Machine Intelligence, Big Data Analytics, and IoT in Image Processing

17Machine Learning Models in Prediction of Strength Parameters of FRP-Wrapped RC Beams

Aman Kumar^1,2*, Harish Chandra Arora^1,2, Nishant Raj Kapoor^1,2 and Ashok Kumar^1,2

¹ CSIR-Central Building Research Institute, Roorkee, India

² AcSIR-Academy of Scientific and Innovative Research, Ghaziabad, India

Abstract

The corrosion of reinforced concrete (RC) structures is of increasing concern across the world. Repair, restoration, replacement, and the construction of new buildings all necessitate the use of economic and long-lasting technology. Fiber reinforced polymer (FRP) has been widely used in both retrofitting and new construction of buildings. FRP has seen an increase in the application as a repair composite material in reinforced concrete and masonry structures over the last decade due to its many properties. This material has various benefits, including excellent strength-to-weight ratios, stiffness-to-weight, lightweight, potential long-term durability, and relative ease of field use. This chapter provides a summary of the machine learning models in the estimation of bond strength between FRP and concrete surface, shear, and flexural strength of FRP-wrapped reinforced concrete beams.

Keywords: Machine learning, artificial intelligence, FRP-concrete bond, flexural strength, shear strength, FRP

17.1. Introduction

Construction is one of the most important industries in all economies across the world. Cement, clay, timber, steel, aluminum, and glass are just a few of the materials ...