RC infilled building performance against the evidence of the 2016 EEFIT Central Italy post-earthquake reconnaissance mission: empirical fragilities and comparison with the FAST method

Abstract

Damage data on low-to-mid-rise Reinforced Concrete (RC) buildings, collected during the UK Earthquake Engineering Field Investigation Team post-earthquake reconnaissance mission on the August 24 Central Italy earthquake, are employed to derive empirical fragility relationships. Given the small dataset, the new data distributions are used for the Bayesian update of fragility functions derived for the L'Aquila earthquake (same seismic region and similar construction typologies). Other properties such as number of storeys, age of construction and shape in plan of the buildings are also analyzed. This information is employed to assess the ability of the FAST method to predict damage states in non-regular infilled RC buildings for the municipalities of Amatrice, Accumoli, Arquata del Tronto and Norcia, all severely affected by the 2016 Central Italy sequence. FAST is a spectral-based method to derive capacity curves and peak ground acceleration damage state thresholds for buildings. It is a dedicated methodology for regular RC frame buildings with masonry infills, first calibrated on damage data from the 2009 L'Aquila earthquake and applied to the 2011 Lorca (Spain), the 2012 Emilia (Italy) events for damage backanalyses. The new data from the August 2016 Central Italy earthquake provide a test-bed for FAST further employments in case of less homogenous building samples. The application of FAST presented here accounts for different shake-maps produced by both the United States Geological Survey and the Italian National Institute of Geophysics and Volcanology which are significantly different and representative of different refinements of the demand scenario. For the area of Amatrice, where the two shake-maps provide similar estimates and the buildings considered match reasonably well the typology for which FAST is calibrated, the comparison between damage level observed and as provided by

Empirical fragility assessment using conditional GMPE-based ground shaking fields: application to damage data for 2016 Amatrice Earthquake

“…Del Gaudio et al (2017) have derived empirical fragility curves based on the large database of post L'Aquila 2009 Earthquake damage surveys conducted by the Italian Civil Protection (Dolce and Goretti 2015) on RC buildings. De Luca et al (2018) have used the observed damage for infilled RC structures in the aftermath of the 24th August 2016 Amatrice Earthquake for Bayesian updating of existing empirical fragility curves for central Italy. Rosti et al (2018) have used the database of damage data collected after the 2009 L'Aquila (Italy) earthquake, to derive damage probability matrices for several building types representative of the Italian building stock.…”

Empirical seismic fragility models for Nepalese school buildings

“…Another aspect to be considered when deriving fragilities from observational data is the spatial distribution of the dataset. Several studies have shown that spatially inhomogeneous datasets (i.e., buildings concentrated in areas where the range of variation of PGA is limited) can lead to inconsistent results (De Luca et al 2018). In these cases, Bayesian updating procedure of existing fragility curves should be adopted (Singhal and Kiremidjian 1998;Miano et al 2016).…”

“…Bayesian Updating (BU) techniques are effective alternatives to traditional statistical methods when dealing with small or spatially inhomogeneous observational damage datasets. Several studies have suggested the adoption of Bayesian techniques to update preexisting fragility curves (e.g., Singhal and Kiremidjian 1998;Miano et al 2016;De Risi et al 2017;De Luca et al 2018). As mentioned in the introduction, a Bayesian approach has been also used by Didier et al (2017) in the context of Nepal, but the analysis was solely focused on residential buildings and did not include schools.…”

From rapid visual survey to multi-hazard risk prioritisation and numerical fragility of school buildings

“…Paulay and Priestley, 1992) and the commonly observed post-earthquake damage on RC structures (e.g. Elnashai and Di Sarno, 2008;Palermo et al, 2017;De Luca et al, 2018). For each of them, Table 3 provides guidance on the selection of the alternatives.…”