Artificial neural network algorithms to predict the bond strength of reinforced concrete: Coupled effect of corrosion, concrete cover, and compressive strength

Abstract

• The bond strength between steel and concrete was predicted using artificial neural network (ANN) models. • Softplus activation function provided an accurate prediction in comparison to Sigmoid and ReLU functions. • Bond strength is predominantly influenced by the level of steel corrosion. Degradation of the bond between reinforcement steel bars and concrete poses a huge challenge to the design of sustainable infrastructure. In this study, an initial effort was made to develop and apply Artificial Neural Network (ANN) models to predict the bond strength between steel reinforcement and concrete. To assess the efficiency of ANN under a case of limited experimental data, the ANN models were activated through Softplus, Rectified Linear unit (ReLU), or Sigmoid functions and their results were compared. The experimental/test data used in the modeling study only covered corrosion levels from 0 to 20 % of the reinforcement bars' weight, concrete compressive strengths of 23 and 51 MPa, and concrete covers ranging between 15 and 45 mm. A comparison was made between the bond strength values predicted by the ANN models, linear/non-linear statistical regression equations, and other analytical equations available in the literature. The model results indicated that the bond strength was predominantly affected by the level of corrosion (in comparison to the other parameters). Moreover, the ANN(Softplus) model with a mean squared error (J) of 2.89 and a coefficient of determination (R 2 ) of 96 % demonstrated a more accurate prediction of the bond strength in comparison to the ANN(Sigmoid), ANN(ReLu), and statistical regression models.