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The Optimal Column Grouping Technique for the Compensation of Column Shortening  

Kim, Yeong-Min (명지대학교 건축학부)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.24, no.2, 2011 , pp. 141-148 More about this Journal
Abstract
This study presents the optimal grouping technique of columns which groups together columns of similar shortening trends to improve the efficiency of column shortening compensation. Here, Kohonen's self-organizing feature map which can classify patterns of input data by itself with unsupervised learning was used as the optimal grouping algorithm. The Kohonen network applied in this study is composed of two input neurons and variable output neurons, here the number of output neuron is equal to the column groups to be classified. In input neurons the normalized mean and standard deviation of shortening of each columns are inputted and in the output neurons the classified column groups are presented. The applicability of the proposed algorithm was evaluated by applying it to the two buildings where column shortening analyses had already been performed. The proposed algorithm was able to classify columns with similar shortening trends as one group, and from this we were able to ascertain the field-applicability of the proposed algorithm as the optimal grouping of column shortening.
Keywords
column shortening; optimal column grouping; Kohonen network; self-organizing feature map;
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Times Cited By KSCI : 4  (Citation Analysis)
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