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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.

The tsunami that hit Indonesia on December 26, 2004 took the lives of about 300,000 people and caused massive property damage amounting to more than 10 billion dollars. Another earthquake hit the northeastern coast of Japan on March 11, 2011 and triggered a tsunami that battered Japan's coast, killing 15,800 and causing property damage amounting to 25 trillion yen. Despite this trend, no feasible actions have been taken on the buildings in Korea to reduce tsunami-induced damage. Therefore, this study will present the method of building's resistance force evaluation and wave force for the building's safety for the tsunami. Then, according to the inundation depth we examined the safety evaluation of masonry buildings in the shore that occupy more than 80% in Korean buildings.

In general， hysteresis models that are applied to