Efficient methodology for seismic fragility curves estimation by active learning on Support Vector Machines

Fragility curves which express the failure probability of a structure as function of a loading intensity measure are nowadays widely used to facilitate the design and decision making of structures/infrastructures against seismic hazard (and possibly other natural hazards), with analysis procedures specified by Seismic Probabilistic Risk Assessment, Performance-Based Earthquake Engineering, and other frameworks. To avoid the use of parametric models (such as the lognormal model) to estimate fragility curves from a reduced number of numerical calculations, a methodology based on Support Vector Machines (SVMs) coupled with an active learning algorithm is proposed in this paper. In practice, input excitation is reduced to some relevant parameters and then SVMs are used for a binary classification of the structural responses relative to a limit threshold of exceedance. Since the output is not binary but a real-valued 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.