Documents disponibles écrits par cet auteur

Dynamic analysis of nonlinear frames by prandtl neural networks / Abdolreza Joghataie in Journal of engineering mechanics, Vol. 134 n°11 (Novembre 2008) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 134 n°11 (Novembre 2008) . - pp.961–969. Titre : Dynamic analysis of nonlinear frames by prandtl neural networks Type de document : texte imprimé Auteurs : Abdolreza Joghataie, Auteur ; Mojtaba Farrokh, Auteur Année de publication : 2008 Article en page(s) : pp.961–969. Note générale : Mécanique appliquée Langues : Anglais (eng) Mots-clés : Neural  networks  Algorithms  Nonlinear  analysis  Hysteresis  Frames  Ground  motion  Dynamic  analysis Résumé : A new type of activation function, based on the use of the Prandtl–Ishlinskii operator, has been developed and used in the feed forward neural networks in order to improve their capabilities in learning to identify and analyze nonlinear structures subject to dynamic loading. The genetic algorithm has been used in its training. The neural network, which is referred to as the Prandtl neural network here, has been trained and used in the analysis of two shear frames, a single degree of freedom (SDOF) and a 3DOF, both subjected to earthquake excitations. To assess the capabilities of the Prandtl neural network under ideal situations, the data on the response of the frames have been obtained through the integration of their governing nonlinear equations of motion. The training has been based on the white noise while the strong earthquakes of 200% El Centro in 1940 and Gilroy have been used for testing. Results have shown the high precision of the Prandtl neural network in solving highly hysteretic problems. The issue is important for two main applications in structural dynamics and control: (1) analysis of highly nonlinear structures where it is desired to train a neural network to directly learn the behavior of a structure from experimental data; and (2) intelligent active control of structures where neural network emulators are designed to provide as precise predictions about the future response of the structures as possible, in order to be used in the determination of the required control forces. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282008%29134%3A11%289 [...] [article] Dynamic analysis of nonlinear frames by prandtl neural networks [texte imprimé] / Abdolreza Joghataie, Auteur ; Mojtaba Farrokh, Auteur . - 2008 . - pp.961–969. Mécanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 134 n°11 (Novembre 2008) . - pp.961–969. Mots-clés : Neural  networks  Algorithms  Nonlinear  analysis  Hysteresis  Frames  Ground  motion  Dynamic  analysis Résumé : A new type of activation function, based on the use of the Prandtl–Ishlinskii operator, has been developed and used in the feed forward neural networks in order to improve their capabilities in learning to identify and analyze nonlinear structures subject to dynamic loading. The genetic algorithm has been used in its training. The neural network, which is referred to as the Prandtl neural network here, has been trained and used in the analysis of two shear frames, a single degree of freedom (SDOF) and a 3DOF, both subjected to earthquake excitations. To assess the capabilities of the Prandtl neural network under ideal situations, the data on the response of the frames have been obtained through the integration of their governing nonlinear equations of motion. The training has been based on the white noise while the strong earthquakes of 200% El Centro in 1940 and Gilroy have been used for testing. Results have shown the high precision of the Prandtl neural network in solving highly hysteretic problems. The issue is important for two main applications in structural dynamics and control: (1) analysis of highly nonlinear structures where it is desired to train a neural network to directly learn the behavior of a structure from experimental data; and (2) intelligent active control of structures where neural network emulators are designed to provide as precise predictions about the future response of the structures as possible, in order to be used in the determination of the required control forces. ISSN : 0733-9399 En ligne : http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399%282008%29134%3A11%289 [...]

Transforming results from model to prototype of concrete gravity dams using neural networks / Abdolreza Joghataie in Journal of engineering mechanics, Vol. 137 N° 7 (Juillet 2011) 

Public ISBD [article]  in Journal of engineering mechanics > Vol. 137 N° 7 (Juillet 2011) . - pp.484-496 Titre : Transforming results from model to prototype of concrete gravity dams using neural networks Type de document : texte imprimé Auteurs : Abdolreza Joghataie, Auteur ; Mehrdad Shafiei Dizaji, Auteur Année de publication : 2011 Article en page(s) : pp.484-496 Note générale : Mécanique appliquée Langues : Anglais (eng) Mots-clés : Neurotransformer  Linear  scaling  Dam  model  Koyna  Dam  Pine  Flat  Dam Résumé : A new method using neural networks for the transformation of results from dam models to prototypes has been proposed and validated through application to Koyna and Pine-Flat Dams, which have also been investigated by other researchers. The neural network has been called the neurotransformer. The common method for building a suitable experimental model for a dam to be tested on a shaking table is linear dimensional analysis or simply linear scaling (LS). However, because LS is theoretically applicable to linear systems, it generally provides imprecise results of transformation for extreme loading when the model or the prototype experiences noticeable nonlinearity. In this paper, it is shown through numerical simulation of the dynamic behaviour of Koyna Dam and its 1/50 model under strong earthquakes, which cause nonlinear behavior in both the dam and its model, that transformation by neural networks is considerably more precise than LS. To show the method can also be applied to other dams, the same procedure was successfully applied to Pine-Flat Dam; again, the neurotransformer outperformed the LS. DEWEY : 620.1 ISSN : 0733-9399 En ligne : http://ascelibrary.org/emo/resource/1/jenmdt/v137/i7/p484_s1?isAuthorized=no [article] Transforming results from model to prototype of concrete gravity dams using neural networks [texte imprimé] / Abdolreza Joghataie, Auteur ; Mehrdad Shafiei Dizaji, Auteur . - 2011 . - pp.484-496. Mécanique appliquée Langues : Anglais (eng) in Journal of engineering mechanics > Vol. 137 N° 7 (Juillet 2011) . - pp.484-496 Mots-clés : Neurotransformer  Linear  scaling  Dam  model  Koyna  Dam  Pine  Flat  Dam Résumé : A new method using neural networks for the transformation of results from dam models to prototypes has been proposed and validated through application to Koyna and Pine-Flat Dams, which have also been investigated by other researchers. The neural network has been called the neurotransformer. The common method for building a suitable experimental model for a dam to be tested on a shaking table is linear dimensional analysis or simply linear scaling (LS). However, because LS is theoretically applicable to linear systems, it generally provides imprecise results of transformation for extreme loading when the model or the prototype experiences noticeable nonlinearity. In this paper, it is shown through numerical simulation of the dynamic behaviour of Koyna Dam and its 1/50 model under strong earthquakes, which cause nonlinear behavior in both the dam and its model, that transformation by neural networks is considerably more precise than LS. To show the method can also be applied to other dams, the same procedure was successfully applied to Pine-Flat Dam; again, the neurotransformer outperformed the LS. DEWEY : 620.1 ISSN : 0733-9399 En ligne : http://ascelibrary.org/emo/resource/1/jenmdt/v137/i7/p484_s1?isAuthorized=no