Abstract
In unsaturated soil mechanics, many attempts have been made to estimate the SWCC based on soil texture and grain-size distribution. This paper proposes a simplified method to estimate the soil-water characteristic curve (SWCC) for both coarse and fine-grained soils using SWCC data and machine learning computer code in the Aburra Valley. Fredlund and Xing parameters has been used to estimate the SWCC correlations. Soil samples collected from field survey were subjected to laboratory testing, SWCCs were estimated using filter paper method. Each SWCC data set from Aburra Valley was fitted with Fredlund and Xing curve using multiple regression analysis, correlations were derived for those four parameters based on predictors derived from machine learning. The proposed method gives a good estimation and low residual errors of the SWCC.
Highlights
The Soil-water characteristic curve (SWCC) has been developed as a key tool in agricultural water management, engineering, hydrology, and soil science research
The soil-water characteristic curve (SWCC) has been widely used for the estimation of unsaturated soil properties (i. e shear strength, coefficient of unsaturated permeability, and volumetric water content and its relative volume change (Fredlund and Rahardjo 1993 [2])
The IP index, percentage pass sieve #200 (P200) and LL parameters show a clear grouping of the ratings, with noise characteristic of any measurement process
Summary
The Soil-water characteristic curve (SWCC) has been developed as a key tool in agricultural water management, engineering, hydrology, and soil science research. The SWCC has been widely used for the estimation of unsaturated soil properties SWCC is generally obtained by laboratory tests. Measuring these characteristics is timeconsuming, labour intensive and expensive. Attempts have been developed to get the SWCC based on soil index properties, such as void ratio, initial gravimetric water content, clay content, silt content, organic matter, density, and salinity. Those approaches based on index properties are highly desirable due to its simplicity and low cost
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