Analytical and Numerical Investigation of Embedded Through-section GFRP-Strengthened RC Beams with a Developed Bonding-based Model

A new approach that combines analytical two-parameter kinematic theory (2PKT) with machine learning (ML) models for estimating the shear capacity of embedded through-section (ETS)-strengthened reinforced concrete (RC) beams is proposed. The 2PKT was first developed to validate its representativeness and confidence against the available experimental data of ETS-retrofitted RC beams. Given the deficiency of the test data, the developed 2PKT was utilized to generate a large data pool with 2643 samples. The aim was to optimize the ML algorithms, namely, the random forest, extreme gradient boosting (XGBoost), light gradient boosting machine, and artificial neural network (ANN) algorithm. The optimized ANN model exhibited the highest accuracy in predicting the total shear strength of ETS-strengthened beams and ETS shear contribution. In terms of predicting the total shear strength of ETS-strengthened beams, the ANN model achieved R2 values of 0.99, 0.98, and 0.96 for the training, validation, and testing data, respectively. By contrast, the ANN model could predict ETS shear contribution with high accuracy, with R2 values of 0.99, 0.99, and 0.97 for the training, validation, and testing data, respectively. Then, the effects of all design variables on the shear capacity of the ETS-strengthened beams were investigated using the hybrid 2PKT–ML. The obtained trends could well appraise the reasonability of the proposed approach.

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Consistency of Bond–Slip Models in Simulating the Bond between ETS-FRP Bars and Concrete and the Shear Contribution of the Strengthening System

Bui,

Sekiya

2024

J. Compos. Constr.

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Analytical and Numerical Investigation of Embedded Through-section GFRP-Strengthened RC Beams with a Developed Bonding-based Model

Cited by 3 publications

References 30 publications

Experimental and 3D Numerical Study on CFRP–Concrete Interface Under Dynamic Loading

Experimental and 3D Numerical Study on CFRP–Concrete Interface Under Dynamic Loading

Data-driven prediction of the shear capacity of ETS-FRP-strengthened beams in the hybrid 2PKT–ML approach

Consistency of Bond–Slip Models in Simulating the Bond between ETS-FRP Bars and Concrete and the Shear Contribution of the Strengthening System

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