Abstract
Soil-Water Characteristic Curve (SWCC) is an important relationship between matric suction and volumetric water content of soils especially when dealing with unsaturated soil problems, these problems may include seepage, bearing capacity, volume change, etc. where the matric or total suction may have a considerable effect on unsaturated soil properties. Obtaining an accurate SWCC for a soil could be cumbersome and sometimes it is time consuming and needs effort for some soils, either through laboratory tests or through field tests. Accurate prediction of this curve can give more precise expectations in design or analysis that include some unsaturated soil properties, which can save more effort and time. This work will concentrate on proposing a new approach for determining the SWCC using Artificial Neural Network (ANN) depending on some soil properties (air-entry point and residual degree of saturation) through computer software MatLab as a tool for ANN. The new approach is to plot the SWCC curve points instead of obtaining the parameters used in Brooks and Corey (BC) Model (1964), van Genuchten (VG) Model (1980), or Fredlund and Xing (FX) Model (1994). Results showed close agreement in determination of the SWCC by verification of the ANN results with an additional curve sample.
Highlights
The Soil–Water Characteristic Curve (SWCC) provides a conceptual relation between the mass of water in a soil and the energy state described as matric suction of the water phase
The SWCCs have an important role in the determination of unsaturated soil property functions
Many researchers had been working on using Artificial Neural Network (ANN), Genetic Programming (GP), and Genetic Based Neural Network (GBNN) in predicting the SWCC
Summary
The SWCC provides a conceptual relation between the mass (and / or volume) of water in a soil and the energy state described as (usually) matric suction of the water phase. The SWCC has proven to be an interpretive model that utilizes the capillary model to provide an understanding of the distribution of water in the voids. The effects of soil texture, void ratio, and gradation became part of the interpretation of measured laboratory SWCC data (i.e. these soil properties are used as parameters in prediction of SWCC). The SWCCs have an important role in the determination of unsaturated soil property functions (e.g. shear strength, volume change, etc.). Neural Networks are widely used in experimental researches that need some soil parameters determination either in field or laboratory, and to describe some complex soil behavior
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