Seismic assessment of churches through integration of digital survey, texture recognition and distinct element modelling

The paper proposes an integrated methodology for the seismic assessment of masonry churches, which exploits the recent progresses of digital survey tools, image-processing algorithms for automatic texture identification, and non-linear seismic analysis. Starting with the digital survey of structural...

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Veröffentlicht in:Journal of Building Engineering 2024-12, Vol.98, p.111375, Article 111375
Hauptverfasser: Chura, Rodrigo Yanez, Meriggi, Pietro, Choueiri, Clarisse, de Felice, Gianmarco
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Sprache:eng
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Zusammenfassung:The paper proposes an integrated methodology for the seismic assessment of masonry churches, which exploits the recent progresses of digital survey tools, image-processing algorithms for automatic texture identification, and non-linear seismic analysis. Starting with the digital survey of structural macro-elements, which provides accurate information on geometry, defects and blocks arrangement, discrete element models are generated. Then, a pushover analysis is carried out, which allows to detect local collapse mechanisms and follow their development up to failure. The capacity curve of the macro-element is therefore obtained, including both ascending and descending branches. The effect of retrofitting measures is directly considered within the model and their efficacy evaluated in terms of variation in acceleration and displacement capacity. The proposed methodology is described step-by-step through the application to the case study of the Romanesque church of Santa Maria Maggiore in Tuscania, Italy. •An integrated methodology for seismic assessment of masonry churches is proposed.•Digital survey allows accurate detection of geometry, defects and blocks arrangement.•Watershed segmentation is used for texture recognition and discrete models generation.•An automatic pushover procedure is proposed for seismic assessment.•Collapse mechanisms are detected automatically, yielding a complete capacity curve.
ISSN:2352-7102
2352-7102
DOI:10.1016/j.jobe.2024.111375