Abstract
In this paper, an extensive simulation program is conducted to find out the optimal ANN model to predict the shear strength of fiber-reinforced polymer (FRP) concrete beams containing both flexural and shear reinforcements. For acquiring this purpose, an experimental database containing 125 samples is collected from the literature and used to find the best architecture of ANN. In this database, the input variables consist of 9 inputs, such as the ratio of the beam width, the effective depth, the shear span to the effective depth, the compressive strength of concrete, the longitudinal FRP reinforcement ratio, the modulus of elasticity of longitudinal FRP reinforcement, the FRP shear reinforcement ratio, the tensile strength of FRP shear reinforcement, the modulus of elasticity of FRP shear reinforcement. Thereafter, the selection of the appropriate architecture of ANN model is performed and evaluated by common statistical measurements. The results show that the optimal ANN model is a highly efficient predictor of the shear strength of FRP concrete beams with a maximum R2 value of 0.9634 on the training part and an R2 of 0.9577 on the testing part, using the best architecture. In addition, a sensitivity analysis using the optimal ANN model over 500 Monte Carlo simulations is performed to interpret the influence of reinforcement type on the stability and accuracy of ANN model in predicting shear strength. The results of this investigation could facilitate and enhance the use of ANN model in different real-world problems in the field of civil engineering.
Highlights
In aggressive environments, the load-bearing capacity of reinforcing bars in the concrete structure can be seriously declined due to steel corrosion
The general flexural theory of reinforced concrete structures is moderately applied for concrete beams using fiber-reinforced polymer (FRP) as flexural reinforcement [20, 21], but using FRP as shear reinforcement raise the complexity of mechanism behavior of concrete beams
It can be seen that a fluctuation of 1% around the mean value is obtained after about 300 simulations, whereas with the same number of Monte Carlo simulations, root mean square error (RMSE) and Mean Absolute Error (MAE) vary about 2% of the mean values (Fig 4C–4F)
Summary
The load-bearing capacity of reinforcing bars in the concrete structure can be seriously declined due to steel corrosion. The performance of reinforced concrete structures could be reduced [1,2,3] To prevent this phenomenon, numerous solutions have been proposed to avoid steel corrosion in reinforced concrete structures, such as increase the concrete cover layer to protect the reinforcements, the use of high-performance concrete (HPC), or waterproof paint [4]. Numerous solutions have been proposed to avoid steel corrosion in reinforced concrete structures, such as increase the concrete cover layer to protect the reinforcements, the use of high-performance concrete (HPC), or waterproof paint [4] These solutions lead to an increase in cost. Predicting shear strength of fiber reinforcement bars concrete beams structures. It is difficult to apply the existing shear strength prediction models of reinforced concrete beams to estimate FRP reinforced concrete beams
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