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<p>The thaw settling coefficient (TSC) of frozen soil serves as a crucial metric for assessing frozen soil thawing deformation. This work investigates the relationship of four parameters - freezing temperature (<i>T</i>), dry density (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{\rho\:}_{d}\)</EquationSource> </InlineEquation>), water content (<i>w</i>), and overlying load (<i>P</i>) with the TSC of frozen soil. Utilizing a nonlinear prediction approach, multiple models are employed to predict the TSC values of frozen soil under varying influencing factors. Leveraging an experimental database comprising 841 data samples, the study conducts a series of sensitivity analyses to identify the most influential parameters in each nonlinear model and to determine the optimal performing model. Results from artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models exhibit superior accuracy in predicting the TSC of frozen soil. The ANFIS model undergoes validation twice using new databases, demonstrating its reliability in meeting engineering requirements. Employing the mutual information (MI) analysis method, the study quantifies the impact of different influencing factors on the TSC of frozen soil, offering valuable insights for predicting TSC of frozen soil.</p>

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Prediction of thaw settling coefficient of frozen soil using machine learning techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • Ke Wang,
  • Shuang Liu,
  • Wenqiang Xing,
  • LianZhen Zhang,
  • Shuang Tian,
  • Mengyao Qiu,
  • Shanzhen Li

摘要

The thaw settling coefficient (TSC) of frozen soil serves as a crucial metric for assessing frozen soil thawing deformation. This work investigates the relationship of four parameters - freezing temperature (T), dry density ( \(\:{\rho\:}_{d}\) ), water content (w), and overlying load (P) with the TSC of frozen soil. Utilizing a nonlinear prediction approach, multiple models are employed to predict the TSC values of frozen soil under varying influencing factors. Leveraging an experimental database comprising 841 data samples, the study conducts a series of sensitivity analyses to identify the most influential parameters in each nonlinear model and to determine the optimal performing model. Results from artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models exhibit superior accuracy in predicting the TSC of frozen soil. The ANFIS model undergoes validation twice using new databases, demonstrating its reliability in meeting engineering requirements. Employing the mutual information (MI) analysis method, the study quantifies the impact of different influencing factors on the TSC of frozen soil, offering valuable insights for predicting TSC of frozen soil.