A LSTM surrogate modelling approach for caisson foundations

Pin Zhang, Zhen Yu Yin, Yuanyuan Zheng, Fu Ping Gao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


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 languageEnglish
Article number107263
JournalOcean Engineering
Publication statusPublished - 15 May 2020


  • Caisson foundation
  • Failure envelope
  • Long short-term memory
  • Smoothed particle hydrodynamics

ASJC Scopus subject areas

  • Environmental Engineering
  • Ocean Engineering


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