Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques

Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isola...

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Autores principales:	Visar Farhangi, Hashem Jahangir, Danial Rezazadeh Eidgahee, Arash Karimipour, Seyed Alireza Nedaei Javan, Hamed Hasani, Nazanin Fasihihour, Moses Karakouzian
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	shape memory alloy (SMA) SMA-equipped bar hysteretic dampers (SMA-BHDs) hysteresis curves artificial neural network (ANN) group method of data handling (GMDH) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/e37bbfd46fda4de09818aefa7d4dc867
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spelling	oai:doaj.org-article:e37bbfd46fda4de09818aefa7d4dc8672021-11-11T15:07:46ZBehaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques10.3390/app1121100572076-3417https://doaj.org/article/e37bbfd46fda4de09818aefa7d4dc8672021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10057https://doaj.org/toc/2076-3417Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the occurrence of residual displacement is inevitable. To address this issue, in this study, a new smart type of bar hysteretic dampers equipped with shape memory alloy (SMA) bars with recentring features, as the supplementary damper, is introduced and investigated. In this regard, 630 numerical models of SMA-equipped bar hysteretic dampers (SMA-BHDs) were constructed based on experimental samples with different lengths, numbers, and cross sections of SMA bars. Furthermore, by utilising hysteresis curves and the corresponding ideal bilinear curves, the role of geometrical and mechanical parameters in the cyclic behaviour of SMA-BHDs was examined. Due to the deficiency of existing analytical models, proposed previously for steel bar hysteretic dampers (SBHDs), to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs accurately, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) approaches. The results showed that, although the ANN models outperform GMDH ones, both ANN- and GMDH-based models can accurately estimate the linear and nonlinear behaviour of SMA-BHDs in pre- and post-yield parts with low errors and high accuracy and consistency.Visar FarhangiHashem JahangirDanial Rezazadeh EidgaheeArash KarimipourSeyed Alireza Nedaei JavanHamed HasaniNazanin FasihihourMoses KarakouzianMDPI AGarticleshape memory alloy (SMA)SMA-equipped bar hysteretic dampers (SMA-BHDs)hysteresis curvesartificial neural network (ANN)group method of data handling (GMDH)TechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10057, p 10057 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	shape memory alloy (SMA) SMA-equipped bar hysteretic dampers (SMA-BHDs) hysteresis curves artificial neural network (ANN) group method of data handling (GMDH) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	shape memory alloy (SMA) SMA-equipped bar hysteretic dampers (SMA-BHDs) hysteresis curves artificial neural network (ANN) group method of data handling (GMDH) Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Visar Farhangi Hashem Jahangir Danial Rezazadeh Eidgahee Arash Karimipour Seyed Alireza Nedaei Javan Hamed Hasani Nazanin Fasihihour Moses Karakouzian Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
description	Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the occurrence of residual displacement is inevitable. To address this issue, in this study, a new smart type of bar hysteretic dampers equipped with shape memory alloy (SMA) bars with recentring features, as the supplementary damper, is introduced and investigated. In this regard, 630 numerical models of SMA-equipped bar hysteretic dampers (SMA-BHDs) were constructed based on experimental samples with different lengths, numbers, and cross sections of SMA bars. Furthermore, by utilising hysteresis curves and the corresponding ideal bilinear curves, the role of geometrical and mechanical parameters in the cyclic behaviour of SMA-BHDs was examined. Due to the deficiency of existing analytical models, proposed previously for steel bar hysteretic dampers (SBHDs), to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs accurately, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) approaches. The results showed that, although the ANN models outperform GMDH ones, both ANN- and GMDH-based models can accurately estimate the linear and nonlinear behaviour of SMA-BHDs in pre- and post-yield parts with low errors and high accuracy and consistency.
format	article
author	Visar Farhangi Hashem Jahangir Danial Rezazadeh Eidgahee Arash Karimipour Seyed Alireza Nedaei Javan Hamed Hasani Nazanin Fasihihour Moses Karakouzian
author_facet	Visar Farhangi Hashem Jahangir Danial Rezazadeh Eidgahee Arash Karimipour Seyed Alireza Nedaei Javan Hamed Hasani Nazanin Fasihihour Moses Karakouzian
author_sort	Visar Farhangi
title	Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
title_short	Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
title_full	Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
title_fullStr	Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
title_full_unstemmed	Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
title_sort	behaviour investigation of sma-equipped bar hysteretic dampers using machine learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e37bbfd46fda4de09818aefa7d4dc867
work_keys_str_mv	AT visarfarhangi behaviourinvestigationofsmaequippedbarhystereticdampersusingmachinelearningtechniques AT hashemjahangir behaviourinvestigationofsmaequippedbarhystereticdampersusingmachinelearningtechniques AT danialrezazadeheidgahee behaviourinvestigationofsmaequippedbarhystereticdampersusingmachinelearningtechniques AT arashkarimipour behaviourinvestigationofsmaequippedbarhystereticdampersusingmachinelearningtechniques AT seyedalirezanedaeijavan behaviourinvestigationofsmaequippedbarhystereticdampersusingmachinelearningtechniques AT hamedhasani behaviourinvestigationofsmaequippedbarhystereticdampersusingmachinelearningtechniques AT nazaninfasihihour behaviourinvestigationofsmaequippedbarhystereticdampersusingmachinelearningtechniques AT moseskarakouzian behaviourinvestigationofsmaequippedbarhystereticdampersusingmachinelearningtechniques
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Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques

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