Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines
This work addresses the need for more efficient and non-parametric fragility curve estimation in seismic risk assessment, though it appears incremental as it builds on existing SVM and active learning techniques.
The paper tackles the problem of estimating seismic fragility curves from limited numerical simulations by proposing a method that combines Support Vector Machines with active learning, achieving efficient estimation without relying on parametric models like the lognormal model.
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.