TY - JOUR

T1 - Accuracy Assessment of Predictive SWCC Models for Estimating the van Genuchten Model Parameters

AU - Ellithy, Ghada S.

AU - Ellithy, Ghada S.

AU - Vahedifard, Farshid

AU - Vahedifard, Farshid

AU - Rivera-Hernandez, Xavier A.

AU - Rivera-Hernandez, Xavier A.

N1 - Proper determination of the soil-water characteristic curve (SWCC) plays an important role in the accuracy of any modeling attempt involving variably saturated soils such as transient unsaturated seepage analysis. While the SWCC can be directly measured, several predictive models have been developed over the past two decades and are employed in practice because of their simplicity, and the lower cost, and time needed to obtain their input parameters.

PY - 2018/6/20

Y1 - 2018/6/20

N2 - Proper determination of the soil-water characteristic curve (SWCC) plays an important role in the accuracy of any modeling attempt involving variably saturated soils such as transient unsaturated seepage analysis. While the SWCC can be directly measured, several predictive models have been developed over the past two decades and are employed in practice because of their simplicity, and the lower cost, and time needed to obtain their input parameters. The predictive models are commonly developed through multiple regression analysis over a large number of measured SWCCs to establish an empirical correlation between the SWCC model parameters and soil index properties such as grain size distribution and Atterberg limits. This study evaluates the performance of seven predictive models to estimate the van Genuchten SWCC model parameters a , n , and θ r that represent the air entry value (AEV), slope of the curve, and the residual water content, respectively. For this purpose, the transient release and imbibition method (TRIM) device is used in the laboratory to obtain the van Genuchten SWCC of silty sand samples collected from a setback levee. The van Genuchten model parameters measured in the laboratory are compared against those estimated using the predictive models. The comparison shows that using predictive models can lead to over two orders of magnitude difference in a , a ratio of θ r to θ s between 0.06 and 0.21, and an n value between 1.25 and 2.85 for the tested soil. The aforementioned differences can lead to significant variations in transient seepage analysis results, a factor which needs to be carefully taken into consideration when using predictive models in practice.

AB - Proper determination of the soil-water characteristic curve (SWCC) plays an important role in the accuracy of any modeling attempt involving variably saturated soils such as transient unsaturated seepage analysis. While the SWCC can be directly measured, several predictive models have been developed over the past two decades and are employed in practice because of their simplicity, and the lower cost, and time needed to obtain their input parameters. The predictive models are commonly developed through multiple regression analysis over a large number of measured SWCCs to establish an empirical correlation between the SWCC model parameters and soil index properties such as grain size distribution and Atterberg limits. This study evaluates the performance of seven predictive models to estimate the van Genuchten SWCC model parameters a , n , and θ r that represent the air entry value (AEV), slope of the curve, and the residual water content, respectively. For this purpose, the transient release and imbibition method (TRIM) device is used in the laboratory to obtain the van Genuchten SWCC of silty sand samples collected from a setback levee. The van Genuchten model parameters measured in the laboratory are compared against those estimated using the predictive models. The comparison shows that using predictive models can lead to over two orders of magnitude difference in a , a ratio of θ r to θ s between 0.06 and 0.21, and an n value between 1.25 and 2.85 for the tested soil. The aforementioned differences can lead to significant variations in transient seepage analysis results, a factor which needs to be carefully taken into consideration when using predictive models in practice.

UR - https://doi.org/10.1061/9780784481684.001

U2 - 10.1061/9780784481684.001

DO - 10.1061/9780784481684.001

M3 - Article

JO - PanAm Unsaturated Soils 2017

JF - PanAm Unsaturated Soils 2017

ER -