Title: Artificial Neural Network to Predict Bond Strength of Deformed Bars in Concrete
Author(s): Vitaliy V. Degtyarev
Publication: Symposium Paper
Appears on pages(s): 81-89
Keywords: anchorage; artificial intelligence; artificial neural network; bond; confinement; deformed reinforcement; k-fold cross-validation; relative rib area; splice
The bond between reinforcing bars and concrete is an important property that determines the performance of reinforced concrete structures. Accurate prediction of the bond strength is essential for ensuring the safety and economy of the structures. This paper proposes an artificial neural network for predicting the bond strength between straight deformed reinforcing bars and concrete under tensile load. The neural network was trained using a large dataset of test results from the ACI Committee 408 database. A robust ten-fold cross-validation method was employed for evaluating network performance and finding optimal network parameters. Hyperparameter tuning was carried out to establish the optimal network hyperparameters. The relative impact of the neural network input parameters on the bond strength was evaluated using the SHAP method. The developed neural network with the optimal hyperparameters shows a good agreement with the test results. Its accuracy exceeds the accuracy of the descriptive equations.