Staff View: Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures

Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are devel...

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Main Authors:	Shariati, Mahdi, Mafipour, Mohammad Saeed, Mehrabi, Peyman, Zandi, Yousef, Dehghani, Davoud, Bahadori, Alireza, Shariati, Ali, Nguyen, Thoi Trung, Salih, Musab N. A., Shek, Poi Ngian
Format:	Article
Published:	Techno Press 2019
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/88451/ http://dx.doi.org/10.12989/scs.2019.33.3.319
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id	my.utm.88451
record_format	eprints
spelling	my.utm.884512020-12-15T00:06:30Z http://eprints.utm.my/id/eprint/88451/ Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures Shariati, Mahdi Mafipour, Mohammad Saeed Mehrabi, Peyman Zandi, Yousef Dehghani, Davoud Bahadori, Alireza Shariati, Ali Nguyen, Thoi Trung Salih, Musab N. A. Shek, Poi Ngian TA Engineering (General). Civil engineering (General) This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model. Techno Press 2019-11 Article PeerReviewed Shariati, Mahdi and Mafipour, Mohammad Saeed and Mehrabi, Peyman and Zandi, Yousef and Dehghani, Davoud and Bahadori, Alireza and Shariati, Ali and Nguyen, Thoi Trung and Salih, Musab N. A. and Shek, Poi Ngian (2019) Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures. Steel and Composite Structures, 33 (3). pp. 319-332. ISSN 12299367 http://dx.doi.org/10.12989/scs.2019.33.3.319
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Shariati, Mahdi Mafipour, Mohammad Saeed Mehrabi, Peyman Zandi, Yousef Dehghani, Davoud Bahadori, Alireza Shariati, Ali Nguyen, Thoi Trung Salih, Musab N. A. Shek, Poi Ngian Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures
description	This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.
format	Article
author	Shariati, Mahdi Mafipour, Mohammad Saeed Mehrabi, Peyman Zandi, Yousef Dehghani, Davoud Bahadori, Alireza Shariati, Ali Nguyen, Thoi Trung Salih, Musab N. A. Shek, Poi Ngian
author_facet	Shariati, Mahdi Mafipour, Mohammad Saeed Mehrabi, Peyman Zandi, Yousef Dehghani, Davoud Bahadori, Alireza Shariati, Ali Nguyen, Thoi Trung Salih, Musab N. A. Shek, Poi Ngian
author_sort	Shariati, Mahdi
title	Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures
title_short	Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures
title_full	Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures
title_fullStr	Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures
title_full_unstemmed	Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures
title_sort	application of extreme learning machine (elm) and genetic programming (gp) to design steel-concrete composite floor systems at elevated temperatures
publisher	Techno Press
publishDate	2019
url	http://eprints.utm.my/id/eprint/88451/ http://dx.doi.org/10.12989/scs.2019.33.3.319
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score	13.244745

Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures

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