Vulnerability analysis of large concrete dams using the continuum strong discontinuity approach and neural networks

Papadrakakis, M; Papadopoulos, V; Lagaros, ND; Oliver, J; Huespe, AE; Sanchez, P

dc.contributor.author	Papadrakakis, M	en
dc.contributor.author	Papadopoulos, V	en
dc.contributor.author	Lagaros, ND	en
dc.contributor.author	Oliver, J	en
dc.contributor.author	Huespe, AE	en
dc.contributor.author	Sanchez, P	en
dc.date.accessioned	2014-03-01T01:29:28Z
dc.date.available	2014-03-01T01:29:28Z
dc.date.issued	2008	en
dc.identifier.issn	0167-4730	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19276
dc.subject	Continuum Strong Discontinuity Approach	en
dc.subject	Fragility curves	en
dc.subject	Monte Carlo Simulation	en
dc.subject	Neural Networks	en
dc.subject	Reliability analysis	en
dc.subject	Soft computing	en
dc.subject	Vulnerability	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Bearing capacity	en
dc.subject.other	Computer simulation	en
dc.subject.other	Elastic moduli	en
dc.subject.other	Failure analysis	en
dc.subject.other	Finite element method	en
dc.subject.other	Fracture energy	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Neural networks	en
dc.subject.other	Poisson ratio	en
dc.subject.other	Tensile strength	en
dc.subject.other	Continuum strong discontinuity approach	en
dc.subject.other	Fragility curves	en
dc.subject.other	Vulnerability analysis	en
dc.subject.other	Concrete dams	en
dc.subject.other	Bearing capacity	en
dc.subject.other	Computer simulation	en
dc.subject.other	Concrete dams	en
dc.subject.other	Elastic moduli	en
dc.subject.other	Failure analysis	en
dc.subject.other	Finite element method	en
dc.subject.other	Fracture energy	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Neural networks	en
dc.subject.other	Poisson ratio	en
dc.subject.other	Tensile strength	en
dc.title	Vulnerability analysis of large concrete dams using the continuum strong discontinuity approach and neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.strusafe.2006.11.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.strusafe.2006.11.005	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Probabilistic analysis is an emerging field of structural engineering which is very significant in structures of great importance like dams, nuclear reactors etc. In this work a Neural Networks (NN) based Monte Carlo Simulation (MCS) procedure is proposed for the vulnerability analysis of large concrete dams, in conjunction with a non-linear finite element analysis for the prediction of the bearing capacity of the Dam using the Continuum Strong Discontinuity Approach. The use of NN was motivated by the approximate concepts inherent in vulnerability analysis and the time consuming repeated analyses required for MCS. The Rprop algorithm is implemented for training the NN utilizing available information generated from selected non-linear analyses. The trained NN is then used in the context of a MCS procedure to compute the peak load of the structure due to different sets of basic random variables leading to close prediction of the probability of failure. This way it is made possible to obtain rigorous estimates of the probability of failure and the fragility curves for the Scalere (Italy) dam for various predefined damage levels and various flood scenarios. The uncertain properties (modeled as random variables) considered, for both test examples, are the Young's modulus, the Poisson's ratio, the tensile strength and the specific fracture energy of the concrete. (C) 2006 Published by Elsevier Ltd.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Structural Safety	en
dc.identifier.doi	10.1016/j.strusafe.2006.11.005	en
dc.identifier.isi	ISI:000254867400004	en
dc.identifier.volume	30	en
dc.identifier.issue	3	en
dc.identifier.spage	217	en
dc.identifier.epage	235	en