@Article{cmes.2021.016172,
AUTHOR = {Tongming Qu, Shaocheng Di, Y. T. Feng, Min Wang, Tingting Zhao, Mengqi Wang},
TITLE = {Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {128},
YEAR = {2021},
NUMBER = {1},
PAGES = {129--144},
URL = {http://www.techscience.com/CMES/v128n1/43016},
ISSN = {1526-1506},
ABSTRACT = {This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling (DEM) and deep learning. A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive models. The low-dimensional principal stress-strain sequence pairs, measured from discrete element modelling of triaxial testing, are used to train recurrent neural networks, and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate transformation. Through detailed hyperparameter investigations, it is found that long short-term memory (LSTM) and gated recurrent unit (GRU) networks have similar prediction performance in constitutive modelling problems, and both satisfactorily predict the stress responses of granular materials subjected to a given unseen strain path. Furthermore, the unique merits and ongoing challenges of data-driven constitutive models for granular materials are discussed.},
DOI = {10.32604/cmes.2021.016172}
}