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This article presents a novel technique for bridge damage identification that integrates the wavelet packet energy curvature difference (WPECD) method with recurrent neural networks using long short-term memory (RNN-LSTM) algorithms. The approach simulates damage by varying material stiffness and utilizes WPECD maps to train the RNN-LSTM model, achieving high accuracy and reliability in identifying structural damage. The proposed method demonstrates significant potential for application in monitoring the he

Author

anshul

Language

EN