Static structural health monitoring and automated data analysis procedures applied to the diagnosis of a complex medieval masonry monastery

Abstract

Static structural health monitoring (SHM), aimed at the continuous measurement of slow-varying parameters over a long period, has been proved to be a powerful tool to support the diagnosis of masonry heritage structures. In such applications, the initial interpretation task involves the identification of evolutionary conditions from recorded data. However, this can be difficult since monitored features are influenced by environmental changes. In addition, many masonry heritage structures are characterised by a complex structural behaviour stemming from the interaction among different elements, making the task of interpreting SHM data for diagnosis very challenging. One such structure is the church of the monastery of Sant Cugat close to Barcelona, built mostly between the 12th and 15th centuries. Certain key structural parameters of the church have been monitored since 2017 with the aim of understanding the cause of visible pathologies and identifying any active deterioration mechanisms that could pose a threat to the structural integrity of the church in the future. This paper presents the application of an automated data analysis methodology to this problem. The method uses dynamic regression models to filter out components related to reversible seasonal fluctuations from measurements and automatically classifies monitored parameters into evolutionary states based on predicted evolution rates and dispersion metrics from the filtering procedure. A tool is presented which allows analysis results to be updated as new data is received. Finally, results from the proposed methodology are used for the diagnosis of the structure and their usefulness in a broader decision-making framework is discussed.

A time domain approach for data interpretation from long‐term static monitoring of historical structures

“…In 2020, Makoond et al 25 have presented an automated analysis methodology to interpret the data obtained from the monitoring system installed on the Monastery of Sant Cugat in Barcellona (Spain). The method uses dynamic regression models to filter out from the recorded data the seasonal components and allows to evaluate possible evolutionary states based on predicted evolution rates and dispersion metrics from the filtering procedure.…”