APPLICATION OF STEEL REINFORCED COMPOSITES FOR STRENGTHENING OF REINFORCED CONCRETE BEAMS AND COLUMNS: EXPERIMENTAL, ANALYTICAL, NUMERICAL, AND MACHINE LEARNING BASED STUDIES

Abstract

The use of steel reinforced composites (SRC) has emerged as a cost-effective and promising solution for the strengthening of reinforced concrete (RC) structures. The strengthening system in SRC comprises a unidirectional fabric made of ultra-high tensile strength galvanized steel cords embedded within an inorganic matrix or polymeric matrix to form steel reinforce grout (SRG) and steel reinforced polymer (SRP), respectively. The studies to date have focused primarily on the application of SRG to flexural deficient RC beams. Moreover, understanding the shear resistance mechanism of SRG-strengthened RC beams and generally, inorganic composites has always been a challenging task, and thus, has not yet been fully addressed. Another important application of SRC is for retrofitting of seismically deficient RC columns. To assess the performance of RC columns, and control damages under lateral loads it is critical to properly define the plastic hinge region, which is the region exposed to maximum plastic deformation. However, accurate determination of the plastic hinge length (PHL) remains a challenge. Accordingly, this dissertation is aimed to examine the application of SRC for strengthening RC beams and columns based on experimental, analytical, numerical, and machine learning (ML) based studies. With this aim, this dissertation comprised seven key studies. The application of SRG for strengthening of shear-deficient RC T-beams is experimentally and analytically investigated, for the first time, in the first study. The second study investigates the efficacy of SRG for shear strengthening of RC rectangular beams focusing on the effect of shear span-to-depth ratio. In the third study, the use of the near surface mounted technique for the SRG system was experimentally investigated. The fourth and fifth studies present ML-based capacity predictive models and reliability analysis of RC beams strengthened with inorganic composites in shear and flexure, respectively. The sixth study numerically explores the application of SRP for strengthening seismically deficient RC columns and the effects of key design parameters on SRP-confined columns. Finally, the last study proposed a robust ensemble ML-based model to predict the PHL of RC columns. The results of the studies revealed the high potential of SRC for strengthening RC beams and columns. The results of all seven studies have been published in peer-reviewed journals.