Back analysis of microplane model parameters using soft computing methods

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dc.creatorKucerova, A.
dc.creatorLeps, M.
dc.creatorZeman, J.
dc.date2009-02-10
dc.date.accessioned2026-07-07T12:39:54Z
dc.date.available2026-07-07T12:39:54Z
dc.descriptionA new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed.
dc.description21 pages, 27 figures, 7 tables
dc.identifierhttps://arxiv.org/abs/0902.1690
dc.identifierhttp://arxiv.org/abs/0902.1690
dc.identifierCAMES: Computer Assisted Mechanics and Engineering Sciences, 14 (2), 219-242, 2007
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/219284
dc.subjectNeural and Evolutionary Computing
dc.subjectArtificial Intelligence
dc.subjectI.2.6
dc.titleBack analysis of microplane model parameters using soft computing methods
dc.typetext

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