Prediction Of Soil-water Characteristic Curve Research Articles

The prediction of the soil freezing characteristic curve using the soil water characteristic curve

Soil-water characteristic curve (SWCC) and soil freezing characteristic curve (SFCC) describe the water-holding characteristics of the soil during drying (or wetting) and freezing (or thawing), respectively. However, the relationship between SWCC and SFCC remains unclear. In order to clarify their relationship, a theoretical model is established based on the Clapeyron equation, which is further examined by laboratory test results for several soils. The results demonstrate that the newly proposed theoretical model has wide adaptability to various soils. With this model, prediction of SWCC from SFCC can be easily achieved, and the problem of time-consuming can also be addressed. In addition, the prediction of SFCC from SWCC can also be achieved, overcoming the difficulty in accurately measuring SFCC for coarse soil (i.e., sand or gravel) due to the lack of high-precision temperature control methods.

Prediction of soil water characteristic curves based on low suction section and inflection point

Abstract Given the cumbersome determination method of the Soil Water Characteristic Curve (SWCC), the collapsible loess (silty clay loam) in Lanzhou was taken as the research object to explore a symmetrical prediction method for SWCC in a low suction section on the inflection point, and to determine the optimal suction section and the inflection point. The results showed that in the range of 0–7,000 cm suction, the spatial variation coefficient (CV) of soil saturation of each bulk density increased with the increase of suction. Soil saturation showed weak spatial variability when suction <800 cm, and moderate spatial variability when suction ≥800 cm. Using a bulk density of 1.58 g/cm3 as an example, the SWCC determined by the symmetry of the bending point was compared with the measured data of 0–300, 0–500, 0–800 and 0–1,000 cm suction sections. It was found that the measured soil saturation of SWCC determined by the data for the 0–800 cm suction section was the most consistent with the predicted value. The measured and predicted saturation points of the SWCC were most consistent with suction segments of 0–800 cm. SWCC data of different textures and bulk density were used to verify the prediction method at low suction section and an inflection point of 0–800 cm. It was found that the average absolute error and root mean square error of statistical indicators were close to 0, and the correlation coefficient was greater than 0.9915. The actual and predicted values of each soil parameter were linearly correlated. This method of predicting SWCCs with low suction and inflection points ensures both a high degree of curve fitting and the accuracy of characteristic soil parameters, providing a simple method for the prediction of SWCCs and guidance for managing soil water in loessial areas.

Characterizing physical and hydraulic properties of soils in Al-Ahsa, Kingdom of Saudi Arabia

Al-Ahsa Oasis is one of the oldest and biggest agricultural regions in Saudi Arabia. Thirty-six soil samples representing most of the soil type in the region were collected and analysed in a laboratory for physical properties including particle size (sand%, silt%, clay%), saturation θs, and bulk density ρ. The soil–water characteristic curve (SWCC) was measured using the filter paper method. Intensive statistical analysis included correlation, stepwise multiple linear regression analysis (SWR), mean square error (MSE), and F-test were used to evaluate the potential PTFs. Silt (silt%) and bulk density (ρ) were achieved a high accuracy in prediction of (ρ) and saturation (θs) respectively. Both field capacity (FC) and wilting point (WP) were correlated significantly with θs with a very high prediction compatibility and MSE 0.004 and 0.001 respectively. Using tow levels of prediction demonstrated high correctness in predicting SWCC with correlation coefficient 0.986 and 0.952 with a low MSE equal to 0.0007 and 0.0028 respectively. The result of this study shown the high feasibility of developing a model for the prediction of SWCC using easily readable PTFs.

Effect of the particle-size distribution variability on the SWCC predictions of coarse-grained materials

The particle-size distribution (PSD) is the key information required by several models for prediction of the soil-water characteristic curve (SWCC). The performance of these models has been extensively investigated in the literature; however, limited studies have been undertaken with respect to the uncertainty associated with the SWCC predictions resulting from the variability in the PSD. This study aims to investigate the influence of the variability of the PSD in the prediction of SWCCs using five different models applied to three different glass beads (GBs). The PSD curves were determined by sieve analysis, laser diffraction, and image analysis. The various testing procedures were statistically evaluated to understand the influence of variability of the PSD in terms of the coefficient of uniformity (CU) and de size of particles corresponding to 10% in the PSD (D10). For each prediction model, a combination of PSD curves and their coefficient of variation were used to estimate the SWCCs. Both theCUandD10proved to have a strong relationship with the predicted SWCCs. TheCUappears to influence more the residual suction prediction while theD10seems to have a major role for the transition and residual stages.

