Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm

Magnetorheological (MR) fluid is among the smart materials that can change its default properties with the influence of a magnetic field. Typical application of an MR fluid based device involves an adjustable damper which is commercially known as an MR fluid damper. It is used in vibration control a...

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Autores principales:	Saharuddin K. D., Ariff M. H. M., Mohmad K., Bahiuddin I., Ubaidillah, Mazlan S. A., Nazmi N., Fatah A. Y. A.
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	magnetorheological fluid mr damper extreme learning machine hysteresis loop machine learning Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/0685c653223d49e4905364ad583f8e83
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id	oai:doaj.org-article:0685c653223d49e4905364ad583f8e83
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spelling	oai:doaj.org-article:0685c653223d49e4905364ad583f8e832021-12-05T14:10:46ZPrediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm2391-543910.1515/eng-2021-0053https://doaj.org/article/0685c653223d49e4905364ad583f8e832021-03-01T00:00:00Zhttps://doi.org/10.1515/eng-2021-0053https://doaj.org/toc/2391-5439Magnetorheological (MR) fluid is among the smart materials that can change its default properties with the influence of a magnetic field. Typical application of an MR fluid based device involves an adjustable damper which is commercially known as an MR fluid damper. It is used in vibration control as an isolator in vehicles and civil engineering applications. As part of the device development process, proper understanding of the device properties is essential for reliable device performance analysis. This study introduce an accurate and fast prediction model to analyse the dynamic characteristics of the MR fluid damper. This study proposes a new modelling technique called Extreme Learning Machine (ELM) to predict the dynamic behaviour of an MR fluid damper hysteresis loop. This technique was adopted to overcome the limitations of the existing models using Artificial Neural Networks (ANNs). The results indicate that the ELM is extremely faster than ANN, with the capability to produce high accuracy prediction performance. Here, the hysteresis loop, which represents the relationship of force-displacement for the MR fluid damper, was modelled and compared using three different activation functions, namely, sine, sigmoid and hard limit. Based on the results, it was found that the prediction performance of ELM model using the sigmoid activation functions produced highest accuracy, and the lowest Root Mean Square Error (RMSE).Saharuddin K. D.Ariff M. H. M.Mohmad K.Bahiuddin I.UbaidillahMazlan S. A.Nazmi N.Fatah A. Y. A.De Gruyterarticlemagnetorheological fluidmr damperextreme learning machinehysteresis loopmachine learningEngineering (General). Civil engineering (General)TA1-2040ENOpen Engineering, Vol 11, Iss 1, Pp 584-591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	magnetorheological fluid mr damper extreme learning machine hysteresis loop machine learning Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	magnetorheological fluid mr damper extreme learning machine hysteresis loop machine learning Engineering (General). Civil engineering (General) TA1-2040 Saharuddin K. D. Ariff M. H. M. Mohmad K. Bahiuddin I. Ubaidillah Mazlan S. A. Nazmi N. Fatah A. Y. A. Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
description	Magnetorheological (MR) fluid is among the smart materials that can change its default properties with the influence of a magnetic field. Typical application of an MR fluid based device involves an adjustable damper which is commercially known as an MR fluid damper. It is used in vibration control as an isolator in vehicles and civil engineering applications. As part of the device development process, proper understanding of the device properties is essential for reliable device performance analysis. This study introduce an accurate and fast prediction model to analyse the dynamic characteristics of the MR fluid damper. This study proposes a new modelling technique called Extreme Learning Machine (ELM) to predict the dynamic behaviour of an MR fluid damper hysteresis loop. This technique was adopted to overcome the limitations of the existing models using Artificial Neural Networks (ANNs). The results indicate that the ELM is extremely faster than ANN, with the capability to produce high accuracy prediction performance. Here, the hysteresis loop, which represents the relationship of force-displacement for the MR fluid damper, was modelled and compared using three different activation functions, namely, sine, sigmoid and hard limit. Based on the results, it was found that the prediction performance of ELM model using the sigmoid activation functions produced highest accuracy, and the lowest Root Mean Square Error (RMSE).
format	article
author	Saharuddin K. D. Ariff M. H. M. Mohmad K. Bahiuddin I. Ubaidillah Mazlan S. A. Nazmi N. Fatah A. Y. A.
author_facet	Saharuddin K. D. Ariff M. H. M. Mohmad K. Bahiuddin I. Ubaidillah Mazlan S. A. Nazmi N. Fatah A. Y. A.
author_sort	Saharuddin K. D.
title	Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
title_short	Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
title_full	Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
title_fullStr	Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
title_full_unstemmed	Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
title_sort	prediction model of magnetorheological (mr) fluid damper hysteresis loop using extreme learning machine algorithm
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/0685c653223d49e4905364ad583f8e83
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Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm

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