Steel and Composite Structures

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

  • Shariati, Mahdi (Institute of Research and Development, Duy Tan University) ;
  • Mafipour, Mohammad Saeed (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Mehrabi, Peyman (Department of Civil Engineering, K.N. Toosi University of Technology) ;
  • Zandi, Yousef (Department of Civil Engineering, Tabriz Branch, Islamic Azad University) ;
  • Dehghani, Davoud (Department of Civil Engineering, Qeshm International Branch, Islamic Azad University) ;
  • Bahadori, Alireza (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Shariati, Ali (Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University) ;
  • Trung, Nguyen Thoi (Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University) ;
  • Salih, Musab N.A. (School of civil engineering, faculty of engineering, Universiti Teknologi Malaysia) ;
  • Poi-Ngian, Shek (Construction Research Center (CRC), Institute for Smart Infrastructure & Innovative Construction (ISIIC), School of Civil Engineering, Universiti Teknologi Malaysia)
  • Received : 2018.06.27
  • Accepted : 2019.10.24
  • Published : 2019.11.10
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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.

Keywords

References

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