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Static structural health monitoring and automated data analysis procedures applied to the diagnosis of a complex medieval masonry monastery
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.
Static structural health monitoring and automated data analysis procedures applied to the diagnosis of a complex medieval masonry monastery
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.
Static structural health monitoring and automated data analysis procedures applied to the diagnosis of a complex medieval masonry monastery
Makoond, Nirvan (author) / Pelà, Luca (author) / Molins, Climent (author) / Roca, Pere (author)
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2020 ; 2020 ; Online Only,California,United States
Proc. SPIE ; 11379
2020-04-23
Conference paper
Electronic Resource
English
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