A Review of Seismic Control Systems and the Role of Artificial Intelligence in Enhancing Their Performance

Seismic control systems, as one of the key tools in reducing earthquake-induced damages, play a vital role in enhancing the safety and stability of structures. These systems improve the performance of buildings against seismic loads by reducing dynamic responses such as displacements, accelerations, and internal forces. In recent decades, with advancements in technology and the rapid growth of engineering knowledge, the use of seismic control systems has become a prominent approach in the design and retrofitting of structures. Given the increasing complexity of structures, the diversity of seismic conditions, and environmental changes, traditional seismic control methods are no longer fully adequate to meet the needs of engineers. In such circumstances, the use of modern technologies such as Artificial Intelligence (AI) can bring about a significant transformation in this field. AI, by enabling rapid, accurate, and intelligent analysis, can serve as an effective complement to seismic control systems. The use of machine learning algorithms, artificial neural networks, fuzzy logic, genetic algorithms, and other AI techniques allows for accurate modeling of nonlinear structural behavior, timely damage detection, and adaptive decision-making. These capabilities are particularly crucial in systems that require fast and effective responses, especially in active and semi-active control systems. This article provides a comprehensive review of various types of seismic control systems, including passive systems (such as tuned mass dampers, viscous dampers, and base isolators), active systems (which require external power sources), semi-active systems (which offer a combination of responsiveness and high efficiency), and hybrid systems. Furthermore, it analyzes the interaction of each system with AI technologies and evaluates the impact of different algorithms on optimizing their performance. Findings from numerical studies, computer modeling, and case studies from real-world projects show that integrating AI techniques with seismic control systems can significantly reduce inter-story drifts, enhance structural stability, and improve performance under severe earthquakes. Moreover, this integration can lead to the design of adaptive systems that optimize their performance based on changing conditions without the need for human intervention. Ultimately, the use of intelligent seismic control systems is not only a significant step toward increasing safety and reducing the vulnerability of structures to earthquakes, but also opens new horizons for developing sustainable, smart, and resilient urban infrastructure. This modern approach represents a promising direction for the future of earthquake engineering and the design of disaster-resilient structures.

کلیدواژه ها:

Seismic control ، Artificial Intelligence ، Machine Learning ، Neural Networks ، Active Control ، Optimization