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Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples

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Abstract

The successful use of fly ash (FA) and silica fume (SF) materials has been reported in the design of concrete samples in the literature. Due to the benefits of using these materials, they can be utilized in many industrial applications. However, the proper use of them in the right mixes is one of the important factors with respect to the strength and weight of concrete. Therefore, this paper develops relationships based on meta-heuristic (MH) algorithms (artificial bee colony technique) to evaluate the compressive strength of concrete specimens using laboratory experiments. A database comprising silica fume replacement ratio, fly ash replacement ratio, total cementitious material, water content coarse aggregate, high-rate water-reducing agent, fine aggregate, and age of samples, as model inputs, was used to evaluate and predict the compressive strength of concrete samples. Developed models of the MH technique created relationships between the mentioned parameters. In the new models, the influence of each parameter on the compressive strength was determined. Finally, using the developed model, optimum conditions for compressive strength of concrete samples were presented. This paper demonstrated that the MH algorithms are able to develop relationships that can serve as good substitutes for empirical models.

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Acknowledgements

This research is supported by a research grant of the University of Tabriz (Number 2718). The authors wish to express their appreciation to University of Tabriz for supporting this study and making it possible.

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Authors and Affiliations

  1. Department of Civil Engineering and Architecture, Hohhot Vocational College, Hohhot, 010051, China

    Lei Sun

  2. Water Conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot, 010018, China

    Lei Sun

  3. Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Mohammadreza Koopialipoor

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

    Danial Jahed Armaghani

  5. Faculty of Civil Engineering, University of Tabriz, 29 Bahman Blvd, Tabriz, 51666, Iran

    Danial Jahed Armaghani & Reza Tarinejad

  6. UTM Construction Research Centre, Institute for Smart, Infrastructure and Innovative Construction (ISIIC), Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    M. M. Tahir

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  1. Lei Sun
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  2. Mohammadreza Koopialipoor
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Correspondence to Danial Jahed Armaghani.

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Sun, L., Koopialipoor, M., Jahed Armaghani, D. et al. Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples. Engineering with Computers 37, 1133–1145 (2021). https://doi.org/10.1007/s00366-019-00875-1

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