Structural damage identification for bridges based on transfer learning and 1D convolutional neural network
Aiming at the discrepancy between the finite element model and the actual structures during the damage pattern identification of the actual bridges,to improve the accuracy of the deep neural network trained on the finite element numerical simulation dataset in identifying the damage patterns of the actual bridges,a method combining Transfer Learning (TL) with 1D Convolutional Neural Network (CNN) for structural damage identification of bridge structures was proposed in this paper.Firstly,the 1D-CNN model was trained based on the structural finite element numerical simulation data,and the model with better damage identification results was selected as the source model.Then,the network structure and weight parameters of the source model were transferred,and the pre-trained model was obtained from the actual structure data.Finally,the target model was obtained by fine-tuning the pre-training model.Through the laboratory test of a three-layers steel frame structure and the field test of a simple steel truss girder bridge in Japan,the structural damage pattern identification accuracy of three neural network models was compared,namely,the source model (model Ⅰ),the CNN model (model Ⅱ) trained only with measured data,and the target model (model Ⅲ) trained by transfer learning.The results confirm that the maximum damage pattern identification accuracy of the three CNN models reaches 63.44％,98.44％ and 99.06％ respectively in the laboratory test.In the field test,the maximum damage pattern identification accuracy of the three CNN models is 59.50％,97.00％,99.50％,respectively.For different structures,the target model (model Ⅲ) has the highest damage pattern identification accuracy and the fastest convergence rate,which is better than the other two CNN models.The transfer convolutional neural network method has good damage identification ability for the actual bridges.The results can provide an effective way to resolve the problem of structural damage identification in the case of limited data.
万方数据
维普
