This paper presents a new trial to reproduce soil stress–strain behaviour by adapting a long short-term memory (LSTM) deep learning method. LSTM is an approach that employs time sequence data to predict future occurrences, and it can be used to consider the stress history of soil behaviour. The proposed LSTM method includes the following three steps: data preparation, architecture determination, and optimisation. The capacity of the adapted LSTM method is compared with that of feedforward and feedback neural networks using a new numerical benchmark dataset. The performance of the proposed LSTM method is verified through a dataset collected from laboratory tests. The results indicate that the LSTM deep-learning method outperforms the feed forward and feedback neural networks based on both accuracy and the convergence rate when reproducing the soil’s stress–strain behaviour. One new phenomenon referred to as “bias at low stress levels”, which was not noticed before, is first discovered and discussed for all neural network-based methods.

本文提出了一项新的试验，通过改编长期短期记忆（LSTM）深度学习方法来重现土壤应力-应变行为。LSTM是一种使用时间序列数据来预测未来发生的方法，可以用来考虑土壤行为的应力历史。提议的LSTM方法包括以下三个步骤：数据准备，架构确定和优化。使用新的数字基准数据集，将适应的LSTM方法的能力与前馈和反馈神经网络的能力进行比较。通过从实验室测试收集的数据集，可以验证所提出的LSTM方法的性能。结果表明，LSTM深度学习方法在再现土壤的应力应变行为时，在准确性和收敛速度方面都优于前馈和反馈神经网络。首次针对所有基于神经网络的方法发现并讨论了一种新现象，称为“低应力水平的偏差”，此现象以前未曾发现。