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Structural Engineering and Mechanics
  Volume 70, Number 5, June10 2019, pages 639-647
DOI: http://dx.doi.org/10.12989/sem.2019.70.5.639
 


Moment-rotation prediction of precast beam-to-column connections using extreme learning machine
Nguyen Thoi Trung, Aiyoub Fazli Shahgoli, Yousef Zandi, Mahdi Shariati, Karzan Wakil, Maryam Safa and Majid Khorami

 
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-tocolumn 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.
 
Key Words
    moment-rotation; forecasting; extreme learning machine; precast beam-to-column connection; partly hidden corbel
 
Address
Nguyen Thoi Trung, Mahdi Shariati: 1Division of Computational Mathematics and Engineering, Institute for Computational Science,
Ton Duc Thang University, Ho Chi Minh City, Vietnam
2Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Aiyoub Fazli Shahgoli, Yousef Zandi: Department of Civil Engineering, Islamic Azad University, Tabriz Branch, Iran
Karzan Wakil: Research Center, Sulaimani Polytechnic University, Sulaimani 46001, Kurdistan Region, Iraq
Maryam Safa: Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
Majid Khorami: Universidad UTE, Facultad de Arquitectura y Urbanismo, Calle Rumipamba s/n y Bourgeois, Quito, Ecuador
 

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