## 1 Introduction  
In the last three decades, several researches have been undertaken to address the seismic performance of structures through probabilistic procedures. A key issue in these methods is the definition of accurate fragility curves that identifies the vulnerability of structures to seismic damage. [PERSON] et al. [1] introduced the concept of damage probability matrices (DPM) to provide the probability that a particular level of damage will occur to a structural system, while the ground motion has been described by the modified Mercall intensity (MMI) scale. Thereafter, in 1991, the Applied Technology Council (ATC) and the Federal Emergency Management Agency (FEMA) introduced the use of vulnerability functions for lifelines in the ATC-25 report [2]. For this purpose, the concept of a continuous relation between the seismic damage and ground motion intensity is established and a regression analysis of different damage probability matrices is performed. In 1997, the risk assessment software package Hazard United States (HAZUS) was first introduced by the Federal Emergency Management Agency (FEMA). The HAZUS earthquake model consisted of an inventory data base, a ground motion model, an earthquake model of buildings, and lifelines damage models in order to evaluate the probability of damage to buildings and infrastructures according to ground shaking data from the U.S. Geological Survey (USGS) shake map [3]. Nowadays, fragility curves have been evolved as an efficient tool for retrofit and design of infrastructures [4]. Different types of fragility curves have been reported in the literature, namely expert-based opinion, experimental, analytical, hybrid, and empirical curves. Expert opinion fragility curves are developed based on statistics estimations of the probable damage when different earthquake intensities occur. ATC-13 [5] developed DPMs for typical California infrastructures using MMIs based on opinion of 42 experts. In 2007, [PERSON] et al. [6] developed vulnerability curves for Sofia's buildings stock and lifelines according to European Macro Seismic Scale (EMS98). Nevertheless, the reliability of this type of fragility curves is questionable due to uncertainties contain the experts' judgements [7]. Empirical fragility curves are developed based on damage observations from past earthquakes. [PERSON] et al. [8] assessed the vulnerability of bridges based on the observed data of Loma Prietta 1989, and [PERSON] 1994 developed empirical fragility curves through logistic regression. In 2003, [PERSON] and [PERSON] [7] developed empirical fragility curves based on a database of 99 post-earthquake damage distributions observed in 19 earthquakes of 340,000 RC buildings. Other researchers [9; 10; 11] have also used post-earthquake damage data to develop empirical curves. On the other hand, the experimental fragilities are based on experimental results. [PERSON] and [PERSON] [12] developed experimental fragility curves for bridge piers based on shake table and cyclic load tests. They correlate the damage state and the damage index to develop fragility functions. In the case of absence of damage data from real earthquakes, analytical fragility curves can be developed using results of structural demands from analytical simulations of non-linear static analysis [13; 14; 15], non-linear dynamic analysis [16; 17; 18; 19; 20; 21; 22; 23; 24; 25], and elastic spectral analysis [26; 27]. Finally, the hybrid method uses a combination of different procedures (available damage statistics and nonlinear analysis) for the prediction of the damage levels. [PERSON] et al. [28; 29] developed DPMs where part of intensity levels was constructed using available data from past earthquakes and results based on nonlinear dynamic analyses. In 2006, [PERSON] et al. [30] presented fragility curves for masonry and RC buildings combining statistical data derived from earthquake-damage Greek buildings and results of nonlinear static or dynamic analyses.