An Experimental Interpretation of High-Performance Fiber-Reinforced Polymer Concrete with FNN Paradigms

Abstract

Strengthening and enhancing of Reinforced Concrete (RC) structural components are important to broaden its administration period, overcoming the first structure limits and to limit the impact of construction defects as well as the design defects. In this work, Fiber Reinforced Polymers (FRPs) is utilized as to strengthen RC structures. In this paper, the utilization of FRP such as Sisal, Jute, and Coir in concrete structures is being examined for its viability in upgrading structural execution both regarding strength and ductility. The structural behavior of FRP specimen is examined by experimental and numerical examination by estimating the parameters, for example, compressive strength, tensile strength, ductility, and deflection. Here, we utilized the Fuzzy Neural Network (FNN) procedure to test the strength of specimen. At the point, when compared with existing work, the proposed FNN model achieves the greatest performance in terms of all parameters for the fiber reinforced specimen under various loaded condition.