Sustainable solutions in the building construction industry have emerged as a new method for retrofitting applications in the last two decades. Fiber-reinforced polymers (FRPs) have garnered much attention among researchers for improving reinforced concrete (RC) structures. The existing design guidelines for FRP-strengthened RC members were developed using empirical methods that are based on specific databases, limiting the accuracy of the predicted results. Therefore, the use of innovative and efficient prediction tools to predict the behavior of FRP-strengthened RC members has become essential. During the last few years, efforts have been progressively focused on the use of machine learning (ML) as a feasible and effective technique for solving various structural engineering problems. Its capability to predict the behavior of complex nonlinear structural systems while considering a wide range of parameters offers a distinctive opportunity to make the behavior of RC members more predictable and accurate. This paper aims to evaluate the current state of using various ML algorithms in RC members strengthened with FRP to enable researchers to determine the capabilities of current solutions as well as to find research gaps to carry out more research to bridge revealed knowledge and practice gaps. Scopus databases were searched using predefined standards. The search revealed ninety-six articles published between 2016 and 2023. Consequently, these articles were analyzed for ML applications in the field of FRP retrofitting, including flexural and shear strengthening of RC beams, flexural strengthening of slabs, confinement and compressive strength of columns, and FRP bond strength. The results reveal that 32% of the reviewed studies focused on the application of ML techniques to the flexural and shear strengthening of RC beams, 32% on the confinement and compressive strength of columns, 6.5% on the flexural strengthening of slabs, 22% on FRP bond strength, 6.5% on materials, and 1% on beam–column joints. This research also revealed that the application of various ML algorithms has shown a significant improvement in resistance prediction accuracy as compared with the existing empirical solutions. Supervised learning techniques were the most favorable learning method due to their good generalization, interpretability, adaptability, and predictive efficiency. In addition, the selection of suitable ML algorithms and optimization techniques is found to be mainly dictated by the nature of the problem and the characteristics of the dataset. Nonetheless, selecting the most appropriate ML model and optimization algorithm for each specific application remains a challenge, given that each algorithm is developed with different principles and methodologies.

Externally bonded (EB) reinforcement and near surface–mounted (NSM) reinforcement were extensively studied to strengthen reinforced concrete beams in the last decades. However, few studies compare their effectiveness. This paper aims to study the effectiveness of EB and NSM strengthening techniques using carbon fiber–reinforced polymer (CFRP) strips in strengthening RC beams. In this study, three different parameters were studied, including the strengthening technique (i.e., NSM, EB), CFRP quantity, and NSM-CFRP distribution. In addition, two different bond behaviors were investigated, including perfect and cohesive models. A total of twelve beams were modeled, including six beams with perfect bond behavior and six beams with cohesive bond behavior. Each six tested beams include one control beam, two strengthened beams with EB-CFRP, and three strengthened beams with NSM-CFRP. All beams have the same cross-section dimensions, material properties, and steel reinforcement. The finite-element (FE) models were analyzed using a nonlinear FE analysis by ABAQUS software, and the results were validated with experimental works obtained from literature. In general, the developed FE models accurately predicted the ultimate strengths, strains, maximum deflections, and failure mode when compared to analytical and experimental results. For the same area of CFRP laminates, beams strengthened with the NSM system achieved higher ultimate strength but relatively lower stiffness when compared to beams strengthened with the EB system.