STRUCTURAL DAMAGE IDENTIFICATION METHOD BASED ON ROUGH SET AND DATA FUSION

In order to make full use of the redundant and complementary information and to assess the structural health states from a structural health monitoring system, a new damage identification method is proposed by integrating with rough set, data fusion and probabilistic neural network (PNN). In this method, rough set is used to reduce attributes so as to decrease spatial dimensions of data firstly, then PNN is utilized to fuse redundant and uncertain information and fusion decision-making and damage identification results are made. It is noteworthy that K-means clustering was employed to discrete data during the attributes reduction. To validate the efficiency of the proposed method, multi-damage patterns from two numerical examples were identified finally, and a comparison was made between the proposed method and a PNN classifier without data processing by rough set. The results show that the proposed method can not only reduce spatial dimension of data, but also have good damage identification accuracy.