Abstract
Externally strengthening reinforced concrete (RC) structures has been a desired practice in both research community and the industry over the last few decades. This application entails bonding composites to the surfaces of RC members to upgrade their strength, stiffness, and ductility. This study will attempt to use Machine Learning (ML) techniques to study and predict the shear strength of RC beams strengthened in shear with externally-bonded fiber-reinforced polymer (EB-FRP) laminates. An extensive database consisting of 511 tested specimens and 17 test parameters were collected. An appropriate artificial neural network (ANN)-based model was used to predict the shear capacities (Vu) of the FRP-strengthened beams, including the specific contributions of the EB-FRP (Vf) to these shear capacities. The obtained results indicate that the ANN-based model provided reasonable predictions for both and Vu and Vf.
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