Geng-Fu He, Pin Zhang, Z. Yin, Yin‐Fu Jin, Yi Yang
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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.
期刊介绍:
Georisk covers many diversified but interlinked areas of active research and practice, such as geohazards (earthquakes, landslides, avalanches, rockfalls, tsunamis, etc.), safety of engineered systems (dams, buildings, offshore structures, lifelines, etc.), environmental risk, seismic risk, reliability-based design and code calibration, geostatistics, decision analyses, structural reliability, maintenance and life cycle performance, risk and vulnerability, hazard mapping, loss assessment (economic, social, environmental, etc.), GIS databases, remote sensing, and many other related disciplines. The underlying theme is that uncertainties associated with geomaterials (soils, rocks), geologic processes, and possible subsequent treatments, are usually large and complex and these uncertainties play an indispensable role in the risk assessment and management of engineered and natural systems. Significant theoretical and practical challenges remain on quantifying these uncertainties and developing defensible risk management methodologies that are acceptable to decision makers and stakeholders. Many opportunities to leverage on the rapid advancement in Bayesian analysis, machine learning, artificial intelligence, and other data-driven methods also exist, which can greatly enhance our decision-making abilities. The basic goal of this international peer-reviewed journal is to provide a multi-disciplinary scientific forum for cross fertilization of ideas between interested parties working on various aspects of georisk to advance the state-of-the-art and the state-of-the-practice.