  Fragility curves which express the failure probability of a structure, or
critical components, as function of a loading intensity measure are nowadays
widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii) to
evaluate impact of construction details on the structural performance of
installations under seismic excitations or under other loading sources such as
wind. To avoid the use of parametric models such as lognormal model to estimate
fragility curves from a reduced number of numerical calculations, a methodology
based on Support Vector Machines coupled with an active learning algorithm is
proposed in this paper. In practice, input excitation is reduced to some
relevant parameters and, given these parameters, SVMs are used for a binary
classification of the structural responses relative to a limit threshold of
exceedance. Since the output is not only binary, this is a score, a
probabilistic interpretation of the output is exploited to estimate very
efficiently fragility curves as score functions or as functions of classical
seismic intensity measures.
