Computationally efficient seismic fragility analysis of geostructures

Lagaros, ND; Tsompanakis, Y; Psarropoulos, PN; Georgopoulos, EC

dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Tsompanakis, Y	en
dc.contributor.author	Psarropoulos, PN	en
dc.contributor.author	Georgopoulos, EC	en
dc.date.accessioned	2014-03-01T01:30:00Z
dc.date.available	2014-03-01T01:30:00Z
dc.date.issued	2009	en
dc.identifier.issn	0045-7949	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19449
dc.subject	Artificial neural networks	en
dc.subject	Geostructures	en
dc.subject	Monte Carlo simulation	en
dc.subject	Seismic fragility analysis	en
dc.subject	Slope stability	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Artificial Neural Network	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Computational effort	en
dc.subject.other	Computational tools	en
dc.subject.other	Computationally efficient	en
dc.subject.other	Damage state	en
dc.subject.other	Fragility analysis	en
dc.subject.other	Fragility curves	en
dc.subject.other	Geostructures	en
dc.subject.other	Log-normal distribution	en
dc.subject.other	Lower probabilities	en
dc.subject.other	Material property	en
dc.subject.other	Monte Carlo simulation	en
dc.subject.other	Probability of exceedance	en
dc.subject.other	Reliability analysis method	en
dc.subject.other	Seismic fragility	en
dc.subject.other	Seismic fragility analysis	en
dc.subject.other	Seismic hazards	en
dc.subject.other	Seismic intensity	en
dc.subject.other	Seismic loadings	en
dc.subject.other	Structural behaviour	en
dc.subject.other	Structural response	en
dc.subject.other	Vulnerability analysis	en
dc.subject.other	Backpropagation	en
dc.subject.other	Computational efficiency	en
dc.subject.other	Computer simulation languages	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Neural networks	en
dc.subject.other	Probability distributions	en
dc.subject.other	Quality assurance	en
dc.subject.other	Risk assessment	en
dc.subject.other	Seismology	en
dc.subject.other	Slope stability	en
dc.subject.other	Structural analysis	en
dc.subject.other	Reliability analysis	en
dc.title	Computationally efficient seismic fragility analysis of geostructures	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.compstruc.2008.12.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.compstruc.2008.12.001	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Seismic 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. (C) 2008 Civil-Comp Ltd. and Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Computers and Structures	en
dc.identifier.doi	10.1016/j.compstruc.2008.12.001	en
dc.identifier.isi	ISI:000273891200004	en
dc.identifier.volume	87	en
dc.identifier.issue	19-20	en
dc.identifier.spage	1195	en
dc.identifier.epage	1203	en