A LSTM surrogate modelling approach for caisson foundations

Abstract This study proposes a hybrid surrogate modelling approach with the integration of deep learning algorithm long short-term memory (LSTM) to identify the mechanical responses of caisson foundations in marine soils. The LSTM based surrogate model is first trained based on limited results generated from the SPH-SIMSAND based numerical simulations with a strong validation, thereafter it is applied to predict the mechanical responses of soil-structure interaction and the failure envelope of unknown caisson foundations with various specifications as testing. The results indicate that the LSTM based model is more flexible than macro-element method, because it can directly learn the failure mechanism of caisson foundation from the raw data, meanwhile guarantees a high computational efficiency and accuracy in comparison with physical and numerical modelling. LSTM based surrogated model shows a great potential of application in engineering practice.