Determinations of unsaturated soil parameters using experimental procedures are time consuming and difficult. In recent years, the soil–water characteristic curve (SWCC) has become an important tool in the interpretation of the engineering behavior of unsaturated soils. Difficulties associated with determining such parameters have justified the use of indirect determination. This paper presents the general nature of the SWCC for soils with different plasticity limits, index and gradation, in terms of gravimetric water content and degree of saturation versus soil matric suction from Anbar governorate. In order to investigate possible relationships between the plasticity limits, index, percent passing no.200 and SWCC, 7 type of soils were tested to find its SWCC experimentally and compared the result with the curves obtained from different model presented in the literature. The objectives of the paper were to check the validity of these models with the experimental results. The results shows a good agreement and to present a simple method for inferring the SWCC for soils, taking into account the liquid limit, plastic limit, plasticity index and percent of fines passing sieve no.200.
The basic correlation that determines the mechanical and hydraulic characteristics of unsaturated soils is the Soil Water Characteristic Curve (SWCC). Critical synthesis The present review brings forth the latest developments in SWCC modeling, measurement and application, with a more specific interest in determining the factors that question the financial forecasting properties of current simple models, especially with dynamic environmental circumstances. In our analysis, we have found that current empirical models are frequently ineffective because they lack the explicit ability to integrate the complex/coupled nature of microstructural properties (e.g., fractal geometry and nano-porosity) and compositional differences (e.g., organic matter). More so, the synthesis shows that there is a fundamental conflict in modeling, whereas the more intricate a conventional empirical model gets, the less accurate it becomes, whereas sophisticated data-driven methods such as Deep Learning are much more effective at making predictions. As a consequence, this review confirms that one of the main future research needs should be the creation of coherent constitutive models that will combine mechanistic controls (fractal theory) and advanced AI prediction. Most importantly, the implications in practice reveal that the neglect of such coupled considerations directly compromises the validity of long-term geotechnical performance forecasts, that is, in relation to slope stability and foundation resilience to moisture changes caused by climate change. The review gives the conclusion by recommending that laboratory results should be immediately validated at a field scale to bring together the gap between theory and engineering design.