Seismic Retrofitting System Design for Reinforced Concrete Structures Using Artificial Intelligence Techniques

Seismic retrofitting of reinforced concrete structures is one of the most important approaches to reduce the vulnerability of buildings against earthquakes. Given the complex seismic behavior of structures, the use of advanced methods such as artificial intelligence (AI) can play a significant role in improving the performance of retrofitting systems. This paper focuses on applying machine learning algorithms, including Artificial Neural Networks (ANN) and evolutionary algorithms like Genetic Algorithms (GA), in the design process of seismic retrofitting systems. These algorithms can analyze complex data and model nonlinear structural behavior, providing optimized retrofitting options with higher accuracy compared to traditional methods. A case study using structural data from a reinforced concrete building is presented, where machine learning models are employed to design an optimal retrofitting system. The results demonstrate that the use of AI not only increases the accuracy and efficiency of the retrofitting system design but also significantly reduces costs, execution time, and material consumption. Overall, the findings indicate that AI techniques, especially in civil engineering and seismic-resistant structural design, can serve as effective and complementary tools to traditional analytical methods and play an important role in the future of structural retrofitting.

Keywords:

Seismic Retrofitting , Reinforced Concrete Structures , Artificial Intelligence , Machine Learning Algorithms , Artificial Neural Networks , Genetic Algorithm.