BP Neural Network Improved by Sparrow Search Algorithm in Predicting Debonding Strain of FRP-Strengthened RC Beams

Abstract

To prevent debonding failure of FRP- (fiber reinforced polymer-) strengthened RC (reinforced concrete) beams, most codes proposed models for debonding strain limitation of FRP reinforcements. However, only a few factors that affect debonding failure are considered in the models. The experimental results show that these models cannot accurately evaluate debonding strain and have a large variability. In order to improve the accuracy of predicting the debonding strain of FRP-strengthened RC beams, a BP neural network model was developed based on the sparrow search algorithm (SSA). To predict the debonding strain of FRP reinforcements, the established neural network model was trained and simulated through experimental data. The results show that the coefficient of variation of the present SSA-BP neural network model is 13%. The main factors affecting debonding strain are the longitudinal reinforcement ratio, stirrup reinforcement ratio, and concrete strength, which are not considered in the code models. The present model has better prediction accuracy and more robustness than the traditional BP neural network and the code models.