A Novel Framework for Seismic Retrofitting and Control of Reinforced Concrete Structures Using GFRP Composites and Artificial Intelligence Algorithms

In this study, an innovative framework is proposed for the seismic retrofitting and control of reinforced concrete structures, utilizing a combination of advanced materials such as glass fiber-reinforced polymer (GFRP) and modern artificial intelligence (AI) technologies. The main objective of this research is to enhance the strength and stability of existing structures against earthquakes through the application of modern and efficient methods compared to conventional retrofitting techniques. The research methodology is based on numerical modeling of a reinforced concrete frame with high seismic vulnerability. In this regard, critical points of the structure that are most susceptible to earthquake forces were first identified, and then GFRP sheets with various configurations were applied in these regions. To determine the optimal strengthening design, artificial intelligence algorithms—particularly the genetic algorithm—were employed. These algorithms, by simulating the natural evolution process, are capable of exploring a wide range of possible solutions and selecting configurations that offer the best seismic performance. Subsequently, nonlinear dynamic analyses were conducted to evaluate the seismic response of the structure under various earthquake acceleration records. The results of these analyses demonstrate that the proposed framework not only significantly reduces displacements, stresses, and internal forces in structural members but also noticeably improves the overall structural performance compared to traditional methods. In other words, the integration of GFRP and AI algorithms in structural strengthening presents an intelligent and effective approach to reducing seismic vulnerability. Moreover, this research investigates the role of AI algorithms in determining the optimal retrofitting parameters. Using these algorithms allows for accurate and reliable virtual design without the need for costly and time-consuming physical trial and error. Ultimately, the findings of this study can serve as a foundation for developing intelligent retrofitting systems at real-world scales and play a crucial role in the design of safe and resilient urban infrastructure.

کلیدواژه ها:

Seismic requirements ، performance optimization ، reinforced concrete frame ، GFRP composites ، genetic algorithm ، nonlinear analysis.