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Investigating energy absorption accessible by plastic deformation of a seismic damper using artificial neural network

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dc.contributor.author Paygozar, Bahman
dc.date.accessioned 2021-02-01T10:22:55Z
dc.date.available 2021-02-01T10:22:55Z
dc.date.issued 2019-08
dc.identifier.issn 2452-3216
dc.identifier.uri https://doi.org/10.1016/j.prostr.2019.12.095
dc.identifier.uri http://hdl.handle.net/20.500.12485/743
dc.description.abstract In this study a seismic damper of high-rise structures is analyzed parametrically using artificial neural network (ANN). The input data for ANN model was generated using experimentally validated finite element (FE) analyses. The study investigates the amount of the absorbed energy dissipated by the plastic deformation of the tubes involved in the damper. The network used in this study computes the absorbed energy of the damping system in terms of three different variables including diameter ratio, the thickness and the diameter of the outer tube. To train the network, 90% of the FE results are utilized as input, and the capability of the network is examined by the rest 10% of data. It is shown that the trained neural structure can estimate the energy dissipation with an error less than 2%. According to the results, it is observed that despite the diameter, increasing in the thickness of the outer tube improves the energy absorption measurably. The results also show that the model with the diameter ratio of 1.6, as a critical design parameter, reflects the optimum absorbed energy among all cases. en_US
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.subject Artificial neural network en_US
dc.subject Energy absorption capacity en_US
dc.subject Hysteresis effect en_US
dc.subject Parametric study en_US
dc.title Investigating energy absorption accessible by plastic deformation of a seismic damper using artificial neural network en_US
dc.type Proceedings Paper en_US
dc.relation.journal Procedia Structural Integrity en_US
dc.identifier.startpage 138 en_US
dc.identifier.endpage 145 en_US
dc.identifier.volume 21 en_US


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