Automated data analysis for static structural health monitoring of masonry heritage structures
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Masonry heritage structures are often affected by slow irreversible deterioration mechanisms that can jeopardise structural stability in the foreseeable future. Static structural health monitoring (SHM), aimed at the continuous measurement of key slow-varying parameters, has the potential to identify such mechanisms at a very early stage. This can greatly facilitate the implementation of adequate preventive and remedial measures, which can be critical to ensure that such structures are preserved for generations to come. However, because monitored parameters usually experience reversible seasonal variations of the same order of magnitude as changes caused by active mechanisms, identification of the latter is often a difficult task. This paper presents a fully integrated automated data analysis procedure for complete static SHM systems utilising dynamic linear regression models to filter out the effects caused by environmental variations. The method does not only produce estimated evolution rates but also classifies monitored responses in predefined evolution states. The procedure has successfully been used to identify vulnerable areas in two important medieval heritage structures in Spain, namely, the cathedral of Mallorca and the church of the monastery of Sant Cugat.
This is the peer reviewed version of the following article: [Makoond, N, Pelà, L, Molins, C, Roca, P, Alarcón, D. Automated data analysis for static structural health monitoring of masonry heritage structures. Struct Control Health Monit. 2020; 27:e2581. https://doi.org/10.1002/stc.2581], which has been published in final form at https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2581. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
CitationMakoond, N. [et al.]. Automated data analysis for static structural health monitoring of masonry heritage structures. "Structural control and health monitoring", Octubre 2020, vol. 27, núm. 10, p. e2581:1-e2581:25.
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