Abstract
The use of near-surface mounted FRP reinforcement in reinforced concrete structures has seen a considerable increase in recent years as a strengthening method. However, few simple models have been published for predicting the shear capacity given by this technique. In this work, two different approaches are proposed. Firstly, the use of neural networks as a means of predicting shear strength without the need to use complex models is developed. Secondly, with the purpose of generating a design formula of simple application to evaluate the shear strength contribution provided by a near-surface mounted system, a multi-objective optimization problem is solved. It results from considering simultaneously the experimental results of beams with and without NSM-FRP reinforcement. The performance of both methodologies is compared with some experimental results. Sensitivity and parametric analyses are performed too for further evaluation of the proposed models.
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