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

Authors: 

Mahdi Shariati, Mohammad Saeed Mafipour, Peyman Mehrabi, Yousef Zandi, Davoud Dehghani, Alireza Bahadori, Ali Shariati*, Nguyen Thoi Trung, Musab N.A. Salih, Shek Poi-Ngian

Source title: 
Steel and Composite Structures, 33(3): 319-332, 2019 (ISI)
Academic year of acceptance: 
2019-2020
Abstract: 

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.