000013382 001__ 13382
000013382 005__ 20161114160338.0
000013382 04107 $$aeng
000013382 046__ $$k2009-06-22
000013382 100__ $$aFragiadakis, M.
000013382 24500 $$aStructural reliability assessment with the aid of neural networks

000013382 24630 $$n2.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013382 260__ $$bNational Technical University of Athens, 2009
000013382 506__ $$arestricted
000013382 520__ $$2eng$$aA methodology based on Neural Networks is presented for the quick vulnerability assessment of steel moment resisting frames. The proposed method combines data extracted from ground motion records with a small number of nonlinear response history analyses to predict the seismic demand on a ten-storey moment-resisting steel frame. Having such a tool at our disposal we use Monte Carlo Simulation to calculate the conditional limit-state probabilities of the vulnerability curves. The framework we present allows taking into account uncertainties on both structural capacity and seismic demand with reduced computational cost. The use of Neural Networks is motivated by the approximate concepts inherent in the fragility assessment and the large number of time-consuming nonlinear response history analyses required for the accurate calculation of the probability of a limit-state being exceeded. The trained Neural Networks is used to obtain the level of seismic demand, which is expressed in terms of maximum interstorey drift.

000013382 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013382 653__ $$aReliability Analysis, Neural Networks, Structural Dynamics Analysis, Earthquake Engineering. Abstract. A methodology based on Neural Networks is presented for the quick vulnerability assessment of steel moment resisting frames. The proposed method combines data extracted from ground motion records with a small number of nonlinear response history analyses to predict the seismic demand on a ten-storey moment-resisting steel frame. Having such a tool at our disposal we use Monte Carlo Simulation to calculate the conditional limit-state probabilities of the vulnerability curves. The framework we present allows taking into account uncertainties on both structural capacity and seismic demand with reduced computational cost. The use of Neural Networks is motivated by the approximate concepts inherent in the fragility assessment and the large number of time-consuming nonlinear response history analyses required for the accurate calculation of the probability of a limit-state being exceeded. The trained Neural Networks is used to obtain the level of seismic demand, which is expressed in terms of maximum interstorey drift.

000013382 7112_ $$aCOMPDYN 2009 - 2nd International Thematic Conference$$cIsland of Rhodes (GR)$$d2009-06-22 / 2009-06-24$$gCOMPDYN2009
000013382 720__ $$aFragiadakis, M.$$iLagaros N., D.
000013382 8560_ $$ffischerc@itam.cas.cz
000013382 8564_ $$s339181$$uhttps://invenio.itam.cas.cz/record/13382/files/CD563.pdf$$yOriginal version of the author's contribution as presented on CD, section: Uncertainty analysis in structural dynamics and earthquake engineering - ii.
000013382 962__ $$r13074
000013382 980__ $$aPAPER