Simulation Analysis of FRP- Strengthened RC slab using ODLN-SSO Approach

Abstract

As of late, research endeavors have been made to explore the structural behavior of Reinforced Concrete (RC) slabs. In the proposed work, the Fiber Reinforced Polymers (FRPs) have been utilized as retrofitting materials to strengthen Reinforced Concrete (RC) structures. The considered FRP materials are Carbon Fiber Reinforced Polymer (CFRP), Glass Fiber Reinforced Polymer (GFRP), Basalt Fiber Reinforced Polymer (BFRP) and Sisal Fiber Reinforced Polymer (SFRP). Also, to improve the execution of RC slabs, a hybrid mix of these fibers are used as the retrofitting material. The structural behavior of FRP-strengthened RC slab is researched by the experimental as well as numerical examination by estimating the parameters, for example, compressive strength, ductility and slab deflection. Here, we utilized the Optimal Deep Learning Network (ODLN) along with Salp Swarm Optimization (SSO) Algorithm to test the FRP-RC slab. At the point, when contrasted with existing work, the proposed ODLN-SSO algorithm accomplishes maximum strength regarding deflection and compressive strength for the hybridized FRP-RC slabs under various loaded condition.