Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2018

Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines

Résumé

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) inSeismic Probabilistic Risk Assessment studies, (ii) to evaluate impact of constructiondetails on the structural performance of installations under seismic excitationsor under other loading sources such as wind. To avoid the use of parametric modelssuch as lognormal model to estimate fragility curves from a reduced number ofnumerical calculations, a methodology based on Support Vector Machines coupledwith an active learning algorithm is proposed in this paper. In practice, input excitationis reduced to some relevant parameters and, given these parameters, SVMs areused for a binary classification of the structural responses relative to a limit thresholdof exceedance. Since the output is not only binary, this is a score, a probabilisticinterpretation of the output is exploited to estimate very efficiently fragility curvesas score functions or as functions of classical seismic intensity measures.
Fichier principal
Vignette du fichier
201800002501.pdf (1.68 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

cea-02339421 , version 1 (20-11-2019)

Identifiants

Citer

Rémi Sainct, Cyril Feau, Jean-Marc Martinez, Josselin Garnier. Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines. 2018. ⟨cea-02339421⟩
79 Consultations
104 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More