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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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