Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge

In this paper a new optimized active control algorithm based on combination of fuzzy neural network and a new highly efficient meta-heuristic population based optimization method extracted from Tug of War competition presents under different earthquake loads. The Efficiency of the proposed control m...

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Autores principales:	Mostafa Ghelichi, Alireza Mirza Goltabar Roshan, Hamidreza Tavakoli, Abbas Karamodin
Formato:	article
Lenguaje:	FA
Publicado:	Iranian Society of Structrual Engineering (ISSE) 2021
Materias:	active control tug of war anfis neural network benchmark bridge lyapunov stability Bridge engineering TG1-470 Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/75c0bd0464974b669e1f46b79fae438a
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id	oai:doaj.org-article:75c0bd0464974b669e1f46b79fae438a
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spelling	oai:doaj.org-article:75c0bd0464974b669e1f46b79fae438a2021-11-08T15:54:08ZFuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge2476-39772538-261610.22065/jsce.2019.150715.1673https://doaj.org/article/75c0bd0464974b669e1f46b79fae438a2021-02-01T00:00:00Zhttps://www.jsce.ir/article_81770_28b735757d90e377d0b1c1acdbae1042.pdfhttps://doaj.org/toc/2476-3977https://doaj.org/toc/2538-2616In this paper a new optimized active control algorithm based on combination of fuzzy neural network and a new highly efficient meta-heuristic population based optimization method extracted from Tug of War competition presents under different earthquake loads. The Efficiency of the proposed control method has been evaluated on the recently proposed nonlinear highway bridge benchmark, consist of nonlinear isolation bearings and nonlinear structural elements which equipped with the hydraulic actuators. A 5-layer neural network is used to obtain the control force. The neural network is utilized to approximate nonlinear rules of control. It gets instructions to the actuators installed between the deck and abutments. Stability of control laws to choose the parameters of the neural network are derived based on Lyapunov theory. The Results are presented in terms of a well-defined set of performance indices which is comparable to previous methods. The results show that the proposed controller method in spite of a simple description of the nonlinearities and non-detailed structural information can effectively reduce the responses of the bridge especially maximum of base shear, maximum of midspan displacement and maximum of acceleration. Also sensible decrease in responses such as maximum of ductility, dissipated energy and plasticity connections show that the proposed method is very effective in reducing structural damages.Mostafa GhelichiAlireza Mirza Goltabar RoshanHamidreza TavakoliAbbas KaramodinIranian Society of Structrual Engineering (ISSE)articleactive controltug of waranfisneural networkbenchmark bridgelyapunov stabilityBridge engineeringTG1-470Building constructionTH1-9745FAJournal of Structural and Construction Engineering, Vol 7, Iss شماره ویژه 4, Pp 5-22 (2021)
institution	DOAJ
collection	DOAJ
language	FA
topic	active control tug of war anfis neural network benchmark bridge lyapunov stability Bridge engineering TG1-470 Building construction TH1-9745
spellingShingle	active control tug of war anfis neural network benchmark bridge lyapunov stability Bridge engineering TG1-470 Building construction TH1-9745 Mostafa Ghelichi Alireza Mirza Goltabar Roshan Hamidreza Tavakoli Abbas Karamodin Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge
description	In this paper a new optimized active control algorithm based on combination of fuzzy neural network and a new highly efficient meta-heuristic population based optimization method extracted from Tug of War competition presents under different earthquake loads. The Efficiency of the proposed control method has been evaluated on the recently proposed nonlinear highway bridge benchmark, consist of nonlinear isolation bearings and nonlinear structural elements which equipped with the hydraulic actuators. A 5-layer neural network is used to obtain the control force. The neural network is utilized to approximate nonlinear rules of control. It gets instructions to the actuators installed between the deck and abutments. Stability of control laws to choose the parameters of the neural network are derived based on Lyapunov theory. The Results are presented in terms of a well-defined set of performance indices which is comparable to previous methods. The results show that the proposed controller method in spite of a simple description of the nonlinearities and non-detailed structural information can effectively reduce the responses of the bridge especially maximum of base shear, maximum of midspan displacement and maximum of acceleration. Also sensible decrease in responses such as maximum of ductility, dissipated energy and plasticity connections show that the proposed method is very effective in reducing structural damages.
format	article
author	Mostafa Ghelichi Alireza Mirza Goltabar Roshan Hamidreza Tavakoli Abbas Karamodin
author_facet	Mostafa Ghelichi Alireza Mirza Goltabar Roshan Hamidreza Tavakoli Abbas Karamodin
author_sort	Mostafa Ghelichi
title	Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge
title_short	Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge
title_full	Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge
title_fullStr	Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge
title_full_unstemmed	Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge
title_sort	fuzzy-tug of war structural active control for a seismically excited benchmark highway bridge
publisher	Iranian Society of Structrual Engineering (ISSE)
publishDate	2021
url	https://doaj.org/article/75c0bd0464974b669e1f46b79fae438a
work_keys_str_mv	AT mostafaghelichi fuzzytugofwarstructuralactivecontrolforaseismicallyexcitedbenchmarkhighwaybridge AT alirezamirzagoltabarroshan fuzzytugofwarstructuralactivecontrolforaseismicallyexcitedbenchmarkhighwaybridge AT hamidrezatavakoli fuzzytugofwarstructuralactivecontrolforaseismicallyexcitedbenchmarkhighwaybridge AT abbaskaramodin fuzzytugofwarstructuralactivecontrolforaseismicallyexcitedbenchmarkhighwaybridge
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Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge

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