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.

中文翻译：

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沉箱基础的 LSTM 代理建模方法

摘要 本研究提出了一种混合代理建模方法，结合深度学习算法长短期记忆 (LSTM) 来识别海洋土壤中沉箱基础的机械响应。基于 LSTM 的代理模型首先根据基于 SPH-SIMSAND 的数值模拟产生的有限结果进行训练，并得到了强有力的验证，然后将其应用于预测土-结构相互作用的力学响应和具有各种不同的未知沉箱基础的破坏包络。规格作为测试。结果表明，基于LSTM的模型比宏观元方法更灵活，因为它可以直接从原始数据中学习沉箱基础的失效机理，与物理和数值建模相比，同时保证了较高的计算效率和准确性。基于 LSTM 的代理模型在工程实践中显示出巨大的应用潜力。