Computationally efficient seismic fragility analysis of geostructuresComputationally efficient seismic fragility analysis of geostructures Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-10-082009enSeismic fragility analysis is considered nowadays as a very efficient computational tool for determining the structural behaviour over a range of seismic intensity levels. 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 for a variety of seismic hazard levels. The Monte Carlo simulation (MCS) technique is regarded as the most consistent reliability analysis method having no limitations regarding its applicability range. However, the required computational effort is the only limitation which increases substantially when implemented for calculating lower probabilities. Incorporating artificial neural networks (ANN) into the fragility analysis framework enhances the computational efficiency of MCS, since ANN require a fraction of time compared to the conventional procedure. In this work two types of ANN are implemented into a MCS-based vulnerability analysis framework of geostructures, where the randomness of material properties, geometry and of the pseudostatically imposed seismic loading is considered. http://creativecommons.org/licenses/by/4.0/Computers and Structures19-20871195-1203 Nikos Lagaros Yiannis Tsompanakis Prodromos Psarropoulos Evaggelos C. Georgopoulos Seismic fragility analysis Regions, Seismically active Seismic areas Seismic zones Seismically active regions Zones, Earthquake Zones, Seismic earthquake zones regions seismically active seismic areas seismic zones seismically active regions zones earthquake zones seismic