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Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models

Author

Listed:
  • Mosbeh R. Kaloop

    (Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
    Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22012, Korea
    Public Works and Civil Engineering Department, Mansoura University, Mansoura 35516, Egypt)

  • Bishwajit Roy

    (School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India)

  • Kuldeep Chaurasia

    (School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India)

  • Sean-Mi Kim

    (Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea)

  • Hee-Myung Jang

    (Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea)

  • Jong-Wan Hu

    (Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
    Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22012, Korea)

  • Basem S. Abdelwahed

    (Structural Engineering Department, Mansoura University, Mansoura 35516, Egypt)

Abstract

This study looks to propose a hybrid soft computing approach that can be used to accurately estimate the shear strength of reinforced concrete (RC) deep beams. Support vector regression (SVR) is integrated with three novel metaheuristic optimization algorithms: African Vultures optimization algorithm (AVOA), particle swarm optimization (PSO), and Harris Hawks optimization (HHO). The proposed models, SVR-AVOA, -PSO, and -HHO, are designed and compared to reference existing models. Multi variables are used and evaluated to model and evaluate the deep beam’s shear strength, and the sensitivity of the selected variables in modeling the shear strength is assessed. The results indicate that the SVR-AVOA outperforms other proposed and existing models for the shear strength prediction. The mean absolute error of SVR-AVOA, SVR-PSO, and SVR-HHO are 43.17 kN, 44.09 kN, and 106.95 kN, respectively. The SVR-AVOA can be used as a soft computing technique to estimate the shear strength of the RC deep beam with a maximum error of ±3.39%. Furthermore, the sensitivity analysis shows that the deep beam’s key parameters (shear span to depth ratio, web reinforcement’s yield strength, concrete compressive strength, stirrups spacing, and the main longitudinal bars reinforcement ratio) are efficiently impacted in the shear strength detection of RC deep beam.

Suggested Citation

  • Mosbeh R. Kaloop & Bishwajit Roy & Kuldeep Chaurasia & Sean-Mi Kim & Hee-Myung Jang & Jong-Wan Hu & Basem S. Abdelwahed, 2022. "Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models," Sustainability, MDPI, vol. 14(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5238-:d:802700
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    References listed on IDEAS

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    1. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
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    Cited by:

    1. Ehsan Mansouri & Maeve Manfredi & Jong-Wan Hu, 2022. "Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning," Sustainability, MDPI, vol. 14(20), pages 1-17, October.

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