摘要
非饱和渗透系数直接测量代价较高,且精度不能完全保证,因此,基于达西定律,建立饱和/非饱和渗透系数的简便实用预测模型具有重要的理论和实践意义。采用自制的非饱和渗透装置测试了不同干密度下延安压实黄土的非饱和渗透系数,并运用核磁共振技术测定了其孔径分布曲线。基于孔隙分布特征将达西定理微分化,建立了孔隙比与饱和/非饱和渗透系数关系模型。研究结果表明:预测模型中的参数(D和B)可用孔径分布曲线上两点(峰值点和半幅点)的累计孔隙体积与孔径在双对数坐标中连成直线的斜率和截距确定;孔隙比和优势孔径在双对数坐标中具有线性关系,模型参数均可用孔隙比表示;模型预测结果与实测值基本吻合,具有简便、可靠和实用性。
The direct measurement of unsaturated permeability coefficient is costly and the accuracy cannot be fully guaranteed.Therefore, the establishment of a simple and practical prediction model for saturated/unsaturated permeability coefficient based on Darcy’s law has important theoretical and practical significance. In this paper, a self-made unsaturated permeability device was used to test the unsaturated permeability coefficient of Yan’an compacted loess with different dry densities, and the pore size distribution curve was determined by nuclear magnetic resonance technology. Based on the characteristics of pore distribution, Darcy’s theorem is differentiated, and the relationship model between pore ratio and saturated/unsaturated permeability coefficient is established. The research results show that the parameters(D and B) in the prediction model can be determined by the slope and intercept of the cumulative pore volume at two points(peak point and half-width point) on the pore size distribution curve and the pore size in a straight line in double logarithmic coordinates;the porosity ratio and the dominant pore diameter have a linear relationship in the double logarithmic coordinates, and the model parameters can be expressed by the porosity ratio;the predicted results of the model are basically consistent with the measured values, which suggests that the proposed model is simple, reliable and practical.
作者
王海曼
倪万魁
WANG Hai-man;NI Wan-kui(College of Geology Engineering and Geomatics,Chang’an University,Xi’an,Shaanxi 710054,China)
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2022年第3期729-736,共8页
Rock and Soil Mechanics
基金
国家自然科学基金项目(No.41931285)。
关键词
压实黄土
非饱和渗透
渗透系数
孔径分布
预测模型
compacted loess
unsaturated infiltration
permeability coefficient
pore size distribution
prediction model
