Prediction of Soil-Water Characteristic Curves of Four Subgrade Materials using a Modified Perera Model

One of the main hydraulic properties of unsaturated soils is the Soil-Water Characteristic Curve (SWCC). It is essential to understand, predict soil water storage and determine the hydraulic and mechanical behaviour of soils. These curves can be obtained by direct and indirect measurements. The measurements to obtain these curves are expensive, delicate to perform and can be really slow for fine soils, so predictive models become necessary. In order to make a numerical model, a couple of identification tests were carried out to obtain the physical properties of each sample among the four subgrade materials collected in the regions of Dakar and Thies (Senegal). The measurement tests of the matric suction were then conducted depending on the nature of the material (fine-grained soil or coarse-grained soil) and allowed to draw the SWCC of each soil. Among numerous predictive models developed for SWCC in the last decades; this study used the Perera model to fit the SWCC of four (۰۴) subgrade materials, which did not give a satisfactory coefficient of correlation (R۲ = ۵۸% and a relatively low sum of the squared residuals (SSR)). This leads to modifying the Perera model to better fit the SWCC on the basis of an understanding of the effect of each parameter on the shape of the SWCC. The proposed modified model was validated by checking the adjusted R۲, minimizing the SSR in order to approach at most the experimental air entry value. The modified model works pretty well on coarse-grained and fine-grained soils. This modified model of Perera provided a very good correlation R۲equal to ۹۹.۹۸, ۹۸.۷۴, ۹۹.۶۴, and ۹۹.۷۳ for the sandy soils (Sebikotane and Keur Mory) and the Marley and Clayey soils of Diamniadio, with a minimal SSR obtained compared to Perera’s and Hernandez model. Doi: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۳-۰۹-۰۶-۰۳ Full Text: PDF

Keywords:

Soil-Water Characteristic Curve (SWCC) , Suction , Degree of Saturation , Predictive Model , Grain Size Distribution.