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Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model

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Abstract

Recent developments on shear strength (Vf) of steel fiber-reinforced concrete beam (SFRCB) simulation have been shifted to the implementation of the computer aid advancements. The current study is attempted to explore new hybrid artificial intelligence (AI) model called integrative support vector regression with firefly optimization algorithm (SVR-FFA) for shear strength prediction of SFRCB. The developed hybrid predictive model is constructed using laboratory experimental data set gathered from the literature and belongs to the shear failure capacity. The related beam dimensional and concrete properties are utilized as input attributes to predict Vf. The proposed SVR-FFA model is validated against classical SVR model and eight empirical formulations obtained from published researches. The attained results of the proposed hybrid AI model exhibited a reliable resultant performance in terms of prediction accuracy. Based on the examined root-mean-square error (RMSE) and the correlation coefficient (R2) over the testing phase, SVR-FFA achieved (RMSE ≈ 0.25 MPa) and (R2 ≈ 0.96).

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Authors and Affiliations

  1. Project and Reconstruction Department, University of Baghdad, Baghdad, Iraq

    Abeer A. Al-Musawi & Afrah A. H. Alwanas

  2. Computer Science Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

    Sinan Q. Salih

  3. Civil Engineering Department, University of Diyala, Baqubah, Iraq

    Zainab Hasan Ali

  4. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Minh Tung Tran & Zaher Mundher Yaseen

Authors
  1. Abeer A. Al-Musawi
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  2. Afrah A. H. Alwanas
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  3. Sinan Q. Salih
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  4. Zainab Hasan Ali
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  5. Minh Tung Tran
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  6. Zaher Mundher Yaseen
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Correspondence to Zaher Mundher Yaseen.

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Al-Musawi, A.A., Alwanas, A.A.H., Salih, S.Q. et al. Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model. Engineering with Computers 36, 1–11 (2020). https://doi.org/10.1007/s00366-018-0681-8

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