Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

It is proposed in this paper a novel two-stage structural damage detection approach using fuzzy neural networks (FNNs) and data fusion techniques. The method is used for structural health monitoring and damage detection, particularly for cases where the measurement data is enormous and with uncertainties. In the first stage of structural damage detection, structural modal parameters derived from structural vibration responses are fed into an FNN as the input. The output values from the FNN are defuzzified to produce a rough structural damage assessment. Later, in the second stage, the values output from three different FNN models are input directly to the data fusion center where fusion computation is performed. The final fusion decision is made by filtering the result with a threshold function, hence a refined structural damage assessment of superior reliability. The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory. Furthermore, the simulation result also shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.