Publications
Bayesian Approaches for Damage Identification of a Historic Masonry Tower
A Bayesian approach successfully identified structural damage in the Torre Grossa, a 60-meter-tall medieval masonry tower in San Gimignano, Italy. This method integrated diverse data sources—static and dynamic Structural Health Monitoring (SHM) systems alongside detailed cracking pattern surveys—to quantify uncertainties and model inadequacies, providing a reliable computational framework for assessing historic buildings' performance under exceptional loads. The study highlights the Torre Grossa's unique challenges, including its multi-leaf stone masonry walls with heterogeneous internal cores and integration into an adjacent building, offering insights applicable to other historic structures.
A risk-reduction framework for urban cultural heritage: a comparative study on Italian historic centres
A comparative study of Italian historic centres, including the UNESCO-listed Historic Centre of San Gimignano, has developed and validated a large-scale methodology for assessing and managing risks to cultural heritage. This framework addresses both tangible and intangible values while considering vulnerabilities such as built environment characteristics, community dynamics, risk awareness, maintenance gaps, and regulatory shortcomings. The approach evaluates primary and secondary hazards, site-specific threats, and event impact chains to tailor mitigation, preparedness, response, and recovery measures. Applied to San Gimignano and Reggio Calabria, the methodology identifies research gaps and practical opportunities for standardized safety guidelines, offering a comprehensive and inclusive tool for disaster risk management in cultural heritage.
Bayesian model updating of historic masonry towers through dynamic experimental data
A Bayesian approach successfully updates finite element models of historic masonry towers, addressing critical uncertainties in material properties and geometry. Using dynamic experimental data from San Gimignano's towers, the study refines numerical models by incorporating measured natural periods and accounting for measurement errors and modeling uncertainties, such as restraint effects from neighboring buildings. This method enhances seismic risk assessment, a key factor in preserving cultural heritage. The findings highlight the potential of Bayesian model updating to improve structural analysis in historic masonry towers, with future applications extending to seismic studies.
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