| 초록 |
Rough set theory and artificial neural network are integrated into a model of seismic damage prediction for buildings. First the rough set theory is used to acquire the knowledge of classification, which includes the decision table construction, attribute discretization, attribute importance ranking, attribution reduction and rule abstract. Then the key components are extracted as the input of the neural network. The method reduces the structure of neural network model, and raises efficiency of training and accuracy of prediction. The importance ranking of these factors to earthquake - resistance performance can be obtained by this model. The research shows that the prediction results agree with actual seismic damage of multistory masonry building. |
