Investigation of Innovative Strengthening Methods and Their Integration with Artificial Intelligence Technologies for Improving Structural Performance

In recent years, modern structural retrofitting methods utilizing advanced materials and innovative engineering techniques have gained significant attention as key solutions for enhancing the safety and seismic performance of structures, particularly against dynamic loads such as earthquakes. These methods have been developed to improve load-bearing capacity, increase ductility, and reduce the vulnerability of buildings to seismic events. One of the most effective of these methods is the use of composite materials such as Glass Fiber Reinforced Polymers (GFRP) and Carbon Fiber Reinforced Polymers (CFRP). These materials not only offer high mechanical strength but also possess low weight, corrosion resistance, ease of installation, and long service life, making them suitable options for strengthening various structural elements including beams, columns, shear walls, and joints—especially in seismic-prone regions. On the other hand, with the advancement of information technology and data science, the integration of emerging technologies such as Artificial Intelligence (AI) into the field of civil engineering has opened new horizons in structural analysis, design, and performance optimization. Machine learning algorithms, artificial neural networks, evolutionary algorithms, and other AI methods have enabled engineers to achieve more accurate modeling of structural behavior using numerical, experimental, or field data. These tools not only reduce the time and cost of analyses but also play a significant role in improving design and implementation decision-making. This paper explores the synergy between modern retrofitting methods and artificial intelligence technologies. In this regard, by reviewing and analyzing recent national and international studies, the benefits, limitations, and opportunities of integrating these two technological approaches are examined. The findings of this research indicate that the strategic combination of AI with advanced retrofitting methods can lead to improved design reliability, reduced implementation costs, optimized material usage, and enhanced seismic performance of structures under real-world conditions. Overall, this combined approach can pave the way for a transformative shift in the future of structural engineering.

کلیدواژه ها:

Structural Strengthening ، Composite Materials (GFRP ، CFRP) ، Dynamic and Seismic Loads ، Artificial Intelligence (AI) ، Machine Learning Algorithms ، Structural Performance Optimization