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Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach

AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
Structural Engineering Department, CSIR—Central Building Research Institute, Roorkee 247667, India
Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India
College of Computer Science and Information Technology, University of Anbar, 11, Ramadi 31001, Iraq
Department of Civil Engineering, Imperial College London, London SW7 2AZ, UK
College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
Author to whom correspondence should be addressed.
Academic Editors: Mojtaba Mahmoodian and Le Li
Sustainability 2022, 14(2), 845;
Received: 24 November 2021 / Revised: 3 January 2022 / Accepted: 8 January 2022 / Published: 12 January 2022
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. View Full-Text
Keywords: GPR; bond strength prediction; FRCM; FRCM–concrete interface; ANN; SVM GPR; bond strength prediction; FRCM; FRCM–concrete interface; ANN; SVM
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MDPI and ACS Style

Kumar, A.; Arora, H.C.; Kumar, K.; Mohammed, M.A.; Majumdar, A.; Khamaksorn, A.; Thinnukool, O. Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach. Sustainability 2022, 14, 845.

AMA Style

Kumar A, Arora HC, Kumar K, Mohammed MA, Majumdar A, Khamaksorn A, Thinnukool O. Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach. Sustainability. 2022; 14(2):845.

Chicago/Turabian Style

Kumar, Aman, Harish C. Arora, Krishna Kumar, Mazin A. Mohammed, Arnab Majumdar, Achara Khamaksorn, and Orawit Thinnukool. 2022. "Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach" Sustainability 14, no. 2: 845.

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