STRUCTURAL DAMAGE IDENTIFICATION METHOD BASED ON ROUGH SET AND PROBABILISTIC NEURAL NETWORK

With the large structural health monitoring system successfully developed and applied, it has become to be focus how to make full use of the redundant and complementary information and assess on structural healthy states. A new structural damage identification method is proposed in this paper. In this method, rough set is employed to process initial data and reduce attributes in advance. Thus a probabilistic neural network (PNN) is employed to fuse multi-sensor data and conclude the damage identification results. To validate the proposed method, six damage patterns from a 7-DOF building model are identified finally, and a comparison is made. The results show that the method can reduce spatial dimension of data, as well as have a good consist with un-attributes reduction.