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Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Author

Listed:
  • Hai-Bang Ly

    (University of Transport Technology, Hanoi 100000, Vietnam)

  • Tien-Thinh Le

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

  • Huong-Lan Thi Vu

    (University of Transport Technology, Hanoi 100000, Vietnam)

  • Van Quan Tran

    (University of Transport Technology, Hanoi 100000, Vietnam)

  • Lu Minh Le

    (Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi 100000, Vietnam)

  • Binh Thai Pham

    (University of Transport Technology, Hanoi 100000, Vietnam)

Abstract

Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

Suggested Citation

  • Hai-Bang Ly & Tien-Thinh Le & Huong-Lan Thi Vu & Van Quan Tran & Lu Minh Le & Binh Thai Pham, 2020. "Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams," Sustainability, MDPI, vol. 12(7), pages 1-34, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2709-:d:338854
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    References listed on IDEAS

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    Cited by:

    1. Muhammad Rashid & Muhammad Attique Khan & Majed Alhaisoni & Shui-Hua Wang & Syed Rameez Naqvi & Amjad Rehman & Tanzila Saba, 2020. "A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    2. Hai-Bang Ly & Thuy-Anh Nguyen & Binh Thai Pham, 2022. "Investigation on factors affecting early strength of high-performance concrete by Gaussian Process Regression," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    3. Van Quan Tran & Hai-Van Thi Mai & Thuy-Anh Nguyen & Hai-Bang Ly, 2021. "Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-21, December.
    4. Xin Wei & Niaz Muhammad Shahani & Xigui Zheng, 2023. "Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach," Mathematics, MDPI, vol. 11(7), pages 1-17, March.

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