Application of LSTM approach for modelling stress–strain behaviour of soil

• Published in: Applied soft computing Vol. 100; p. 106959
• Main Authors: Zhang, Ning, Shen, Shui-Long, Zhou, Annan, Jin, Yin-Fu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2021
• Subjects: Bias at low stress levels; Laboratory test; LSTM approach; Stress history; Stress–strain behaviour; Bias at low stress levels; Laboratory test; LSTM approach; Stress–strain behaviour; Stress history

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 prop...