Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks

Optimization of square-on-square double layer grids is beneficial for design purpose. For this purpose, use of a gradient based optimization algorithm incorporating stochastic feature called static perturbation stochastic approximation (SPSA) has not investigated. A computational procedure for cons...

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Main Author: Moghadas, Reza Kamyab
Format: Thesis
Language:English
Published: 2012
Subjects:
Online Access:http://eprints.usm.my/42441/1/REZA_KAMYAB_MOGHADAS.pdf
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spelling my-usm-ep.424412019-04-12T05:26:23Z Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks 2012-02 Moghadas, Reza Kamyab TA1-2040 Engineering (General). Civil engineering (General) Optimization of square-on-square double layer grids is beneficial for design purpose. For this purpose, use of a gradient based optimization algorithm incorporating stochastic feature called static perturbation stochastic approximation (SPSA) has not investigated. A computational procedure for constrained optimization of square-onsquare double layer grids combining FEM, SPSA algorithm and neural network has been formulated. Using the formulated procedures, a total of 208 set of optimization have been carried out on square-on-square double layer grids with different combinations of span L(25m~75m) and height h (0.035L~0.095L). Of the 208 sets of data, 173 and 35 have been used in the training and testing of radial basis function(RBF) and generalized regression(GR) neural networks for prediction of optimal design and the corresponding maximum deflection of square-on-square double layer grids with different spans and heights. 2012-02 Thesis http://eprints.usm.my/42441/ http://eprints.usm.my/42441/1/REZA_KAMYAB_MOGHADAS.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuteraan Awam
institution Universiti Sains Malaysia
collection USM Institutional Repository
language English
topic TA1-2040 Engineering (General)
Civil engineering (General)
spellingShingle TA1-2040 Engineering (General)
Civil engineering (General)
Moghadas, Reza Kamyab
Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks
description Optimization of square-on-square double layer grids is beneficial for design purpose. For this purpose, use of a gradient based optimization algorithm incorporating stochastic feature called static perturbation stochastic approximation (SPSA) has not investigated. A computational procedure for constrained optimization of square-onsquare double layer grids combining FEM, SPSA algorithm and neural network has been formulated. Using the formulated procedures, a total of 208 set of optimization have been carried out on square-on-square double layer grids with different combinations of span L(25m~75m) and height h (0.035L~0.095L). Of the 208 sets of data, 173 and 35 have been used in the training and testing of radial basis function(RBF) and generalized regression(GR) neural networks for prediction of optimal design and the corresponding maximum deflection of square-on-square double layer grids with different spans and heights.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Moghadas, Reza Kamyab
author_facet Moghadas, Reza Kamyab
author_sort Moghadas, Reza Kamyab
title Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks
title_short Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks
title_full Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks
title_fullStr Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks
title_full_unstemmed Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks
title_sort optimization of double layer grid structures using fem, spsa and neural networks
granting_institution Universiti Sains Malaysia
granting_department Pusat Pengajian Kejuteraan Awam
publishDate 2012
url http://eprints.usm.my/42441/1/REZA_KAMYAB_MOGHADAS.pdf
_version_ 1747821067542462464