Finite Element Method And Rbf Neural Network-based Structural Health Monitoring

Research of structure health monitoring is one of the hot issues in current civil engineering. Among it, the research of structure damage diagnosis is the key point and difficult part of structure health monitoring. The technique of vibration modal analysis and artificial neural network is suitable to solve the problem.To accurately orient the damage location and identify the damage degree, an effective method of damage diagnosis is proposed. This paper introduced how to obtain the dynamic response of structure by environmental vibration experiment and the application of it, then discussed the method of time and frequency analysis based on the theory of wavelet. The identification of the structure damage, the process of structure damage orientation and identification of structure damage degree and the method using modal frequency to damage diagnosis are presented in this paper. A cantilever beam is chosen as the research object. The cantilever beam is simulated by using FEM analysis, the modal parameter is obtained by modal analysis, then constitute damage indices of natural frequency, so the damage mdices of different categories are made as the input parameters of RBF neural network to train the network and identify the damage. The procedure of RBF using MATLAB to orient the damage location and identify the damage degree is effective. A method that different index adopted in different damage diagnosis stage is proposed. At last the method of combination of structure dynamic characteristics analysis and neural network in structure damage diagnosis is summarized and the future research direction is presented.Lastly, using FEM method, a real damaged structure is taken as numerical example using ADINA, the studies of reinforcement and leaning rectification of the building are shown. The purpose of this simulation is to justify that through this method, the partial damage of structure can be prevented in the rectifying process.