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Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach

Author

Listed:
  • Aman Kumar

    (AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
    Structural Engineering Department, CSIR—Central Building Research Institute, Roorkee 247667, India)

  • Harish Chandra Arora

    (AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
    Structural Engineering Department, CSIR—Central Building Research Institute, Roorkee 247667, India)

  • Krishna Kumar

    (Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India)

  • Mazin Abed Mohammed

    (College of Computer Science and Information Technology, University of Anbar, 11, Ramadi 31001, Iraq)

  • Arnab Majumdar

    (Department of Civil Engineering, Imperial College London, London SW7 2AZ, UK)

  • Achara Khamaksorn

    (College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Orawit Thinnukool

    (College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restoration of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time.

Suggested Citation

  • Aman Kumar & Harish Chandra Arora & Krishna Kumar & Mazin Abed Mohammed & Arnab Majumdar & Achara Khamaksorn & Orawit Thinnukool, 2022. "Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach," Sustainability, MDPI, vol. 14(2), pages 1-25, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:845-:d:723127
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    References listed on IDEAS

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    1. Ignacio J. Navarro & Víctor Yepes & José V. Martí, 2018. "Life Cycle Cost Assessment of Preventive Strategies Applied to Prestressed Concrete Bridges Exposed to Chlorides," Sustainability, MDPI, vol. 10(3), pages 1-16, March.
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