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

摘要

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.