Application Of Convolutional Neural Network In Bridge Damage Identification

In the current era of big data background,deep learning has attracted more and more scholars’ attention.As one of the important algorithms in deep learning,convolutional neural network has an obvious advantage in reducing the order of parameters compared with traditional algorithms.Due to its ability to automatically extract the internal features of big data,convolutional neural network has developed rapidly in recent years.This paper takes convolution nerve as the research object in the application of damage identification,analyses the advantages of convolutional neural network compared with traditional damage identification method,and introduces several classic model of convolutional neural network and the role of each layer.Finally with the help of finite element method and dynamics of vibration experiment,convolution neural network application in the damage identification is discussed.In the damage identification of finite element model of simply-supported beam with single damage location,the study shows that the convolutional neural network has a high accuracy rate for the single damage location situation.In the damage identification of finite element model of the simply-supported beam with double-damage position,the study shows that compared with the single-damage case,the recognition accuracy of the convolutional neural network in the case of double-damage position is lower,but it also achieves considerable recognition accuracy.Through the above research process,it is found that increasing the data batch through a series of data augmentation measures can effectively avoid the convolutional nerve falling into local fitting and improve the universality of the network.By force hammer excitation experiment of steel beam with double-damage position,the effectiveness of convolution neural network in damage identification is validated.Research shows that the convolutional neural network can successfully predict the real damage location of steel beam through testing data after a training of data of finite element model,a certain degree of universality of it has been proved.A vibration experiment of a complete steel beam with the same structural parameters was carried out,and the convolutional neural network was trained by the collected acceleration signal of it.Finally,the feasibility of the data-driven idea in the convolutional neural network was verified successfully by setting the mean square error threshold.In the process of research,it is found that: the convolutional neural network has a certain degree of anti-noise ability;The convolutional neural network can solve the problem that the traditional damage identification method depends on the accurate finite element model;By setting the threshold of damage function in advance,the convolutional neural network has the potential to be applied in health monitoring.