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<p>The rock digital twin is a virtual system that continuously learns real-time observations of the physical entity, allowing for the dynamic updating of the mechanical parameters in the numerical model. This paper develops a digital twin framework based on data from triaxial rock mechanics test, numerical simulation, and data assimilation (DA) algorithm, focusing on the mechanical behavior of metamorphic silty sandstone from Mogu tilting deformation body under three different confining pressures. Mechanical tests on the rock samples were conducted and numerical simulation integrating the Iterative Local Updating Ensemble Smoothing (ILUES) algorithm was implemented. This integrated approach combined modondel with experimental data, achieved the iterative updating of parameters. The updated parameters were subsequently fed back into the numerical model to predict its response. We evaluated the performance of the digital twin model by assessing the mechanical parameters and model prediction. The findings of this research are of significant importance in reducing uncertainties associated with model parameters in rock materials, thereby enhancing our insight to evaluate the risk assessment of geotechnical engineering.</p>

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A Digital Twin System for Rock Triaxial Mechanical Tests Incorporating Probabilistic Parameter Updates and Model Prediction Through Data Assimilation Technique

  • 期刊论文

  • 发表日期：

    2025年1月28日

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  • Changhao Lyu,
  • Weiya Xu,
  • Haijiang Wang,
  • Yizhe Wu,
  • Huanling Wang

摘要

The rock digital twin is a virtual system that continuously learns real-time observations of the physical entity, allowing for the dynamic updating of the mechanical parameters in the numerical model. This paper develops a digital twin framework based on data from triaxial rock mechanics test, numerical simulation, and data assimilation (DA) algorithm, focusing on the mechanical behavior of metamorphic silty sandstone from Mogu tilting deformation body under three different confining pressures. Mechanical tests on the rock samples were conducted and numerical simulation integrating the Iterative Local Updating Ensemble Smoothing (ILUES) algorithm was implemented. This integrated approach combined modondel with experimental data, achieved the iterative updating of parameters. The updated parameters were subsequently fed back into the numerical model to predict its response. We evaluated the performance of the digital twin model by assessing the mechanical parameters and model prediction. The findings of this research are of significant importance in reducing uncertainties associated with model parameters in rock materials, thereby enhancing our insight to evaluate the risk assessment of geotechnical engineering.