Fracture acoustic emission signals identification of stay cables in bridge engineering application using deep transfer learning and wavelet analysis

Stay cables are typically exposed to the environment and traffic loading leading to degradations due to corrosion and cyclic loading after years’ in service. A non-destructive method to detect the defects of cables as early as possible is needed and important for adequate large-span bridge maintenance. Use of a status-driven acoustic emission (AE) monitoring Convolutional neural network (CNN) method is investigated by combing wavelet analysis and transfer deep learning. CNN is used to construct the relationship between AE signals’ scalograms and cable status. The trained CNN is suitable to identify the in-situ monitored signals and evaluate the current status of cables during the operation of a bridge. As a pilot study, the binary AE signals classification CNN is implemented to identify noise & fracture AE signals in static tests of a stay-cable. Accuracy of the method is investigated. In addition, the trained model is examined using AE signals which are not used in the machine learning to check possible improvements of the accuracy. Expectations in recognition of results and status-driven monitoring potentials are addressed in the paper.