For example, FRP rods have been successfully applied to replace steel rods in several fields and industries because of their high performance in terms of durability, and the problems they avoid related to the corrosion of steel. The use of Fiber-Reinforced Polymer (FRP) composites is widely used in the reinforcement of reinforced concrete (RC) structures, and can provide a different solution in terms of modulus of elasticity, tensile strength, and bond behavior. Finally, it is important to highlight that the architecture presented is contact-free, automatic, cost-effective, and measures directly on the laminate surfaces, which allows them to be widely used in the application of pre-stressed laminates. ResNet34 deep learning architecture provided the most accurate results, reaching a root mean square error (RMSE) of 0.057‰ for strain prediction. Furthermore, dropout and cross-validation methods for quantifying traditional machine learning algorithms and neural networks are used to efficiently provide uncertainty estimates. The adopted methods range from traditional machine learning to deep learning. The architecture is fed by digitally deformed synthetic images, generated based on a low-resolution camera. The main objective is to provide a solution that might be economically feasible, automated, easy to use, and accurate. This paper proposes and benchmarks contact-free architecture for measuring the strain level of CFRP laminate based on computer vision. Thus, the development of expedited approaches to accurately measure the pre-stressed application in the laminates represents an important contribution to the field. The measurement of the strain level during the pre-stress application usually involves laborious and time-consuming applications of instrumentation. retained The application is more effective if pre-stressed CFRP laminates are adopted. The strengthening of concrete structures with laminates of Carbon-Fiber-Reinforced Polymers (CFRP) is a widely adopted technique.
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