초고층 건축물의 부등축소량 예측을 위한 뉴랄-네트워크의 적용

Application of Neural Network to Prediction of Column Shortening of High-rise Buildings

The objectives of this study are to develop and evaluate the Neural Network algorithm which can predict the inelastic shortening such as the creep strain and the drying shrinkage strain of reinforced concrete members using the previous test data. New learning algorithms for the prediction of creep strain and the drying shrinkage strain are proposed focusing on input layer components and a normalization method for input data and their validity is examined through several test data. In Neural Network algorithm, the main input data to be trained are the compressive strength of the concrete, volume to surface ratio, curing condition, relative humidity, and the applied load. The results show that the new algorithms proposed herein successfully predict creep strain and the drying shrinkage strain.