Statistical correlation between environmental time series and data from long-term monitoring of buildings

Within the context of civil structures, a monitoring system supported by an intelligent diagnostic features extraction allows to keep under observation the overall health state of a building. In most cases, the diagnostic features are influenced by Environmental and Operational Variations (EOVs) which cause fluctuations that can be confused with the appearance of damage, or worse, hide it. A useful strategy to get rid of those confounding effects consists in modelling the structural behaviour of the system, considering and pre-dicting these harmless and reversible fluctuations. However, a model approximates a much more complex reality and therefore it is based on a reasonable number of components whose selection might turn out complicated. In this research, a large amount of heteroge-neous field data is systematically analysed to investigate which have the greatest influence on structural behavior and therefore, could contribute for modelling the behaviour of a his-toric building for Structural Health Monitoring (SHM) purpose. Environmental data, mea-surements of static sensors and modal natural frequencies collected in more than 10 years are scanned and crossed in order to discover any correlations. The analysis of these time series, treated with mathematical and statistical tools, has led to some mechanical inter-pretations of the observed behaviour of the system, i.e. the Sanctuary of Vicoforte, a mon-umental Italian church which houses the largest masonry oval dome in the world. The results obtained, especially in terms of correlations between different factors affecting measurements, are deemed relevant in the practice of long-term monitoring of cultural heritage and existing buildings in general. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This research systematically analyzes a large amount of heterogeneous field data to investigate the factors that have the greatest influence on structural behavior, which could contribute to modeling the behavior of historic buildings for Structural Health Monitoring (SHM) purposes.