Seismic Fragility Curves for Rc Buildings at Territorial Scale

Abstract

The seismic events recently occurred in a different part of the world remind us once again the importance of reducing the risk of the national building stock. In order to properly invest the limited funds available for the retrofit of most exposed constructions, it is fundamental an estimate of the seismic vulnerability of the different building typologies. Such vulnerability is nowadays mainly expressed in terms of fragility curves, calibrated through post-seismic observed data or simulated data obtained by building mechanical models. In this paper, empirical fragility curves are presented for existing reinforced concrete buildings based on the post-earthquake observational data collected after the 2012 Emilia earthquake. This structural typology presents large variability in terms of seismic performance, depending on the seismic design level and other parameters such as the number of stories and the building typological classifications. Various regression models and statistical procedures have been proposed over the years to obtain fragility curves, hence more than one stochastic model is adopted, in order to point out how the fragility curves parameters change by varying the assumed probabilistic distribution.

Typological Damage Fragility Curves for Unreinforced Masonry Buildings affected by the 2009 L'Aquila, Italy Earthquake

“…The comparison in terms of fragility curves is proposed in Fig. (11). The distance between the two models is not as dramatic as in the study by Rota et al [3], also because this study and that done by Del Gaudio et al [10] are referred to the same earthquake.…”

“…The correlation in Eq. (11) shows the lowest coefficient of determination, because the amount of partitions can easily vary from building to building depending on erratic factors such as the size of structural masonry cells, the occurrence and the type of the staircase, the use of the building (no partitions are found in storage constructions). However, the impact of this uncertainty on the building damage state is limited given the small size of this surface in comparison to that of vertical structures (Eq.…”

“…In the literature, there are many models, empirical [1 -3] or mechanical [4,5], for the estimation of damage due to earthquakes by means of the definition of damage probability matrices [6 -9] or fragility curves [10,11,8]. The empirical methods were developed since the 1970s [12], based on the observed damage surveyed after earthquakes, and considering the definition of typological classes [3,10,11] or a vulnerability index as a function of different structural parameters [6, 13 -16, 9]. The analytical or mechanical models have been largely developed in the last few years as a consequence of increased computation capacity that allowed the definition of more complex numerical models [2,17,18].…”

Census-Based Typological Damage Fragility Curves and Seismic Risk Scenarios for Unreinforced Masonry Buildings

“…Among the different vulnerability models available in the literature, empirical fragility curves, based on observed damage, are largely widespread in Italy thanks to the recent publication of the data collected after the most relevant Italian earthquakes occurred in the last fifty years by the Civil Protection department throughout the DaDO (Observed Damage Database) platform [16,17]. Empirical methods were developed since the 1970s (e.g., [18]) and, in the last years, many models were developed for both reinforced concrete buildings [19][20][21][22][23] and unreinforced masonry buildings (URM) [1,[24][25][26][27][28][29][30] The empirical models require to define different aspects that can influence their effectiveness, among them the most relevant: (i) the selection of the intensity measure; (ii) the classification of the typological classes; (iii) the definition of a global damage index; and (iv) the probabilistic distribution adopted to fit the observed data.…”

“…The empirical models are defined for typological building classes [2,18,21,34] or considering a vulnerability index as a function of different structural parameters [25][26][27]35,36]. A high number of parameters leads to a more accurate estimation, but the information required are not always simple to get for a large-scale vulnerability assessment (e.g., [25,36]).…”