Bayesian Approach for Predicting Soil-Water Characteristic Curve from Particle-Size Distribution Data

Soil-water characteristic curve (SWCC) is a significant prerequisite for slope stability analysis involving unsaturated soils. However, it is difficult to measure an entire SWCC over a wide suction range using in-situ or laboratory tests. As an alternative, the Arya and Paris (AP) model provides a feasible way to predict SWCC from the routinely available particle-size distribution (PSD) data by introducing a scaling parameter. The accuracy of AP model is generally dependent on the calibrated database which contains test data collected from other sites. How to use the available test data to determine the scaling parameter and to predict the SWCC remains an unresolved problem. This paper develops a Bayesian approach to predict SWCC from PSD. The proposed approach not only determines the scaling parameter, but also identifies fitting parameters of the parametric SWCC model. Finally, the proposed approach is illustrated using real data in Unsaturated Soil Database (UNSODA). Results show that the proposed approach provides a proper prediction of SWCC by making use of the available test data. Additionally, the proposed approach is capable of predicting SWCC in the high suction range, allowing engineers to obtain a complete SWCC in practice with reasonable accuracy.

Experimental study on soil-water characteristic curve for silty clay with desiccation cracks

Cracks are ubiquitous in soils and can provide preferential pathways for rainfall infiltration and contaminant transport. The soil-water characteristic curve (SWCC) for a cracked soil is required when conducting seepage analysis. However, measuring SWCCs for cracked soils is difficult due to two reasons: (1) The presence of cracks leads to a significant change in the water content at extremely low suctions that cannot be controlled accurately; (2) The full development of cracks generally requires a large apparatus to accommodate the representative elementary volume of a crack network. This study aims to develop an experimental method to measure the SWCC for a silty clay with desiccation cracks. A large apparatus, 350mm in diameter and 280mm in height, was developed, which is capable of controlling extremely low suctions (<0.01kPa) at an accuracy of 0.005kPa and accommodating a clay sample with fully developed crack networks. The crack development in the soil sample was observed, providing necessary parameters for the prediction of SWCCs based on a theoretical model for cracked soils. The drying and wetting SWCCs for a silty clay with desiccation cracks were measured in the suction range from 0.01kPa to 300kPa, and compared with the SWCCs predicted using the theoretical model.

Predicting bimodal soil–water characteristic curves and permeability functions using physically based parameters

Experimental evidence shows that a gap-graded soil or a widely-graded granular material may have a bimodal soil–water characteristic curve (SWCC) and a bimodal permeability function. A bimodal SWCC or a bimodal permeability function originates from a dual-porosity structure. To date, the prediction of bimodal SWCCs for gap-graded soils is still a difficult task. In this paper, a bimodal SWCC model is proposed to describe the drying process of granular soils considering a dual-porosity structure. The new SWCC model shows powerful capability in fitting the SWCCs for soils varying from gravel to silt. Regression analysis is conducted to establish empirical relations between the model parameters and the indexes of soil grain-size distribution (GSD). Based on these relations, the new model predicts well both the bimodal SWCCs for gap-graded soils and the unimodal SWCCs for well-graded soils and uniform soils. A bimodal permeability function is also proposed and linked to the new SWCC model. In the absence of experimental SWCCs and permeability functions, the new model can be used to obtain preliminary SWCCs and permeability functions for granular soils. It should be mentioned that the prediction of the SWCC from the GSD is still empirical and does not address the cyclic wetting/drying process. Measurement of the SWCC should be performed wherever an accurate SWCC is required.

Prediction of SWCC using artificial intelligent systems: A comparative study

The significance of the Soil Water Characteristic Curve (SWCC) or soil retention curve in understanding the unsaturated soils behavior such as shear strength, volume change and permeability has resulted in many attempts for its prediction. In this regard, the authors had previously developed two models, namely. Genetic-Based Neural Network (GBNN) and Genetic Programming (GP). These two models have identical set of input parameters. These parameters include void ratio, initial water content, clay fraction, silt content and logarithm of suction normalized with respect to air pressure. In this paper, performance of these two models is further investigated using additional test data. For this purpose, soil samples from 14 different locations in Shiraz city in the Fars province of Iran are tested and their SWCCs are established, using a pressure plate apparatus. Next, the results are used to demonstrate the suitability of the previously proposed models and to evaluate relative importance of the input parameters. Assessment of the results indicates that predictions from GBNN model have relatively higher accuracy as compared to GP model.