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Evaluation of the Hydraulic Conductivity of Geosynthetic Clay Liners
Clays and Clay Minerals ( IF 2.2 ) Pub Date : 2022-02-18 , DOI: 10.1007/s42860-021-00167-0
Juan Hou 1, 2, 3 , Lei Liu 1, 4, 5 , Yuyang Teng 2 , Shifen Bao 2 , Hao Li 2
Affiliation  

The hydraulic conductivity of geosynthetic clay liners (GCLs) is not fully understood and certain gaps in knowledge are still present, such as the effect of coupled mechanical and chemical processes. The current study aimed to develop a simplified mathematical model to predict the hydraulic conductivity of GCLs, particularly regarding the coupled effects of mechanical and chemical processes. Based on Darcy's Law and Poiseuille’s Law, the method combines diffuse double layer (DDL) theory and fractal theory. External factors such as confining pressure and the concentration of the permeating solution, and inherent properties such as exchangeable cations, ionic radius, montmorillonite surface fractal dimension, the distance between two montmorillonite layers (m) after swelling at the exchangeable cation i (i denotes the primary exchangeable cations, such as Na+, Ca2+, K+, and Mg2+ in bentonite), density, and coefficient of viscosity of interlayer water between two montmorillonite layers, were considered. The proposed theoretical model gave relatively accurate predictions. A practical estimate of GCL hydraulic conductivity was also derived. The predictions were compared with experimental results and good qualitative agreement was found. From the experimental results, the proposed prediction model has a maximum deviation of ~1:10–10:1, and the empirical model has a mean deviation of ~1:15–15:1.



中文翻译:

土工合成粘土衬垫的水力传导率评估

土工合成粘土衬垫 (GCL) 的导水率尚未完全了解,并且仍然存在某些知识空白,例如耦合机械和化学过程的影响。目前的研究旨在开发一个简化的数学模型来预测 GCL 的水力传导率,特别是关于机械和化学过程的耦合效应。该方法基于达西定律和泊肃叶定律,结合了扩散双层(DDL)理论和分形理论。围压、渗透液浓度等外在因素,以及可交换阳离子、离子半径、蒙脱石表面分形维数、在可交换阳离子i处溶胀后两蒙脱石层之间的距离(m)等固有性质(i表示主要可交换阳离子,如膨润土中的Na +、Ca 2+、K +和Mg 2+),考虑了两个蒙脱石层之间的层间水的密度和粘度系数。所提出的理论模型给出了相对准确的预测。还得出了 GCL 水力传导率的实际估计值。将预测结果与实验结果进行了比较,发现了良好的定性一致性。从实验结果来看,所提出的预测模型的最大偏差为~1:10–10:1,经验模型的平均偏差为~1:15–15:1。

更新日期:2022-02-18
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