Abstract
Over the past years, fiber-reinforced polymer (FRP) rebars have been extensively used in the field of construction instead of steel rebars, thanks to their non-corrosive nature and high tensile strength. The bond strength between FRP rebars and concrete is a critical design parameter that controls reinforced concrete members’ performance at the serviceability and ultimate limit states. The latter is generally affected by several factors. Unlike steel reinforcement, FRP materials are anisotropic, non-homogeneous, and linearly elastic, resulting in different force transfer mechanisms between the reinforcement and concrete. Therefore, accurate estimation of the bond strength is considered a critical element and might be helpful in many practical applications. In this study, a database including 477 experimental beam results gathered from the available literature is used to develop an artificial neural network (ANN) model to predict the bond strength of FRP bars in concrete. Two ANN models using the Scaled Conjugate Gradient algorithm (SCG) and Variable Learning Rate Backpropagation algorithm (GDX) are constructed and evaluated in terms of bond strength prediction accuracy. The assessment of the models is conducted using statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results show that the proposed ANN model can accurately predict the bond strength of FRP bars in concrete, which appears as an efficient numerical alternative for engineers.
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Nguyen, TA., Ta, H.N.T. (2022). Application of an Artificial Neural Network Model for the Prediction of the Bond Strength of FRP Bars in Concrete. In: Ha-Minh, C., Tang, A.M., Bui, T.Q., Vu, X.H., Huynh, D.V.K. (eds) CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure. Lecture Notes in Civil Engineering, vol 203. Springer, Singapore. https://doi.org/10.1007/978-981-16-7160-9_180
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DOI: https://doi.org/10.1007/978-981-16-7160-9_180
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