Empirical fragility curves for masonry buildings after the 2009 L’Aquila, Italy, earthquake

Gaudio, Carlo Del; Martino, Giuseppina De; Ludovico, Marco Di; Manfredi, Gaetano; Prota, Andrea; Ricci, Paolo; Verderame, Gerardo Mario

doi:10.1007/s10518-019-00683-4

Cited by 94 publications

(53 citation statements)

References 27 publications

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“…Concerning empirical fragility curves, consisting in an upgraded model of damage probability matrices early developed in Italy in the eighties (Braga et al 1982, 1983, 1986, Dolce 1984, different models were proposed for M and RC building typologies in Italy, e.g. (Di Pasquale et al 2005;Rota et al 2008;Del Gaudio et al 2019, 2020 is an exemplifying list of some available proposals in the literature. Also, analytical methods were employed to derive analytical based fragility curves such as the ones for M (e.g., D'Ayala and Speranza 2003;Rota et al 2010) and RC buildings (Polese et al 2008;Del Gaudio et al 2018).…”

Section: Evolution Of National Seismic Risk Maps In Italymentioning

confidence: 99%

Seismic risk assessment of residential buildings in Italy

Dolce

Prota

Borzi

et al. 2020

Bull Earthquake Eng

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182

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The last National Risk Assessment NRA for Italy was developed at the end of 2018 by the Department of Civil Protection (DPC) in response to the specific requirement of the Sendai Framework for Disaster Risk Reduction 2015–2030 to periodically adjourn the assessment of disaster risk. The methodology adopted to perform seismic risk assessment and build national seismic risk maps was specifically developed to comply with the recent Code for Civil Protection, issuing that, in addition to a solid scientific base, risk assessment should be characterized by a wide consensus of the scientific community. As a result, six research units belonging to two Centers of Competence of the DPC, namely ReLUIS (Network of university laboratories for seismic engineering) and EUCENTRE (European Centre for Training and Research in Earthquake Engineering), collaborated under the guidance and coordination of DPC to produce the recent updating of national seismic risk maps for the residential building stock. This paper describes the methodology adopted to develop the consensus-based national seismic risk assessment and presents the main results in terms of expected damage and impact measures (unusable buildings, homeless, casualties, direct economic losses).

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Section: Evolution Of National Seismic Risk Maps In Italymentioning

confidence: 99%

Seismic risk assessment of residential buildings in Italy

Dolce

Prota

Borzi

et al. 2020

Bull Earthquake Eng

Self Cite

182

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“…Empirical fragility functions are derived directly from the post-earthquake damage data (Del Gaudio et al 2017, 2019a, 2019bRosti et al 2019;Bertelli et al 2018) and, at a first glance, it is assumed they represent the actual behavior of the real buildings that formed the building class. Mechanical-based methods require the idealization of the building class through one or more archetype buildings, to be investigated through numerical models (usually nonlinear dynamic analyses, through: incremental dynamic analysis-IDA, cloud method, multiple-stripe analysis-MSA) or analytical simplified formulations, which depends on few relevant parameters (available in the inventory for the whole building stock), for example: Bernardini et al (1990), D'Ayala et al (1997, Calvi (1999), Glaister and Pihno (2003), Restrepo and Magenes (2004), D'Ayala (2005), Borzi et al (2008), Molina et al (2009), Oropeza et al (2010), Lagomarsino and Cattari (2013), Rota et al (2010), Erberik (2008), Gehl et al (2013), Donà et al (2019).…”

Section: Introductionmentioning

confidence: 99%

The heuristic vulnerability model: fragility curves for masonry buildings

Lagomarsino

Cattari

Ottonelli

2021

Bull Earthquake Eng

114

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In the framework of seismic risk analyses at large scale, among the available methods for the vulnerability assessment the empirical and expert elicitation based ones still represent one of most widely used options. In fact, despite some drawbacks, they benefit of a direct correlation to the actual seismic behaviour of buildings and they are easy to handle also on huge stocks of buildings. Within this context, the paper illustrates a macroseismic vulnerability model for unreinforced masonry existing buildings that starts from the original proposal of Lagomarsino and Giovinazzi (Bull Earthquake Eng 4(4):445–463, 2006) and has further developed in recent years. The method may be classified as heuristic, in the sense that: (a) it is based on the expertise that is implicit in the European Macroseismic Scale (EMS98), with fuzzy assumptions on the binomial damage distribution; (b) it is calibrated on the observed damage in Italy, available in the database Da.D.O. developed by the Italian Department of Civil Protection (DPC). This approach guarantees a fairly well fitting with actual damage but, at the same time, ensures physically consistent results for both low and high values of the seismic intensity (for which observed data are incomplete or lacking). Moreover, the method provides a coherent distribution between the different damage levels. The valuable data in Da.D.O. allowed significant improvements of the method than its original version. The model has been recently applied in the context of ReLUIS project, funded by the DPC to support the development of Italian Risk Maps. To this aim, the vulnerability model has been applied for deriving fragility curves. This step requires to introduce a correlation law between the Macroseismic Intensity (adopted for the calibration of the model from a wide set of real damage data) and the Peak Ground Acceleration (at present, one of most used instrumental intensity measures); this conversion further increases the potential of the macroseismic method. As presented in the paper, the first applications of the model have produced plausible and consistent results at national scale, both in terms of damage scenarios and total risk (economic loss, consequences to people).

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“…Observational fragilities for masonry school buildings are calculated with the Maximum Likelihood Estimation (MLE) method. MLE is one of the most widely adopted techniques for deriving empirical fragility curves and it has been used to assess the performance of numerous structural types in different regional contexts (Shinozuka et al 2000;Colombi et al 2008;De Luca et al 2015;Del Gaudio et al 2019). The first step of MLE consists of subdividing the dataset in ranges of PGA (bins) for which the Damage State exceedance probabilities are computed (Fig.…”

Section: Empirical Fragility Estimates For Masonry School Buildingsmentioning

confidence: 99%

Empirical seismic fragility models for Nepalese school buildings

et al. 2020

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Empirical vulnerability models are fundamental tools to assess the impact of future earthquakes on urban settlements and communities. Generally, they consist of sets of fragility curves that are derived from georeferenced post-earthquake damage data. Following the 2015 Nepal earthquake sequence, the World Bank, through the Global Program for Safer Schools, conducted a Structural Integrity and Damage Assessment (SIDA) of about 18,000 school buildings in the earthquake-affected area. In this work, the database is utilized to identify the main structural characteristics of the Nepalese school building stock. For the first time, extended SIDA school damage data is processed to derive fragility curves for the main structural typologies. Data sets for each structural typology are used for a Bayesian updating of existing fragilities to obtain regional models for Nepalese schools. These fragility estimates can be adopted to assess potential seismic losses of the school infrastructure in Nepal. Additionally, they can be used for calibrating loss assessment studies in the wider Himalayan region where the structural typologies are similar.

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Empirical fragility curves for masonry buildings after the 2009 L’Aquila, Italy, earthquake

Cited by 94 publications

References 27 publications

Seismic risk assessment of residential buildings in Italy

Seismic risk assessment of residential buildings in Italy

The heuristic vulnerability model: fragility curves for masonry buildings

Empirical seismic fragility models for Nepalese school buildings

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