Application of multi-hybrid metaheuristic algorithm on prediction of split-tensile strength of shear connectors

Liu C. Zandi Y. Rahimi A. Peng Y. Ge G. Khadimallah M.A. Issakhov A. Tatyana Yu S.
August 2021 Techno-Press

Smart Structures and Systems
2021 #28 Issue 2 167 - 180 pp.

Shear connectors play a major role in the development of composite steel concrete systems. The behavior of shear connectors is usually calculated by push-out measurements. These experiments are expensive and take a lot of time. Soft Computation (SC) may be applied as an additional solution to remove the need for push-out testing. The objective of the research is to explore the implementation, as sub-branches of the SC approaches, of artificial intelligence (AI) techniques for the prediction of advanced C-shaped shear connectors. To this end, multiple push-out tests on these connectors will be carried out and the requisite data is obtained for the AI models. The Grey Wolf Optimizer algorithm (GWO) is built to define the parameters that influence the shear strength of angle connectors. Two regression metrics as determination coefficient (R²) and root mean square (RMSE) were used to measure the results of model. Furthermore, only four parameters in the predictive models are sufficient to provide an extremely precise prediction. It was found that GWO is a faster method and is able to achieve marginally higher output indices than in experiments. t

Multi-hybrid metaheuristic algorithm , Prediction , Shear connector , Split-tensile strength

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Shenyang Borlid Technology Co., Ltd., Shenyang, 110036, China
Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Ma’anshan University, Ma’anshan, 243100, China
Prince Sattam Bin Abdulaziz University, College of Engineering, Civil Engineering Department, Al-Kharj, 16273, Saudi Arabia
Laboratory of Systems and Applied Mechanics, Polytechnic School of Tunisia, University of Carthage, Tunis, Tunisia
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Kazakh-British Technical University, Almaty, Kazakhstan
South Ural State University, Chelyabinsk, Russian Federation

Shenyang Borlid Technology Co.
Department of Civil Engineering
Ma’anshan University
Prince Sattam Bin Abdulaziz University
Laboratory of Systems and Applied Mechanics
Al-Farabi Kazakh National University
Kazakh-British Technical University
South Ural State University

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