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...

詳細記述

書誌詳細
第一著者: Moghadas, Reza Kamyab
フォーマット: 学位論文
言語:英語
出版事項: 2012
主題:
オンライン・アクセス:http://eprints.usm.my/42441/
Abstract Abstract here
_version_ 1855629683691880448
author Moghadas, Reza Kamyab
author_facet Moghadas, Reza Kamyab
author_sort Moghadas, Reza Kamyab
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.
first_indexed 2025-10-17T08:19:12Z
format Thesis
id usm-42441
institution Universiti Sains Malaysia
language English
last_indexed 2025-10-17T08:19:12Z
publishDate 2012
record_format EPrints
record_pdf Restricted
spelling usm-424412019-04-12T05:26:23Z http://eprints.usm.my/42441/ Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks 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 NonPeerReviewed application/pdf en http://eprints.usm.my/42441/1/REZA_KAMYAB_MOGHADAS.pdf Moghadas, Reza Kamyab (2012) Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks. PhD thesis, Universiti Sains Malaysia.
spellingShingle TA1-2040 Engineering (General). Civil engineering (General)
Moghadas, Reza Kamyab
Optimization Of Double Layer Grid Structures Using FEM, SPSA And Neural Networks
thesis_level PhD
title 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_short 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
topic TA1-2040 Engineering (General). Civil engineering (General)
url http://eprints.usm.my/42441/
work_keys_str_mv AT moghadasrezakamyab optimizationofdoublelayergridstructuresusingfemspsaandneuralnetworks