ECCOMAS 2024

Digital Twin for structural health monitoring of cultural heritage: the BUILDCHAIN Demo-Pilot, Palazzo Poniatowski-Guadagni in Florence

  • Croce, Pietro (University of Pisa, DICI)
  • Landi, Filippo (University of Pisa ,DICI)
  • Meligeni, Francesca (University of Pisa, DICI)

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Preserving the historical and cultural value of the built environment complying with the recent EU directives addressing resilience, sustainability, and energy efficiency in the building sector, is a modern challenge for Europe, as historical urban centres are prone to earthquakes and climate extremes events. Masonry buildings are particularly relevant in this context and the proper definition of mechanical parameters can be a crucial issue since they can vary in a wide range significantly affecting the outcomes of the assessment and the intervention strategies. A robust treatment of material uncertainties would require a substantial number of material tests, which can be both costly and time-consuming, as well as not aligned with the requirements of preservation. For this reason, it is extremely useful to rely on Bayesian inverse methods for the calibration of structural models based on limited measurements of the structural response. In the paper, a generalized Polynomial Chaos expansion (gPCE) surrogated model is presented for the natural frequencies and mode shapes of a significant case study, the Palazzo Poniatowski-Guadagni in Florence, a 18th century masonry building serving today as the headquarter of the local police. The surrogate model significantly decreases the computational time of the physics-based finite element (FE) model, propagating uncertainties in material properties, such as the elastic and shear modulus of masonry, relevant for the building’s dynamic response [1, 2]. Global sensitivity analyses are carried out using Sobol’ indices to assess the impact of input variability on the eigenfrequencies and eigenmodes, evaluating the need for experimental campaigns and the set-up of a structural health monitoring (SHM) system. Replacing the deterministic FE solver with a gPCE surrogate model will facilitate quasi-real-time SHM. The outcome will be a digital twin that serves as a tool for early warning and damage detection, relying on the Bayesian inference approach [3]. The developed digital twin will part of the BUILDCHAIN system (https://buildchain-project.eu/), including Digital Building Logbook (DBL) and BIM, contributing to demonstrate the benefits of implementing innovative DBLs for management and preservation of cultural heritage.