AUTHOR=Gasser Moamen , Mahmoud Omar , Elsayed Taha , Deifalla Ahmed 

TITLE=Reliable machine learning for the shear strength of beams strengthened using externally bonded FRP jackets

JOURNAL=Frontiers in Materials

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2023.1153421

DOI=10.3389/fmats.2023.1153421

ISSN=2296-8016

ABSTRACT=All over the world, shear strengthening of reinforced concrete elements using external fiber-reinforced polymer jackets could be used to improve building sustainability. However, reports issued by the American Institute for concrete called for heavy scrutinizing before actual field implementation. The very limited number of proposed shear equations lacks reliability and accuracy. Thus, further investigation in this area is needed. In addition, machine learning techniques are being implemented successfully in developing strength models for complex problems including shear, flexure, and torsion. This study provides a reliable machine-learning model for reinforced concrete beams strengthened in shear using externally reinforced fiber polymer sheets. The proposed model was developed and validated against the experimental database and the very limited models in the literature. The model showed better agreement with the experimentally measured strength than the previous models.