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Machine Learning Driven Shear Strength Prediction Model for FRP-Reinforced Concrete Beams

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Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022 (CSCE 2022)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 348))

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Abstract

The shear strength prediction of a fiber-reinforced polymer (FRP)-reinforced concrete (RC) beam is a difficult undertaking that is influenced by several design variables. While the usage of FRP bars has emerged as a viable alternative in reducing corrosion problems associated with steel reinforcement in an extreme environment, a precise and reliable method of shear strength prediction is required to assure cost-effective material use and optimum design. Several design provisions and optimized design equations are now available in the current literature; nevertheless, when these equations are used, a significant variation between the experimental and the predicted shear strength of FRP-RC beams is observed. This study utilised the power of data-driven modelling techniques for enhanced prediction capability of such a complex phenomenon. The objective of the current study is to develop a data-driven shear strength prediction model for FRP-RC slender beams using the extreme gradient boosting (XGBoost) algorithm. A large database of 302 tests of RC beams longitudinally reinforced with FRP bars without stirrups was collected from the available literature. The performance of the proposed ML model is compared with the existing standards, codes, guidelines, and optimized shear strength equation. The results reveal that the XGBoost model outperformed the existing shear strength provisions and has a high level of prediction accuracy.

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Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada

    Mohammad Rezaul Karim

  2. Department of Civil, Geological and Mining Engineering, Polytechnique Montreal, Montreal, QC, Canada

    Kamrul Islam

  3. Department of Civil Engineering, Lakehead University, Thunder Bay, ON, Canada

    A. H. M. Muntasir Billah

  4. The School of Engineering, The University of British Columbia, Kelowna, BC, Canada

    M. Shahria Alam

Authors
  1. Mohammad Rezaul Karim
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  2. Kamrul Islam
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  3. A. H. M. Muntasir Billah
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  4. M. Shahria Alam
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Corresponding author

Correspondence to Mohammad Rezaul Karim .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, University of Victoria, Victoria, BC, Canada

    Rishi Gupta

  2. Department of Civil Engineering, University of Victoria, Victoria, BC, Canada

    Min Sun

  3. Department of Earthquake Engineering, The University of British Columbia, Vancouver, BC, Canada

    Svetlana Brzev

  4. The University of British Columbia-Okanagan Campus, Kelowna, BC, Canada

    M. Shahria Alam

  5. University of Regina, Regina, SK, Canada

    Kelvin Tsun Wai Ng

  6. Environmental Engineering Program, University of Northern British Columbia, Prince George, BC, Canada

    Jianbing Li

  7. The University of Western Ontario, London, ON, Canada

    Ashraf El Damatty

  8. Department of Civil Engineering, The University of British Columbia, Vancouver, BC, Canada

    Clark Lim

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© 2023 Canadian Society for Civil Engineering

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Karim, M.R., Islam, K., Billah, A.H.M.M., Alam, M.S. (2023). Machine Learning Driven Shear Strength Prediction Model for FRP-Reinforced Concrete Beams. In: Gupta, R., et al. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022. CSCE 2022. Lecture Notes in Civil Engineering, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-031-34159-5_70

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  • DOI: https://doi.org/10.1007/978-3-031-34159-5_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34158-8

  • Online ISBN: 978-3-031-34159-5

  • eBook Packages: EngineeringEngineering (R0)

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