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Structural Engineering and Mechanics Volume 70, Number 5, June10 2019 , pages 639-647 DOI: https://doi.org/10.12989/sem.2019.70.5.639 |
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Moment-rotation prediction of precast beam-to-column connections using extreme learning machine |
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Nguyen Thoi Trung, Aiyoub Fazli Shahgoli, Yousef Zandi, Mahdi Shariati, Karzan Wakil,
Maryam Safa and Majid Khorami
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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-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 | ||