Publicação
SHM for historic masonry: long-term data from the Dome of Santa Maria del Fiore
| Resumo: | This study presents the long-term static monitoring data acquired from the Dome of Santa Maria del Fiore in Florence. The monitoring system, operating since the late 1980s, includes displacement transducers and temperature sensors strategically placed along radial, meridian, and parallel directions of the dome, recording time series of over 54,000 samples, as well as force-balance accelerom eters measuring the structural response at four levels in three directions. The data was pre-processed accounting for various anomalies, including missing data, spikes, and shifts and then analysed from a statistical point of view considering the whole time series. The work highlights key challenges in handling anomalies and assessing data quality, and discusses the integration of multi-modal data, support ing the need for automated pre-processing frameworks and future incorporation of dynamic measurements into the structural assessments of the dome. |
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| Autores principais: | Marafini, F. |
| Outros Autores: | Zini, G.; Barontini, A.; Mendes, N.; Betti, M.; Bartoli, G. |
| Assunto: | Structural health monitoring Historic masonry structures Cultural heritage Sensor malfunction Preventive conservation |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | This study presents the long-term static monitoring data acquired from the Dome of Santa Maria del Fiore in Florence. The monitoring system, operating since the late 1980s, includes displacement transducers and temperature sensors strategically placed along radial, meridian, and parallel directions of the dome, recording time series of over 54,000 samples, as well as force-balance accelerom eters measuring the structural response at four levels in three directions. The data was pre-processed accounting for various anomalies, including missing data, spikes, and shifts and then analysed from a statistical point of view considering the whole time series. The work highlights key challenges in handling anomalies and assessing data quality, and discusses the integration of multi-modal data, support ing the need for automated pre-processing frameworks and future incorporation of dynamic measurements into the structural assessments of the dome. |
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