###### Abstract  
The current study offers a data-driven methodology to predict the ultimate strain and compressive strength of concrete reinforced by aramid FRP wraps. An experimental database was collected from the literature, on which seven different machine learning (ML) models were trained. The diameter and length of the cylindrical specimens, the compressive strength of unconfined concrete, the thickness, elasticity modulus and ultimate tensile strength of the FRP wrap were used as the input features of the machine learning models, to predict the ultimate strength and strain of the specimens. The experimental dataset was further enhanced with synthetic data using the tabular generative adversarial network (TGAN) approach. The machine learning models' performances were compared to the predictions of the existing strain capacity and compressive strength prediction equations for aramid FRP-confined concrete. The accuracy of the predictive models was measured using state-of-the-art statistical metrics such as the coefficient of determination, mean absolute error and root mean squared error. On average, the machine learning models were found to perform better than the available equations in the literature. In particular, the extra trees regressor, XGBoost and K-nearest neighbors algorithms performed significantly better than the remaining algorithms, with R\({}^{2}\) scores greater than 0.98. Furthermore, the SHapley Additive exPlanations (SHAP) method and individual conditional expectation (ICE) plots were used to visualize the effects of various input parameters on the predicted ultimate strain and strength values. The unconfined compressive strength of concrete and the ultimate tensile strength of the FRP wrap were found to have the greatest impact on the machine learning model outputs. aramid fiber reinforced polymers; machine learning; compressive strength; concrete confinement; XGBoost; SHAP; TGAN +
Footnote †: journal: Applied Sciences  
soft body armor systems. The impact resistance, energy absorption and deformation properties of these systems have been tested using ballistics tests. It was reported that polyurea elastomer processing of Kevlar plates reduces the weight and thickness of multilayered soft body armors and improves the impact resistance. [PERSON] et al. [5] compared the performances of Kevlar, date palm, carbon, and hybrid fibers used in armor plates in terms of energy absorption with finite element analysis. The hybrid composite plates were found to perform best in terms of energy absorption. The usage of shear thickening fluid impregnation to enhance the penetration resistance of AFRP armor plates was proposed by [PERSON] et al. [6]. The addition of shear thickening fluid was shown to improve the flexibility, reduce the thickness and enhance the impact resistance of the armor plates. [PERSON] et al. [7] tested the impact resistance, flexibility and single yarn pull-out resistance of aramid fabrics impregnated with shear thickening fluid and adhesive polyurethane. The aramid fabrics impregnated with both shear thickening fluid and polyurethane were shown to perform better in terms of impact resistance. Another study with hybrid composites was carried out by [PERSON] et al. [8], who demonstrated the application of natural curaua fibers, in combination with aramid fibers in laminated composite plates. Increasing the number of curaua-reinforced layers was shown to reduce the flexural strength, energy absorption, and stiffness. Fiber-reinforced polymers have also found applications in the area of pile splicing. [PERSON] and [PERSON] [9; 10] implemented FRP materials in the splicing of prestressed precast concrete piles. A new FRP-based splicing method has been proposed as an economical and corrosion-resistant splicing technique which satisfies the established design requirements.