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.

Fiber-reinforced polymers (FRPs) have been widely used to strengthen steel structures, which could suffer from corrosion or the introduction of web openings, for utilities such as ductwork, plumbing, electrical conduits, and HVAC systems. The present numerical study involves the application of unidirectional carbon FRP (CFRP) sheets to steel I-beams, damaged due to corrosion or web openings, to regain their lost load-carrying capacity. Finite element analysis (FEA) was utilized to develop and validate three beam models against existing experimentally tested specimens. Subsequently, a parametric study was conducted investigating the effect of various corrosion levels and the number of circular web openings on the yield and ultimate load capacities of the beams. The optimum number of CFRP layers needed to strengthen corroded beams was determined and six CFRP strengthening scenarios were adopted to determine the best configurations to retrofit steel beams with openings (SBWOs). The results revealed that corrosion, introduced by thinning the bottom flange, reduced both yield and ultimate load capacities, with a nearly perfect linear reduction in ultimate load for each 2.5% thickness loss. The optimum number of CFRP layers depended on the level of corrosion damage. Furthermore, while maintaining a constant total opening area, beams with a greater number of smaller circular web openings demonstrated higher yield and ultimate load capacities than those with fewer larger openings. Out of the six adopted CFRP strengthening scenarios, three configurations that involved applying CFRP sheets to both flanges and the web effectively restored the strength of SBWOs, when adequate CFRP layers were used.

This paper presents a nonlinear finite element analysis (FEA) of textiles reinforced mortars (TRM)-confined reinforced concrete (RC) columns through jacketing, under combined axial and cyclic loadings. The FEA models were validated with an experimental study in the literature that was conducted on full-scale square columns reinforced with continuous steel bars (no lap splices). Subsequently, parametric study was performed on the validated FEA models. The parameters considered include various jacket’s lengths and mortar strengths. Moreover, semiempirical models were developed to evaluate the plastic hinge length (L_P) and the ultimate drift ratio of RC columns confined with TRM and FRP jackets, while considering the jacket length effect. The FEA models and experimental results were in good agreement. The finite element results revealed that the increase in the jacket length improved the lateral deformation capacity and increased the plastic hinge length linearly up to a confinement ratio of 0.2. Beyond this point, the plastic hinge length shortened as the confinement ratio raised. Moreover, mortars with higher flexural strength resulted in a slightly higher deformation capacity. However, the difference in the mortar compressive strength did not affect the ultimate lateral deformation capacity. The semiempirical models show that the average difference in the predicted L_P and the ultimate drift ratio values as compared to the experimental and simulated columns was 3.19 and 16.06%, respectively.

Sustainable solutions in the building construction industry promotes the use of innovative materials such as textile reinforced mortar (TRM) as a strengthening technique resulting in a reduced life-cycle cost. This paper presents a nonlinear finite element analysis (FEA) of TRM strengthened RC walls with cut-out openings under axial loading. FEA models were developed and validated with two experimental tests from the literature. Subsequently, a parametric study was performed to investigate the contribution of TRM in strengthening RC walls considering various opening sizes, types, numbers and orientations of window openings, and TRM strengthening configurations. The parametric study results revealed that strengthened models with smaller opening sizes had higher axial strength enhancement. Furthermore, the increase in the axial load capacities of walls with door and window openings were 34 and 26%, respectively, as compared to the corresponding control ones. TRM was more effective with a lower opening aspect ratio (H_o/L_o). In addition, confining the wall piers with U-shaped TRM jackets was the most effective configuration in improving the walls’ axial strengths with maximum enhancements of 16 and 22% as compared to the models strengthened with side-bonded sheets and strips, respectively. Finally, the axial strengths of the FEA models were also compared with the existing empirical solution and showed reasonable correlation with an average discrepancy of 15%.