Structural Engineering and Mechanics

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Moment-rotation prediction of precast beam-to-column connections using extreme learning machine

  • Trung, Nguyen Thoi (Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University) ;
  • Shahgoli, Aiyoub Fazli (Department of Civil Engineering, Islamic Azad University) ;
  • Zandi, Yousef (Department of Civil Engineering, Islamic Azad University) ;
  • Shariati, Mahdi (Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University) ;
  • Wakil, Karzan (Research Center, Sulaimani Polytechnic University) ;
  • Safa, Maryam (Institute of Research and Development, Duy Tan University) ;
  • Khorami, Majid (Universidad UTE, Facultad de Arquitectura y Urbanismo)
  • Received : 2019.03.29
  • Accepted : 2019.05.31
  • Published : 2019.06.10
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Abstract

The performance of precast concrete structures is greatly influenced by the behaviour of beam-to-column connections. A single connection may be required to transfer several loads simultaneously so each one of those loads must be considered in the design. A good connection combines practicality and economy, which requires an understanding of several factors; including strength, serviceability, erection and economics. This research work focuses on the performance aspect of a specific type of beam-to-column connection using partly hidden corbel in precast concrete structures. In this study, the results of experimental assessment of the proposed beam-to-column connection in precast concrete frames was used. The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) for moment-rotation prediction of precast beam-to-column connections. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models was accessed based on simulation results and using several statistical indicators.

Keywords

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