An AI‐based model for describing cyclic characteristics of granular materials

Summary Modelling cyclic behaviour of granular soils under both drained and undrained conditions with a good performance is still a challenge. This study presents a new way of modelling the cyclic behaviour of granular materials using deep learning. To capture the continuous cyclic behaviour in time...

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Vydáno v:International journal for numerical and analytical methods in geomechanics Ročník 44; číslo 9; s. 1315 - 1335
Hlavní autoři: Zhang, Pin, Yin, Zhen‐Yu, Jin, Yin‐Fu, Ye, Guan‐Lin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 25.06.2020
Témata:
Computer simulation
constitutive model
Constitutive models
Cyclic loading
Cyclic loads
deep learning
Deformation
Deformation effects
Densification
Dimensions
Granular materials
Learning behaviour
liquefaction
Machine learning
Mathematical models
Modelling
neural network
Neural networks
Sand
Shear strain
Soil
Soils
ISSN:0363-9061, 1096-9853
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Shrnutí:Summary Modelling cyclic behaviour of granular soils under both drained and undrained conditions with a good performance is still a challenge. This study presents a new way of modelling the cyclic behaviour of granular materials using deep learning. To capture the continuous cyclic behaviour in time dimension, the long short‐term memory (LSTM) neural network is adopted, which is characterised by the prediction of sequential data, meaning that it provides a novel means of predicting the continuous behaviour of soils under various loading paths. Synthetic datasets of cyclic loading under drained and undrained conditions generated by an advanced soil constitutive model are first employed to explore an appropriate framework for the LSTM‐based model. Then the LSTM‐based model is used to estimate the cyclic behaviour of real sands, ie, the Toyoura sand under the undrained condition and the Fontainebleau sand under both undrained and drained conditions. The estimates are compared with actual experimental results, which indicates that the LSTM‐based model can simultaneously simulate the cyclic behaviour of sand under both drained and undrained conditions, ie, (a) the cyclic mobility mechanism, the degradation of effective stress and large deformation under the undrained condition, and (b) shear strain accumulation and densification under the drained condition.
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ISSN:0363-9061
1096-9853
DOI:10.1002/nag.3063