Simple Graphical Prediction of Relative Permeability of Unsaturated Soils under Deformations

At present, there are only a few existing models that can be used to predict the relative permeability of unsaturated soil under deformations, and the calculation process is relatively complex. In order to fit the measured value of the relative permeability coefficient of unsaturated soil before def...

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Bibliographic Details
Main Authors: Gaoliang Tao, Qing Wang, Qingsheng Chen, Sanjay Nimbalkar, Yinjie Peng, Heming Dong
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:Fractal and Fractional
Subjects:
Online Access:https://www.mdpi.com/2504-3110/5/4/153
Description
Summary:At present, there are only a few existing models that can be used to predict the relative permeability of unsaturated soil under deformations, and the calculation process is relatively complex. In order to fit the measured value of the relative permeability coefficient of unsaturated soil before deformation, this work employs the simplified unified model of the relative permeability coefficient of unsaturated soil, and it obtains the index <i>λ</i> before deformation of the soil. In addition, the value of index <i>λ</i> remains unchanged before and after deformation. Based on the actual measured value of the soil–water characteristic curve before deformation, the air-entry value prediction model is used to predict the air-entry value of soil with different initial void ratios. The relative permeability coefficient of unsaturated soil is then conveniently predicted using the graphical method in combination with the simplified unified model. The method is validated by using the test data of silt loam, sandy loam, and unconsoildated sand. The results show that the predicted results are consistent with the measured values. The prediction method in this paper is simple and overcomes the limitations associated with the determination of the index <i>λ</i>. It expands the application range of the unsaturated relative permeability coefficient model while improving the accuracy of predictions.
ISSN:2504-3110