Deep long short-term memory (LSTM) networks for ultrasonic-based distributed damage assessment in concrete

This paper presented a comprehensive study on developing a deep learning approach for ultrasonic-based distributed damage assessment in concrete. In particular, two architectures of long short-term memory (LSTM) networks were proposed: (1) a classification model to evaluate the concrete's damage stage; (2) a regression model to predict the concrete's absorbed energy ratio. Two input configurations were considered and compared for both architectures: (1) the input was a single signal; (2) the inputs were four signals from four sides of the specimen. A comprehensive experimental study was designed and conducted on ground granulated blast furnace slag-based geopolymer concrete, providing a total number of 1920 ultrasonic signals from different damage stages. Unsupervised k-means clustering based on dynamic time warping (DTW) was implemented to cluster the ultrasonic response signals from the experimental study into five defined damage stages. The proposed LSTM architectures were successfully trained and validated using the experimental dataset. Moreover, the performance of the LSTM models was evaluated in noisy environments. The proposed LSTM models in this study used the time series of response signals for damage assessment. Therefore, the damage-sensitive features were automatically extracted by the LSTM layers. For comparison, a set of linear and nonlinear ultrasonic features were manually extracted from the response signals as damage-sensitive features, and their sensitivity to damage was investigated. Artificial neural networks were implemented to combine the extracted features and perform the same tasks defined for LSTM models. Comparing the two approaches showed that using the time series of ultrasonic response signals as the input of LSTM models outperforms the idea of using the manually extracted features. This study showed that the presented method is efficient, reliable, and promising for nondestructive evaluation of damage in concrete.