RESUMEN
In the pursuit of creating more sustainable and resilient structures, the exploration of construction materials and strengthening methodologies is imperative. Traditional methods of relying on steel for strengthening proved to be uneconomical and unsustainable, prompting the investigation of innovative composites. Fiber-reinforced polymers (FRPs), known for their lightweight and high-strength properties, gained prominence among structural engineers in the 1980s. This period saw the development of novel approaches, such as near-surface mounted and externally bonded reinforcement, for strengthening of concrete structures using FRPs. In recent decades, additional methods, including surface curvilinearization and external prestressing, have been discovered, demonstrating significant additional benefits. While these techniques have shown the enhanced performance, their full potential remains untapped. This article presents a comprehensive review of current approaches employed in the fortification of reinforced cement concrete structures using FRPs. It concludes by identifying key areas that warrant in-depth research to establish a sustainable methodology for structural strengthening, positioning FRPs as an effective replacement for conventional retrofitting materials. This review aims to contribute to the ongoing discourse on modern structural strengthening strategies, highlight the properties of FRPs, and propose avenues for future research in this dynamic field.
RESUMEN
Fiber reinforced concrete (FRC) is attracting many researchers' attention due to its excellent mechanical and fracture properties. However, its widespread implementation is hampered by the issues related to the dispersion and orientation of its fibers. According to the fracture mechanics, the reinforcement would provide maximum bridging when placed perpendicular to the crack propagation. This study is focused on the magnetic-based orientation of synthetic fibers which are mostly used in strain hardening FRC also termed as Engineered Cementitious Composites (ECC). Initially, the PVA fibers were coated with waste iron particles using a hydrothermal synthesis procedure. This was done to make synthetic fibers magnetically responsive by the formation of a physical bond between iron and PVA fibers. A solenoid was used to provide a high-intensity magnetic flux to orient these fibers in the direction of magnetic lines. Three different ECC mixes were prepared and cast in wooden molds. The molds were then placed one by one into the magnetic field for the orientation of the fibers. The fibers were successfully aligned perpendicular to the flexure cracks in only flexure dominant regions with the aid of a magnetic field. The orientation of fibers was verified with the help of microscopic images of the tortured surfaces. As a result of well aligned fibers dispersed in the ECC mix, the flexural strength was increased by 21%.
RESUMEN
Due to the exceptional qualities of fiber reinforced concrete, its application is expanding day by day. However, its mixed design is mainly based on extensive experimentations. This study aims to construct a machine learning model capable of predicting the fracture behavior of all conceivable fiber reinforced concrete subclasses, especially strain hardening engineered cementitious composites. This study evaluates 15x input parameters that include the ingredients of the mixed design and the fiber properties. As a result, it predicts, for the first time, the post-peak fracture behavior of fiber-reinforced concrete matrices. Five machine learning models are developed, and their outputs are compared. These include artificial neural networks, the support vector machine, the classification and regression tree, the Gaussian process of regression, and the extreme gradient boosting tree. Due to the small size of the available dataset, this article employs a unique technique called the generative adversarial network to build a virtual data set to augment the data and improve accuracy. The results indicate that the extreme gradient boosting tree model has the lowest error and, therefore, the best mimicker in predicting fiber reinforced concrete properties. This article is anticipated to provide a considerable improvement in the recipe design of effective fiber reinforced concrete formulations.