Transfer learning-based confinement strength prediction of concrete confined by FRP transverse reinforcements

Although several confinement strength models have been proposed for concrete confined by fiber-reinforced polymer (FRP) transverse reinforcements, these models show considerable scatter and discrepancies in the estimation of experimental data. Machine learning (ML) techniques may present an alternative prediction approach, but challenges arise due to insufficient experimental data. This study, therefore, develops a transfer learning-based model for predicting the confinement strength of concrete confined by FRP transverse reinforcements. Firstly, a literature review was conducted to collect a target dataset of FRP-confined concrete columns and a source dataset of steel-confined concrete columns used for knowledge transfer. Subsequently, a transfer learning algorithm was proposed to construct an ML confinement strength model for FRP-confined concrete. Finally, the performance of the proposed model was compared to that of six ML models and seven physics-based models. The model evaluation based on one-time random split indicated that the proposed model provided more accurate predictions for the confinement strength than other models considered in this study, achieving an R2 of 0.9089 on the test dataset. Shuffle split evaluation demonstrated that the proposed model exhibited superior stability and robustness compared to other models considered. Based on the proposed transfer learning-based model, the importance of the input parameters was obtained, further confirming the robustness of the proposed model.