Predicting the compressive strength of CFRP-confined concrete using deep learning

Abstract

The application of externally bonded fiber-reinforced polymers (FRP) has revolutionized structural rehabilitation, offering cost-effective and rapid solutions for damaged structures. This study presents a novel deep learning approach for predicting the compressive strength of circular carbon-FRP-confined concrete columns. Leveraging an extensive database of 664 experimental results, the model incorporates monotonicity and smoothness constraints, with hyperparameters optimization using the OPTUNA framework. The proposed model demonstrates exceptional accuracy, achieving R² = 0.93, a20-index = 0.95, and MAPE = 7.89 % on unseen test data, consistently outperforming nine benchmark models including established design guidelines. Scenario-based analysis confirms the model's ability to capture known physical behaviors, such as the effects of concrete strength, column diameter, and FRP thickness on confinement effectiveness. The integration of physical constraints enhances the model's reliability and interpretability, bridging the gap between data-driven and physics-based approaches. This research contributes to the advancement of more accurate, economical, and reliable design guidelines for FRP-strengthened structures, while also demonstrating the potential of constrained deep learning in structural engineering applications.