Explicit neural network model for predicting FRP-concrete interfacial bond strength based on a large database

Abstract This study builds a large database from an extensive survey of existing single-lap shear tests on fiber-reinforced polymer (FRP)-concrete interfacial bonds, comprising 969 test results. Twenty shear-bond strength models published over the past 20 years have been collected and analyzed. These models take into account the effects of the concrete compressive strength, concrete width, FRP elastic modulus, FRP thickness, FRP width and FRP bond length on the ultimate bond strength of the FRP-concrete interface. This paper evaluates the predictive accuracy of the 20 collected models and finds that these models have limited accuracy. To accurately predict the bond strength of the FRP-concrete interface, this paper employs the back propagation neural networks (BPNN) method to train and test the database and builds an artificial neural networks (ANN) model that consists of weighted values, biases and transfer functions. The ANN model test conducts 84 training iterations and selects the optimal combination of input nodes. The accuracy of the developed ANN model is higher (i.e., lower predictive error) than that of the existing bond strength models in the literature. Furthermore, this paper develops an explicit user-friendly formula based on the trained ANN model. The proposed formula estimates and validates the 969 bond strength results, and the predictions using the explicit equation fit the test data very well with small error. As such, the formula can be easily applied during practical designs instead of the implicit processes in the ANN model.