Mert Can Kurucu;Ercan Atam;Müjde Güzelkaya;İbrahim Eksin
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引用次数: 0
Abstract
Seismic protection of multi-story buildings using friction dampers (FDs) is a cheap and effective passive structural control solution. For optimization of system response, optimal distribution of FDs between floors is required, which is a challenging problem. In this paper, we propose novel intelligent computational methods based on reinforcement learning for the distribution of
$n$
FDs for
$m$
-story buildings. In order to demonstrate the effectiveness of the proposed methods, a case study of optimally distributing 16 FDs in a 3-story building is considered, and the results are compared with the optimal solution found from a statistical analysis based on a large number of earthquake accelerations.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.