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.
Original language | English |
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Article number | 107263 |
Journal | Ocean Engineering |
Volume | 204 |
DOIs | |
Publication status | Published - 15 May 2020 |
Keywords
- Caisson foundation
- Failure envelope
- Long short-term memory
- Smoothed particle hydrodynamics
ASJC Scopus subject areas
- Environmental Engineering
- Ocean Engineering