Tongming Qu1, Shaocheng Di2, Y. T. Feng1,3,*, Min Wang4, Tingting Zhao3, Mengqi Wang1
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.1, pp. 129-144, 2021, DOI:10.32604/cmes.2021.016172
- 28 June 2021
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 More >