtain a working model. This work applies a Bayesian model updating procedure to material-related uncertainties affecting a recently developed finite element model of the Leaning Tower of Pisa also comprising the underlying soil layers. A general Polynomial Chaos Expansion-based surrogation of the model was employed to evaluate the sensitivity of modal properties and ease the computational burden that comes with the probabilistic framing of the updating problem. Material property distributions were then updated, taking advantage of modal data from a newly installed sensor network which allowed the most up-to-date dynamic identification of the monument. The results represent the first probabilistic model-based assessment of material uncertainties in a three-dimensional finite element model of the Leaning Tower of Pisa. They shed some light into the value of specific modal information, while the use of analytical surrogation paves the way for the future design of a real-time updating procedure for monitoring and damage detection. © 2024 11th European Workshop on Structural Health Monitoring,