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物理参数影响粉质粘土渗透系数变化规律 被引量:5

Influence of physical parameters on variation of permeability coefficient of silty clay
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摘要 为揭示粉质粘土渗透系数与土体颗粒级配、粒度成分、含水率、孔隙比等物理参数的相关关系及计算公式,以取自安徽省运漕幅、无为县幅的粉质粘土为研究对象,采用数理分析和实验的方法,研究非饱和粉质粘土的液性指数、含水率、孔隙比等物理指标对渗透系数变化规律的影响,此外,基于各物理参数与渗透系数的相关性,采用物理参数对数形式的不同组合方式对粉质粘土渗透系数进行多元回归分析.研究结果表明:粉质粘土渗透系数随着液性指数、含水率、孔隙比三个主要物理参数的增大而增大;推导出适用于一定范围内粉质粘土渗透系数的经验计算表达式,有利于完善非饱和粉质粘土渗透系数的研究. In order to reveal the correlation between permeability coefficient of silty clay and physical parameters such as particle size distribution,particle size composition,water content and void ratio,the silty clay from Yuncao and Wuwei county in Anhui province was taken as the research object.The effects of physical indexes such as liquid index,water content and void ratio of unsaturated silty clay on the variation of permeability coefficient were studied by means of mathematical analysis and experiments.Furthermore,based on the correlation between physical parameters and permeability coefficient,the permeability coefficient of silty clay is analyzed by multiple regression analysis using different combinations of physical parameters in logarithmic form.The results show that the permeability coefficient of silty clay increases with the increase of three main physical parameters:liquid index,water content and void ratio.The empirical formula for calculating the permeability coefficient of silty clay in a certain range is deduced,which is helpful to improve the study of the permeability coefficient of unsaturated silty clay.
作者 杜怡韩 黄博 曹兵 符文熹 DU Yihan;HUANG Bo;CAO Bing;FU Wenxi(College of Civil Engineering and Architecture,Anhui Polytechnic University,Wuhu 241000,China;College of Water Resource&Hydropower,Sichuan University,Chengdu 610065,China)
出处 《辽宁工程技术大学学报(自然科学版)》 CAS 北大核心 2019年第2期148-153,共6页 Journal of Liaoning Technical University (Natural Science)
基金 国家自然科学基金(41772321) 安徽省高等学校自然科学研究重点项目(KJ2018A0108) 安徽工程大学引进人才科研启动基金(2015YQQ014)
关键词 粉质粘土 渗透系数 液性指数 孔隙比 多元线性回归 silty clay permeability coefficient liquid index void ratio multiple linear regression
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