TY  - EJOU
AU  - Qu, Tongming 
AU  - Di, Shaocheng 
AU  - Feng, Y. T. 
AU  - Wang, Min 
AU  - Zhao, Tingting 
AU  - Wang, Mengqi 

TI  - Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2021
VL  - 128
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Deep learning; granular materials; constitutive modelling; discrete element modelling; coordinate transformation; LSTM; GRU

DO  - 10.32604/cmes.2021.016172