昆仑山地区冻融土导热系数试验测试与预测模型研究

为探究青藏高原工程走廊带昆仑山地区冻融土导热系数基本特征，采用瞬态平面热源法对钻取的349组冻土试样和245组融土试样导热系数进行了测试，分析了五类土导热系数分布特征及天然含水率、干密度与导热系数的偏相关性，并以两者为变量因素建立了经验公式拟合、支持向量回归（SVR）和径向基（RBF）神经网络导热系数预测模型。结果表明:冻融土导热系数整体均呈粗颗粒土大于细颗粒土特征，且冻土和融土导热系数随土性分布规律存在差异；天然含水率、干密度与导热系数均呈正相关性，不同土类偏相关性结果差异明显，典型土导热系数二元经验回归方程表现为非线性拟合结果。对比三种预测模型下各典型土冻融土导热系数预测结果，全风化千枚岩、角砾及砾砂三种预测模型效果整体较佳，粉土的SVR及RBF神经网络预测精度较好；融土导热系数预测效果整体略优于冻土，SVR及RBF神经网络模型下角砾、粉土及全风化千枚岩融土导热系数预测精度较高。综合导热系数模型预测效果和误差结果分析可得，SVR和RBF神经网络模型预测效果显著优于经验方程拟合，后者针对部分土性拟合效果相对较好，可满足一般工程估算需求；SVR和RBF神经网络预测模型针对不同土性导热系数预测效果呈差异性变化，整体预测效果相当，且预测精度更高、应用土性范围更广。

Research on experimental tests and prediction models of thermal conductivity of freezing-thawing soil in the Kunlun Mountains

In order to explore the basic laws of freezing and thawing soil in the Qinghai-Tibet Engineering Corridor in the Kunlun Mountains area,the coefficient of thermal conductivity of 349 groups of drilling frozen soil samples and 245 groups of thawing soil samples is tested by the transient plane heat source method.The characteristics of five kinds of soil thermal conductivity distribution and natural moisture content,dry density and the partial correlation coefficient of thermal conductivity are analyzed,and the experience for both variables in fitting formula,support vector regression(SVR)and radial basis(RBF)neural network prediction model of thermal conductivity are established.The results show that the thermal conductivity of freezing-thawing soil is larger than that of fine-grained soil,and the thermal conductivity of freezing-thawing soil varies with the distribution of soil properties. Natural moisture content and dry density are positively correlated with thermalconductivity, and the partial correlation results of different soil types are significantly different. The binaryempirical regression equation of typical soil thermal conductivity is shown as a nonlinear fitting result. The resultsof thermal conductivity prediction of the typical soil and freezing-thawing soil under three prediction models showthat the prediction effect of fully weathered phyllite, breccia and gravel sand is better, and the prediction accuracyof SVR and RBF neural network of silty soil is also better. On the whole, the prediction effect of thermalconductivity on thawed soil is slightly better than that of frozen soil, and the prediction accuracy of thermalconductivity of breccias, silty soil and fully weathered meltwater is higher under the SVR and RBF neural networkmodels. The prediction results and error analysis of the three thermal conductivity models show that the predictionresults of the SVR and RBF neural network models are significantly better than that of the empirical fittingequation method. The prediction effect of SVR and RBF neural network prediction models varies with differentsoil thermal conductivities, and the overall prediction effect is similar, with higher prediction accuracy and widerrange of soil application.