An experimental interpretation of high-performance fibre-reinforced polymer concrete with fnn paradigms

Abstract

In modern building, reinforced concrete (RC) is a flexible composite and one of the most widely used materials. Furthermore, it is necessary to strengthen and enhance the structural components of Reinforced Concrete to extend its administration span, to overcome the first structural challenges and to limit the impact of construction defects along with design defects. To reinforce RC structures, Fibre Reinforced Polymers (FRPs) are used in the proposed system as FRP composites have become essential materials for maintaining and strengthening existing infrastructure. In this work, the use of FRP in concrete structures such as Sisal, Jute, and Coir is investigated for its viability in upgrading structural execution in terms of strength and ductility. Experimental and computational examinations analyse the structural behaviour of the FRP sample by estimating the parameters, such as compressive strength, tensile strength, ductility, and deflection. Moreover, Fuzzy Neural Network (FNN) model are used to evaluate the intensity of the specimens in different loaded conditions. The results show the superiority of our approach over the existing state-of-the-art systems.