Abstract
In this paper, the feed-forward backpropagation neural network (FFBPNN) is used to propose a new formulation for predicting the compressive strength of fiber-reinforced polymer (FRP)-confined concrete cylinders. A set of experimental data has been considered in the analysis. The data include information about the dimensions of the concrete cylinders (diameter, length) and the total thickness of FRP layers, unconfined ultimate concrete strength, ultimate confinement pressure, ultimate tensile strength of the FRP laminates and the ultimate concrete strength of the concrete cylinders. The confined ultimate concrete strength is considered as the output data, while other parameters are considered as the input data. These parameters are mostly used in existing FRP-confined concrete models. Soft computing techniques are used to estimate the compressive strength of FRP-confined concrete cylinders. Finally, a new formulation is proposed. The results of the proposed formula are compared to the existing methods. To verify the proposed method, results are compared with other methods. The results show that the described method can forecast the compressive strength of FRP-confined concrete cylinders with high precision in comparison with the existing formulas. Moreover, the mean percentage of error for the proposed method is very low (3.49%). Furthermore, the proposed formula can estimate the ultimate compressive capacity of FRP-confined concrete cylinders with a different type of FRP and arbitrary thickness in the initial design of practical projects.
Highlights
A combination of high-strength fibers and matrix leads to the construction of a fiber-reinforced polymer (FRP)
For the proposed formula, more than 96% of the simulated results are entirely consistent with the experimental results, and that the proposed method is very accurate compared to other existing methods
A soft computing model for the ultimate strength estimation of FRP-confined concrete cylinders (FRPCCC) has been proposed in this paper
Summary
A combination of high-strength fibers and matrix leads to the construction of a fiber-reinforced polymer (FRP). Ebrahimpour Komleh and Maghsoudi [46] proposed a new formulation to estimate the curvature ductility factor for FRP-reinforced high-strength concrete beams using ANFIS and multiple regression methods. The ANFIS and ANN models were applied by Amini and Moeini [48] to compare results obtained for the shear strength of reinforced concrete beams with building codes. The feed-forward backpropagation neural network (FFBPNN) method has been used to estimate the ultimate compressive capacity of FRP-confined concrete cylinders. For this purpose, a set of previously published and available experimental data (281 instances) for concrete made of ordinary sand has been collected for training and testing. The proposed method can be employed using a calculator with high precision while, in the case of neuro-fuzzy, neural network and other known methods, a computer and sophisticated software are usually needed
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