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http://dx.doi.org/10.7474/TUS.2018.28.3.277

Prediction of the Stress-Strain Curve of Materials under Uniaxial Compression by Using LSTM Recurrent Neural Network  

Byun, Hoon (Department of Energy Systems Engineering, Seoul National University)
Song, Jae-Joon (Department of Energy Systems Engineering, Seoul National University)
Publication Information
Tunnel and Underground Space / v.28, no.3, 2018 , pp. 277-291 More about this Journal
Abstract
LSTM (Long Short-Term Memory) algorithm which is a kind of recurrent neural network was used to establish a model to predict the stress-strain curve of an material under uniaxial compression. The model was established from the stress-strain data from uniaxial compression tests of silica-gypsum specimens. After training the model, it can predict the behavior of the material up to the failure state by using an early stage of stress-strain curve whose stress is very low. Because the LSTM neural network predict a value by using the previous state of data and proceed forward step by step, a higher error was found at the prediction of higher stress state due to the accumulation of error. However, this model generally predict the stress-strain curve with high accuracy. The accuracy of both LSTM and tangential prediction models increased with increased length of input data, while a difference in performance between them decreased as the amount of input data increased. LSTM model showed relatively superior performance to the tangential prediction when only few input data was given, which enhanced the necessity for application of the model.
Keywords
Recurrent neural network; LSTM; Uniaxial compression test; Stress-strain curve;
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