Soil electrical resistivity is an important parameter in geotechnical engineering. In this paper, a multivariate distribution model is used to predict soil electrical resistivity. The Box-Cox method was used to transform the soil parameters, which were then analysed by a correlation matrix. Multivariate distribution models with different input parameters were established, verified and analysed, and compared with conventional empirical models. The results show that the predictive accuracy of the multivariate distribution models was improved significantly by increasing the number of parameters. The correlation coefficient (R²) of the calibration data increased from 0.05 to 0.88, and that of the validation data increased from 0.06 to 0.83. The model with the best predictive accuracy was the R_E−{G, F, Sr} model (R² = 0.87, E(ε) = 1.22, COV(ε) = 0.98). The predictive accuracy of the multivariate distribution model was obviously higher than that of the conventional empirical model. The multivariate distribution model is an effective and simple way to predict soil electrical resistivity. The proposed model exhibits good performance using limited electrical resistivity data from frozen soils.

土壤电阻率是岩土工程中的一个重要参数。本文采用多元分布模型来预测土壤电阻率。Box-Cox 方法用于转换土壤参数，然后通过相关矩阵进行分析。建立、验证和分析了不同输入参数的多元分布模型，并与常规经验模型进行了比较。结果表明，通过增加参数数量，多变量分布模型的预测精度显着提高。校准数据的相关系数（R ²）从0.05增加到0.88，验证数据的相关系数从0.06增加到0.83。预测准确率最高的模型是R _E -{G, F, Sr} 模型 ( R ²  = 0.87, E ( ε ) = 1.22, COV( ε ) = 0.98)。多元分布模型的预测精度明显高于常规经验模型。多元分布模型是预测土壤电阻率的一种有效而简单的方法。所提出的模型使用来自冻土的有限电阻率数据表现出良好的性能。