Prediction of Structural Damage Trend Based on Multi-Model Integration

Because a single prediction model cannot fully reflect the complex laws and structural vibration information, in order to improve the accuracy of structural health trend prediction and make full use of the advantages of each model, a multi-model integrated prediction method was proposed based on depth-confidence network (DBN), long-short-term memory neural network (LSTM) and wavelet neural network (WNN). Firstly, decomposing engineering structural vibration signal into instantaneous frequency with variation mode decomposition (VMD) and Hilbert transform, the system was arranged to take the instantaneous frequency as the input for the multi-model integration to decide the weight coefficients based on a combination of the weighted average method and the voting method. And then the influence of different weights on the prediction accuracy was analyzed. Finally, some verified experiments were carried out. The experimental results show that the prediction results of the multi-model integration method are closer to the actual values when the weight value \omega equals to 0.8. Compared with the traditional arithmetic average model and other three single prediction models, the multi-model integration method can provide better prediction performance and higher prediction accuracy.