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Proceedings Paper

Determining the optimal number and position of damping viscose (MR) in seismic vibration control of buildings to use genetic algorithms (fuzzy engineering application)
Author(s): Farzad Hatami; Esmail Karimi
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Paper Abstract

In this paper, study optimization performance of damping semi-active viscose MR in order to reduce the seismic vibrations of a building with 12 floors. For this purpose, we used genetic algorithms to obtain optimal number and optimal situation in 15 major earthquakes with different frequency content through which final status damping are determined. Investigating the influence of this type of damper on the construct dynamic response, the construct equation has been written regarding dampers .Then, the aforesaid equation has been transmitted to the situational setting ,the optimum quantity of each damper in the form of time function has been computed using optimization algorithm genetic and, the construct response has been determined. The results show that different positions of optimal in the structure height can have different effects on different responses. In a way that aligned damping in the upper parts of structures although much reduced Roof floor shift but are less than the rate of decline in shear force base. It also aligned damping in the bottom parts of structures are less than the rate of decline in base shear force roof and floor shift. Therefore, by determining optimized position of optimal by genetic algorithms that can simultaneously optimize the rate of reduction in the structure response.

Paper Details

Date Published: 13 January 2012
PDF: 5 pages
Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83492M (13 January 2012); doi: 10.1117/12.920259
Show Author Affiliations
Farzad Hatami, Research Institute of Petroleum Industry (Iran, Islamic Republic of)
Esmail Karimi, Azad Univ. (Iran, Islamic Republic of)


Published in SPIE Proceedings Vol. 8349:
Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis
Zhu Zeng; Yuting Li, Editor(s)

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