Optimization of mass dampers with artificial intelligence algorithms for masonry buildings

Historical structures, which are a significant part of our cultural heritage, are presented as masonry buildings. These structures do not have sufficient resistance to dynamic loads due to their floors, which do not exhibit rigid diaphragm behavior, and walls that possess limited ductility. Their lack of reinforcement makes them more vulnerable to earthquake forces by restricting ductile behavior. Thick-walled sections exacerbate these forces. As a result of the aforementioned reasons, the use of passive, semi-active, and active control systems is recommended for the preservation of historical buildings. One of the passive control devices, tuned mass dampers (TMD), is preferred not only for high-rise buildings but also for historical structures. By reducing the building's response to dynamic effects such as earthquakes and wind, TMDs extend the lifespan of structural elements and ensure safety. TMDs are widely used due to their simple operating principles and successful applications, and their performance is largely dependent on design parameters. In this thesis, the optimum parameters of TMDs have been determined using artificial intelligence algorithms, revealing that a TMD with a mass participation ratio of 5% reduces maximum displacement the most. The developed coding has been proposed as an effective solution for masonry structures with regular geometry, allowing for the optimization of the dynamic responses of buildings. This approach offers an alternative method for preserving historical structures beyond strengthening and aims to protect cultural heritage in the future with minimal intervention.