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Vibration-based Multi-class Damage Detection and Localization Using Long Short-Term Memory (LSTM) Networks

In this paper, a novel method is proposed for detecting and localizing structural damage by classifying acceleration responses of a structure using a long short-term memory (LSTM) network. Windows of samples are extracted from acceleration responses in a novel data pre-processing pipeline, and an LSTM network is developed to classify the signals into multiple classes. A predicted classification of a signal by the LSTM network into one of the damage levels indicates a damage detection. Furthermore, multiple signals obtained from the vibration sensors placed on a structure are provided as input to the LSTM model, and the resulting predicted class probabilities are used to identify the locations with high probability of damage. The proposed method is validated on the experimental setup of the Qatar University Grandstand Simulator (QUGS) for binary classification, as well as, full-scale study of the Z24 bridge benchmark data for multi-class damage classification. Experiments show that the proposed LSTM-based method performs on par with 1D convolutional neural networks (1D CNN) on the QUGS dataset, and outperforms the 1D CNN on the Z24 dataset. The novelty of this study lies in the use of recurrent neural network based LSTM for vibration data for multi-class damage identification and localization.