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Development of prediction models for shear strength of SFRCB using a machine learning approach

  • Theory and Applications of Soft Computing Methods
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Abstract

In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.

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Acknowledgments

The authors are thankful to Professor Marc O. Eberhard (University of Washington) for providing a part of the experimental database. The authors appreciate the support and stimulating discussions of Professor Mohammad Ghasem Sahab [Amirkabir University of Technology (Tehran Polytechnic)].

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Authors and Affiliations

  1. Department of Civil Engineering, Kashmar Branch, Islamic Azad University, Kashmar, Iran

    Masoud Sarveghadi

  2. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, 48824, USA

    Amir H. Gandomi

  3. Department of Civil Engineering, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran

    Hamed Bolandi

  4. Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Amir H. Alavi

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  1. Masoud Sarveghadi
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  2. Amir H. Gandomi
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  3. Hamed Bolandi
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  4. Amir H. Alavi
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Correspondence to Amir H. Gandomi.

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Sarveghadi, M., Gandomi, A.H., Bolandi, H. et al. Development of prediction models for shear strength of SFRCB using a machine learning approach. Neural Comput & Applic 31, 2085–2094 (2019). https://doi.org/10.1007/s00521-015-1997-6

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  • DOI: https://doi.org/10.1007/s00521-015-1997-6

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