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Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data
Computer Modeling in Engineering & Sciences, Volume: 128, Issue: 1, Pages: 129 - 144
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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 as...
|Published in:||Computer Modeling in Engineering & Sciences|
Computers, Materials and Continua (Tech Science Press)
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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.
Deep learning; granular materials; constitutive modelling; discrete element modelling; coordinate transformation; LSTM; GRU
College of Engineering