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Vibration Monitoring of an Existing Masonry Building Under Demolition

 Vibration Monitoring of an Existing Masonry Building Under Demolition
Auteur(s): , , ,
Présenté pendant IABSE Symposium: Synergy of Culture and Civil Engineering – History and Challenges, Wrocław, Poland, 7-9 October 2020, publié dans , pp. 585-592
DOI: 10.2749/wroclaw.2020.0585
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The loss potential of earthquakes is substantial even for sites of moderate seismicity, due to the disastrous consequences of rare events. Large parts of the existing European building stock, mainl...
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Détails bibliographiques

Auteur(s):



Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Symposium: Synergy of Culture and Civil Engineering – History and Challenges, Wrocław, Poland, 7-9 October 2020
Publié dans:
Page(s): 585-592 Nombre total de pages (du PDF): 8
Page(s): 585-592
Nombre total de pages (du PDF): 8
Année: 2020
DOI: 10.2749/wroclaw.2020.0585
Abstrait:

The loss potential of earthquakes is substantial even for sites of moderate seismicity, due to the disastrous consequences of rare events. Large parts of the existing European building stock, mainly masonry structures, do not fulfil the current seismic standards, while many buildings have long exceeded their design lifespan. Given the inherent uncertainties of masonry as a composite material, unknown effects of ageing, and the corresponding difficulty to estimate the nonlinear response of such structures, data-driven health monitoring provides an efficient way to reduce epistemic uncertainty and to derive damage-sensitive features for structural assessment after strong ground motions. In this study, vibrational recordings during the demolition of a real masonry building have been analyzed. The accumulating damage during demolition provides a valuable insight into the correlation between dynamic response and structural health. The findings shed light on the performance of a typical masonry structure, built in the 19th century, under non-conventional loading and form a step towards the definition of damage-sensitive features based on real data.