Steel and Composite Structures

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Estimation of moment and rotation of steel rack connections using extreme learning machine

  • Shariati, Mahdi (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) ;
  • Wakil, Karzan (Research Center, Sulaimani Polytechnic University) ;
  • Mehrabi, Peyman (Department of Civil Engineering, K.N. Toosi University of Technology) ;
  • Safa, Maryam (Institute of Research and Development, Duy Tan University) ;
  • Khorami, Majid (Universidad UTE, Facultad de Arquitectura y Urbanismo)
  • Received : 2018.03.06
  • Accepted : 2019.05.07
  • Published : 2019.06.10
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Abstract

The estimation of moment and rotation in steel rack connections could be significantly helpful parameters for designers and constructors in the initial designing and construction phases. Accordingly, Extreme Learning Machine (ELM) has been optimized to estimate the moment and rotation in steel rack connection based on variable input characteristics as beam depth, column thickness, connector depth, moment and loading. The prediction and estimating of ELM has been juxtaposed with genetic programming (GP) and artificial neural networks (ANNs) methods. Test outcomes have indicated a surpass in accuracy predicting and the capability of generalization in ELM approach than GP or ANN. Therefore, the application of ELM has been basically promised as an alternative way to estimate the moment and rotation of steel rack connection. Further particulars are presented in details in results and discussion.

Keywords

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