Damage Diagnosis of Beam Bridge Based on PCA-EWM Two-level Feature Fusion and NGO-GRU
In order to improve sensitivity and noise immunity of a single index to damage in damage identification,based on modal strain energy theory,a two-level feature fusion method combining principal component analysis(PCA)and entropy weight method(EWM)were proposed.The northern goshawk optimization(NGO)algorithm combined with gated recurrent unit(GRU)were used for bridge damage degree prediction.Firstly,based on traditional modal strain energy theory,the diagonal modal strain energy ratio was constructed,and then change rate of the diagonal modal strain energy ratio,dissipation rate of the diagonal modal strain energy ratio,and normalized difference index of the diagonal modal strain energy ratio were derived.Secondly,principal component analysis was used to extract features within the index,and entropy weight method was used to fuse features between indexes.Finally,weighted decision index(WDI)was constructed.The single modal strain energy derivative index was input into the NGO-GRU hybrid neural network,as well as damage degree was output,so as to established the relationship between index value and damage degree,and then realized damage quantification.The proposed method was verified by a three-span continuous beam bridge numerical model.The results show that weighted decision index has good damage location ability and noise immunity.The hybrid neural network has high damage prediction accuracy,with a prediction accuracy rate of 91.14％.
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维普
