Development of a Prediction Algorithm for Column Shortening in High-Rise Buildings Using a Neural Network

Abstract:

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The objective of this study is to formulate and evaluate a new training algorithm of Neural Network to predict the inelastic shortening of reinforced concrete members using the column shortening data of high-rise buildings. The new training algorithm of Neural Network for the prediction of column shortening focuses on component of input data and training methods. The validity is examined by training and prediction process based on column shortening measuring data of high-rise buildings. The polynomial fit line of measuring data is used as the training data instead of measuring data. The result shows that the new Neural Network algorithm proposed in this study successfully predicts column shortening of high-rise buildings.

Info:

Periodical:

Key Engineering Materials (Volumes 348-349)

Edited by:

J. Alfaiate, M.H. Aliabadi, M. Guagliano and L. Susmel

Pages:

901-904

DOI:

10.4028/www.scientific.net/KEM.348-349.901

Citation:

W. J. Yang and W. H. Yi, "Development of a Prediction Algorithm for Column Shortening in High-Rise Buildings Using a Neural Network ", Key Engineering Materials, Vols. 348-349, pp. 901-904, 2007

Online since:

September 2007

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Price:

$35.00

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