International Journal of Disaster Risk Reduction, volume 83, pages 103428

Fragility curves derivation for masonry buildings damaged after 2009 L'Aquila earthquake accounting for the effect of construction age

Publication typeJournal Article
Publication date2022-12-01
scimago Q1
SJR1.132
CiteScore8.7
Impact factor4.2
ISSN22124209
Building and Construction
Geotechnical Engineering and Engineering Geology
Geology
Safety Research
Abstract
The aim of this study is the analysis of seismic fragility of residential masonry buildings, with particular emphasis to the evolution of seismic behaviour over the years. To this purpose, a detailed and comprehensive taxonomy has been established, considering a restricted selection of building's features, avoiding the ineffective fragmentation of the database. The fragility assessment has been performed based on the data collected after the L'Aquila 2009 earthquake and made available by the Italian Department of Civil Protection through the online platform Da.D.O. (Database of Observed Damage). The database has undergone a refinement process to guarantee the completeness of the information, avoiding possible bias in the subsequent fitting procedure for fragility analysis. Thus, PGA (peak ground acceleration) from the ShakeMap has been used for ground motion characterization and 5 + 1 damage levels defined according to the European Macroseismic Scale have been considered for damage classification. Different regression models have been adopted to determine the parameters of lognormal fragility curves, measuring their goodness of fit with the observed damage probability matrices. Starting from the unconstrained model, further regression constraints (i.e., a common value for logarithmic standard deviation and the respect of the hierarchy of median PGA with the construction age) have been introduced, thus leading to the definition of the constrained model. The benefits in the introduction of further regression constraints are counterposed to the effectiveness of constrained curves to model observational data through the comparison of the goodness of fit between the unconstrained and constrained models.
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