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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2019
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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 |
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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 |
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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. |
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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 |
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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 |
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2019 |
url |
http://eprints.utm.my/id/eprint/88451/ http://dx.doi.org/10.12989/scs.2019.33.3.319 |
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1687393572931764224 |
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13.18916 |