000011937 001__ 11937
000011937 005__ 20141205160002.0
000011937 04107 $$aeng
000011937 046__ $$k2008-10-12
000011937 100__ $$aTsompanakis, Yiannis
000011937 24500 $$aSeismic Vulnerability Assessment of Large-Scale Geostructures

000011937 24630 $$n14.$$pProceedings of the 14th World Conference on Earthquake Engineering
000011937 260__ $$b
000011937 506__ $$arestricted
000011937 520__ $$2eng$$aSeismic vulnerability analysis of structural and infrastructural systems is commonly performed by means of fragility curves. There are two approaches for developing fragility curves, either based on the assumption that the structural response follows the lognormal distribution or using reliability analysis techniques for calculating the probability of exceedance for various damage states and seismic hazard intensity levels. The Monte Carlo Simulation (MCS) technique is considered as the most consistent reliability analysis method having no limitations regarding its applicability range. Nevertheless, the only limitation imposed is the required computational effort, which increases substantially when implemented for calculating lower probabilities. Incorporating artificial neural networks (ANN) into the vulnerability analysis framework enhances the computational efficiency of MCS, since ANN require a fraction of time compared to the conventional procedure. Thus, ANN offer a precise and efficient way to determine a geostructure’s seismic vulnerability for multiple hazard levels and multiple limit states.

000011937 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000011937 653__ $$aslope stability, fragility curves, neural networks, Monte Carlo simulation

000011937 7112_ $$a14th World Conference on Earthquake Engineering$$cBejing (CN)$$d2008-10-12 / 2008-10-17$$gWCEE15
000011937 720__ $$aTsompanakis, Yiannis$$iLagaros, Nikos D.$$iPsarropoulos, Prodroms N.$$iGeorgopoulos, Evaggelos C.
000011937 8560_ $$ffischerc@itam.cas.cz
000011937 8564_ $$s253326$$uhttps://invenio.itam.cas.cz/record/11937/files/04-02-0045.pdf$$yOriginal version of the author's contribution as presented on CD, Paper ID: 04-02-0045.
000011937 962__ $$r9324
000011937 980__ $$aPAPER