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Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework

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Abstract

In this research, the principal purpose is investigating the performance of structural elements significantly degrades at elevated temperatures. Steel–concrete composite floor systems are one of the most relevant components in building construction in which fire-induced problems directly damage their performance. The purpose of this study is on employing analytical intelligence technique to predict two major structural characteristics of the steel–concrete composite floor system. Two intelligent methods, ELM-PSO and ELM-GWO, were used as a multi-combination AI method to predict the shear and tensile response of these composite floor systems at high temperature. Accordingly, authenticated data on monotonic loading response of this steel–concrete composite floor system in different heat stages had been employed from different literatures. The results show that ELM-GWO technique presented the best prediction of split-tensile load, and the ELM-GWO provided the best estimation of slip value. By giving each prediction equations, the best equations were proposed. As a result, employing ELM-GWO, successfully presented reliable and decisive results and proved the proficiency of the techniques.

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Notes

  1. Extreme learning machine.

  2. Artificial neural network.

  3. Genetic programming.

  4. Adaptive neuro-fuzzy inference system.

  5. Fiber-reinforced concrete.

  6. Grey wolf optimizer.

  7. High performance concrete.

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Acknowledgements

The study presented herein also was made possible by the Universiti Kebangsaan Malaysia Research Grant, GUP-2018-030.

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Authors and Affiliations

  1. Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran

    Armin Morasaei

  2. Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX, 79409, USA

    Aria Ghabussi

  3. Faculty Member of Architecture, Urbanism and Art Department Architecture, Urmia University, 5756151818, Urmia, Iran

    Soheila Aghlmand

  4. Faculty of Built Environment, University of New South Wales, Sydney, Australia

    Maziar Yazdani

  5. Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia

    Shahrizan Baharom

  6. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Hamid Assilzadeh

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  1. Armin Morasaei
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Morasaei, A., Ghabussi, A., Aghlmand, S. et al. Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework. Engineering with Computers 38, 2567–2582 (2022). https://doi.org/10.1007/s00366-020-01228-z

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