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http://dx.doi.org/10.12989/sem.2017.63.2.237

Estimation of fundamental period of reinforced concrete shear wall buildings using self organization feature map  

Nikoo, Mehdi (Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University)
Hadzima-Nyarko, Marijana (Faculty of Civil Engineering, University of J.J. Strossmayer)
Khademi, Faezehossadat (Civil and Environmental Engineering Department, Illinois Institute of Technology)
Mohasseb, Sassan (Technical Director Smteam Gmbh)
Publication Information
Structural Engineering and Mechanics / v.63, no.2, 2017 , pp. 237-249 More about this Journal
Abstract
The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.
Keywords
fundamental period; Reinforced Concrete Shear Wall (RC SW) buildings; Genetic Algorithm (GA); nonlinear regression analysis; Self-Organization Feature Map (SOFM);
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Times Cited By KSCI : 7  (Citation Analysis)
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