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.

中文翻译：

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基于AI的描述粒状材料循环特性的模型

建模具有良好性能的排水和不排水条件下的粒状土壤循环行为仍然是一个挑战。这项研究提出了一种使用深度学习对颗粒材料的循环行为进行建模的新方法。为了捕获时间维度上的连续循环行为，采用了长短期记忆（LSTM）神经网络，该网络的特征在于顺序数据的预测，这意味着它提供了一种预测各种条件下土壤的连续行为的新颖方法。加载路径。由高级土壤本构模型生成的排水和不排水条件下循环荷载的综合数据集首先用于探索基于LSTM模型的合适框架。然后，基于LSTM的模型用于估算真实砂土的循环行为，即 不排水条件下的丰谷砂和不排水条件下的枫丹白露砂。将估计值与实际实验结果进行比较，这表明基于LSTM的模型可以同时模拟排水和不排水条件下的砂土循环行为，即（a）循环运动机理，有效应力的退化和大变形（b）在排水条件下的剪切应变累积和致密化。