Prediction of soil water characteristic curve using artificial neural network: a new approach

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.