A method for identifying cable damage in cable-stayed bridges by combining CNN and LSTM neural network
In cable structures,the cable response and damage are in a highly nonlinear state and the accuracy is often poor in conventional mathematical models for cable damage identification.A finite element model of a seven-wire strand cable was created to address this problem,and a CNN&LSTM neural network damage identification method based on the combination of indicators was proposed.The finite element model of the cable was used to simulate four types of damage conditions and extract response features for each.Different indicators such as the rate of total energy change,frequency,energy ratio deviation,and energy ratio variance were compared and analyzed for their representation of damage severity.A composite damage indicator combining energy and frequency was established.The recognition results of the combined damage indicator using the CNN&LSTM neural network were compared and analyzed against individual indicators using the conventional convolutional neural network(CNN)and long and short term memory(LSTM)network.Results reveal that the accuracy of cable damage identification using the combined indicators with the CNN&LSTM deep learning network is the highest,reaching 96.67%,which is higher than the accuracy of CNN alone(86.63%)and LSTM alone(82.15%).The results demonstrate that CNN&LSTM have great potential in cable damage identification in cable-stayed structures.
cable finite elementconvolutional neural networklong and short term memorydamage identificationcomposite indicators
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