The use of fiber-reinforced polymers (FRPs) for strengthening existing reinforced concrete (RC) structures has significantly improved structural rehabilitation processes, providing efficient, durable, and non-invasive solutions. This study presents an advanced deep learning-based predictive model specifically developed to estimate the shear strength of concrete beams strengthened externally with carbon fiber-reinforced polymer (CFRP) composites. Using a comprehensive dataset of 216 experimentally tested CFRP-wrapped concrete beams drawn from existing research, a deep neural network model was rigorously optimized with the Optuna hyperparameter tuning framework and k-fold cross-validation to ensure robustness and generalizability. Model validation involved a thorough comparative analysis against established international design codes (ACI PRC-440.2-17, CSA-S806-12, JSCE) and a parametric study examining the sensitivity of shear strength predictions to key influencing factors, including concrete compressive strength, beam depth, and CFRP wrap thickness. Results demonstrated superior prediction accuracy and reliability of the deep learning approach compared to traditional empirical design models. Consequently, this research significantly enhances the precision of shear strength predictions for CFRP-strengthened concrete beams, supporting the development of more efficient and accurate structural rehabilitation and design guidelines.
Mahmoudabadi et al. (Wed,) studied this question.