Rule induction for structural damage identification

Structural damage identification is becoming a worldwide research subject. Some machine learning methods have been used to solve this problem, and most of them are neural network methods. In this paper, three different rule inductive methods named as divide-and-conquer (DAC), bagging and separate-and-conquer (SAC) are investigated for predicting the damage position and extent of a concrete beam. Then radial basis function neural network (RBFNN) is used here for comparative purposes. The rule inductive methods, especially bagging is shown to obtain good prediction.