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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 국제구조공학회 2019 Steel and Composite Structures, An International J Vol.33 No.3
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.