Research on damage localization of principal component analysis-based probabilistic neural network

As the probabilistic neural network (PNN) describes measurement data in a Bayesian probabilistic approach, it shows great potential for structural damage detection in noisy conditions. Meanwhile, the size of PNN increases as the learning samples increase. This reduces the running velocity. Based on this, a damage localization method called PNN is proposed based on principal component analysis in this paper. Three PNN models, namely, the traditional PNN (TPNN), the principal component analysis PNN (PCAPNN) and the adaptive PNN (APNN) models are utilized to detect the damage location of a suspension bridge respectively. This study shows that the identification accuracy (IA) of damage localization using the APNN is the best, the IA using the TPNN is the worst, and the IA using the PCAPNN is between the former two models. But the training time using the APNN is very long, and the size of the model is relatively large. Meanwhile, the others hardly need time to train the PNN models, and the size of PCAPNN reduces to that of other two models from 1/3 to 1/4. Furthermore, in low noise level, the IA using PCAPNN is almost the same as the APNN.