Multi-fidelity data-driven modelling of rate-dependent behaviour of soft clays

ABSTRACT

Conventional phenomenological elasto-viscoplastic models include numerous parameters that need to be calibrated by case-specific experiments. Data-driven modelling has recently emerged and provided an alternative to constitutive modelling. This study proposes a modelling framework based on multi-fidelity data to model the rate-dependent behaviour of soft clays. In this framework, low-fidelity (LF) data generated by an elasto-viscoplastic model and high-fidelity (HF) data from experimental tests are necessary. Stress–strain-strain rate correlations behind LF and HF data can be captured by long short-term memory and feedforward neural networks, respectively, such that final predictions can be given by a multi-fidelity residual neural network (MR-NN). Such a framework with the same LF data is applied in Hong Kong marine deposits and Merville clay to investigate its feasibility and generalisation ability. In addition, the effect of LF data on the performance of MR-NN is discussed to verify the robustness of the framework. All results demonstrate that rate-dependent undrained shear strength and pore-water pressure can be accurately modelled through the framework, showing adaptive non-linear modelling capability, less demand for experimental data, and superior robustness. These characteristics indicate a considerable potential in modelling the rate-dependent behaviour of clays.

KEYWORDS:

• Clay
• multi-fidelity
• data-driven
• neural network
• rate-dependent behaviour
• constitutive model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research was financially supported by the Research Grants Council (RGC), University Grants Committee of Hong Kong Special Administrative Region Government (HKSARG) of China [grant numbers 15217220, N_PolyU534/20, UGC/FDS13/E02/20].