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• W4225132530 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." @default.
• W4225132530 created "2022-05-01" @default.
• W4225132530 creator A5028747236 @default.
• W4225132530 creator A5057900744 @default.
• W4225132530 creator A5073180390 @default.
• W4225132530 date "2022-06-03" @default.
• W4225132530 modified "2023-10-18" @default.
• W4225132530 title "Jaya-Based Long Short-Term Memory Neural Network for Structural Damage Identification with Consideration of Measurement Uncertainties" @default.
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• W4225132530 doi "https://doi.org/10.1142/s0219455422501619" @default.
• W4225132530 hasPublicationYear "2022" @default.
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