The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.

The deterioration of reinforced concrete (RC) structures is a significant global concern. Demolishing degraded structures is costly, time-consuming, and impacts the integrity of connected members. Implementing jackets, including fiber-reinforced polymer (FRP), fiber-reinforced cementitious matrix (FRCM), and ferro-cement, for the retrofitting and rehabilitation of existing structural members presents an advanced alternative to the deteriorated RC structures. As compared to FRP and FRCM, the ferro-cement is an economical solution that improves the mechanical strength of the structural members, service life, and offers fire resistance, durability, and versatility. This research evaluated the axial load-carrying capacity (ALCC) of ferro-cement confined RC columns using existing analytical models and machine learning (ML) algorithms. A database of 151 RC column specimens from literature was utilized. Four ML models (Artificial Neural Network, Decision Tree, Linear Regression, and Support Vector Machine) were developed to predict the axial capacity of ferro-cement confined RC columns. The ANN model performed the best out of all the ML models. The developed model was precise, user-friendly, and cost-effective. The ANN-based mathematical model can be easily used by structural engineers and ferro-cement jacketing designers to estimate the axial capacity of the ferro-cement strengthened RC columns.