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

The objective of this study is to propose and evaluate a neural network algorithm to predict column shortening, including drying shrinkage and creep in high-rise RC buildings. A proposed neural network algorithm for the prediction of column shortening focuses on data processing and training methods....

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Bibliographic Details
Published in:Advances in structural engineering 2012-03, Vol.15 (3), p.509-523
Main Authors: Yang, Won-Jik, Lee, Jung-Han, Yi, Waon-Ho
Format: Article
Language:English
Subjects:
Algorithms
Columns (process)
Columns (structural)
Creep (materials)
High rise buildings
Mathematical models
Neural networks
Training
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Summary:The objective of this study is to propose and evaluate a neural network algorithm to predict column shortening, including drying shrinkage and creep in high-rise RC buildings. A proposed neural network algorithm for the prediction of column shortening focuses on data processing and training methods. The validity of the proposed neural network algorithm is examined through a training and prediction process based on column shortening measuring data of high-rise buildings. In the training data of a proposed neural network algorithm, the polynomial fit line of measuring data is used as the training data instead of measuring data. As a result, it has been verified that column shortening can be estimated by using the proposed neural network algorithm and that such a prediction is more accurate than what has been predicted by the conventional method using numerical models.
ISSN:1369-4332
2048-4011
DOI:10.1260/1369-4332.15.3.509