Moment-rotation estimation of steel rack connection using extreme learning machine

Mahdi Shariati , Nguyen Thoi Trung , Karzan Wakil , Peyman Mehrabi , Maryam Safa , Majid Khorami

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.

Key Words

steel racks; moment rotation behavior; upright column; beam-end connector; ELM

Address

(1) Mahdi Shariati, Nguyen Thoi Trung — Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
(2) Mahdi Shariati, Nguyen Thoi Trung — Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
(3) Karzan Wakil — Research Center, Sulaimani Polytechnic University, Sulaimani 46001, Kurdistan Region, Iraq
(4) Peyman Mehrabi — Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran
(5) Maryam Safa — Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
(6) Majid Khorami — Universidad UTE, Facultad de Arquitectura y Urbanismo, Calle Rumipamba s/n y Bourgeois, Quito, Ecuador.

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