Jaya-Based Long Short-Term Memory Neural Network for Structural Damage Identification with Consideration of Measurement Uncertainties

Abstract

Structural damage identification based on the long short-term memory (LSTM) neural network (NN) is proposed in this study. To address the hyperparameters selection problem for the LSTM, the Jaya algorithm is applied to minimize the difference between the observed and predicted data in the validation datasets and determine the LSTM network’s optimal hyperparameters, including the number of nodes, learning rate, and maximum iteration number. Frequency-domain data, such as natural frequencies and mode shapes, are used as input of the network, and then damage locations and extents are utilized as output. Measurement uncertainties are introduced during NN training to improve the robustness of the model. Numerical and experimental studies showed that the proposed method can identify structural damage accurately when measurement noise is considered, even for damage scenarios beyond the training datasets.