Geostatistical Analysis of the Permeability Coefficient in Different Soil Textures (original) (raw)
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2021
Knowledge about soil permeability is important in various scientific fields: hydrology and hydrogeology, geotechnics, environmental geotechnics, and others. Depending on the different goals that need to be achieved by a particular engineering project, the conditions in which the permeability coefficient is determined in terms of applied hydraulic gradients, applied stresses, type of test fluid, etc. are adjusted, as well as the required precision of its determination. In addition, the permeability coefficient is a soil property with the largest range of possible values. It can be determined through various laboratory and field methods, and by applying established empirical correlations using data on the grain-size distribution and empirical coefficients that depend on some factors, such as hydraulic radius (specific surface), curvature, porosity, etc. This paper presents the results of laboratory testing of the permeability coefficient by the constant head test and the use of a permeameter. The results were compared with the permeability coefficient obtained by applying a number of empirical correlations. Artificial samples were prepared in the laboratory by mixing different previously prepared soil fractions in order to determine the influence of particle size and soil gradation on the estimated soil permeability coefficient.
Estimation of permeability function from the soil–water characteristic curve
Because direct measurement is time-consuming and costly, the permeability function of unsaturated soil is commonly determined by estimation using the soil–water characteristic curve (SWCC). Various prediction models (i.e., indirect methods) for the permeability function have been proposed by different researchers. Mualem (1986) categorized these prediction models into three groups: empirical, macroscopic, and statistical models. Of these, the statistical model is the most rigorous and provides the most accurate results (Leong and Rahardjo 1997). In this paper, a new equation for the calculation of the permeability function is proposed in which the per-meability function of unsaturated soil is correlated with the fitting parameters of SWCC. In other words, unsatu-rated hydraulic conductivity is computed using an electronic spreadsheet with input parameters (i.e., the fitting parameters of SWCC). The proposed equation is shown to be the general form of the equations by Marshall (1958) and Kunze et al. (1968). In the equation, suction is considered as a variable and SWCC, in the form of degree of saturation, S, is adopted as a probability function. Soil volume change is also incorporated in this equation. Lastly, the proposed equation is verified using experimental data from the literature.
Transportation Geotechnics, 2023
This paper reviews commonly used parameters and prediction models for assessing the permeability of granular soils. Following a review of published models for prediction of soil permeability, a dimensional homogenous transformation model for a-priori estimation of soil permeability was calibrated using a large database (CG/ KSAT/7/1278) comprising permeability data for a wide range of granular soils sourced from over 50 publications. The new transformation model requires knowledge of the void ratio and gradation of the material to make estimates of the soil permeability. The prediction accuracy of the calibrated model was then assessed alongside that of other empirical and semi-empirical models also calibrated using CG/KSAT/7/1278. The potential influences of void ratio, key gradation parameters and permeability test type on the prediction accuracy of the proposed model are also examined. The paper shows that while the fitted constants in the proposed transformation model are affected to varying extents by the aforementioned parameters, it does offer reasonable predictions of permeability with only knowledge of the void ratio and material gradation required.
Estimation of soil permeability
Alexandria Engineering Journal, 2016
Soils are permeable materials because of the existence of interconnected voids that allow the flow of fluids when a difference in energy head exists. A good knowledge of soil permeability is needed for estimating the quantity of seepage under dams and dewatering to facilitate underground construction. Soil permeability, also termed hydraulic conductivity, is measured using several methods that include constant and falling head laboratory tests on intact or reconstituted specimens. Alternatively, permeability may be measured in the field using insitu borehole permeability testing (e.g. [2]), and field pumping tests. A less attractive method is to empirically deduce the coefficient of permeability from the results of simple laboratory tests such as the grain size distribution. Otherwise, soil permeability has been assessed from the cone/piezocone penetration tests (e.g. [13,14]). In this paper, the coefficient of permeability was measured using field falling head at different depths. Furthermore, the field coefficient of permeability was measured using pumping tests at the same site. The measured permeability values are compared to the values empirically deduced from the cone penetration test for the same location. Likewise, the coefficients of permeability are empirically obtained using correlations based on the index soil properties of the tested sand for comparison with the measured values.
2015
Soil texture is one of the most important soil properties governing most of the physical, chemical and hydrological properties of soils. Variability in soil texture may contribute to the variation in nutrient storage and availability, water retention and transport and binding and stability of soil aggregates. It can directly or indirectly influence many other soil functions and soil threats such as soil erosion. Geostatistics has been extensively used for quantifying the spatial pattern of soil properties and Kriging techniques are proving sufficiently robust for estimating values at unsampled locations in most of the cases. For this purpose, 50 soil samples were provided from fields of Farahan plain during May 2014. Soil texture was measured for each sample. The Kriging method with Circular, Spherical, Tetra spherical, Pent spherical, Exponential, Gaussian, Rational Quadratic, Hole Effect, k-Bassel, J-Bassel and Stable semivariograms for Prediction the Spatial Variability of Soil T...
Spatial variability of near-saturated soil hydraulic properties in Moghan plain, North-Western Iran
Arabian Journal of Geosciences, 2018
The spatial variability of the hydraulic properties of near-saturated soil was investigated in Moghan plain, northwestern Iran. To include all types of cultivated crops and examine the effects of the distance, through nested grid design, 212 sites were selected, with a distance interval of 200, 400, and 800 m. Soil samples were collected from 0-to 20-cm depth for determination of selected soil chemical and physical properties in the laboratory. A tension infiltrometer was employed to consecutively measure the unsaturated infiltration at matric suctions (h m) of 2, 5, 10, and 15 cm. The infiltration data was modeled using Wooding's analytical method, and best-fit values of Gardner's parameters of saturated hydraulic conductivity (K s) and macroscopic capillary length (λ c) were derived. The data was also modeled using numerical method in DISC software, and the van Genuchten parameters (θ s , α, n, and K s(DISC)) were optimized. The results of the study revealed that K s(DISC) had the highest coefficient of variation (CV), i.e., 212%, among the hydraulic parameters of the soil; shape parameter n, conversely, had the lowest CV. Once the means of the hydraulic parameters were compared, no significant differences were found in the hydraulic parameters among the cultivated crop types. To map the spatial variability of soil parameters by means of ordinary kriging, a spherical model, chosen based on mean error (ME) values and root-mean-square error (RMSE), was used. Based on the semi-variogram parameters, i.e., range, slope, and nugget to sill ratio, the spatial distribution of soil properties was not consistent in the studied area. The lowest and largest ranges of spatial dependency were 1021 and 4500 m for unsaturated hydraulic conductivities at matric suctions of 15 and 2 cm (K 15 and K 2), respectively. The spatial dependencies of most variables under investigation were moderate to strong. Overall, the findings of this study put forward the view that the variability of soil hydraulic parameters might be controlled conjointly by variability in intrinsic soil properties, namely, particle size distribution, and bulk density, and several management practices in the plain have paramount importance. Policy makers and farm managers can effectively make use of the maps made in this study to manage their in site-specific irrigation practices.
Soil Permeability Comparison By Laboratory Tests: Baghdad City
Intermediate part of Iraq and symbolizes the first governorate from the eighteen governorates of Iraq in area called of Baghdad City. Permeability in Baghdad city is the meaning property that effects on the construction's constancy, then, the research is focused to evaluate the permeability for (cohesion less soil). Because of the soil morphology of Baghdad city, numerous disturbed samples at different depths were taken indicative of locations covering the region in Baghdad governorate. Four sites of soils in Baghdad city are certain. These sites are categorized consistent with the values of effective diameter to (A, B, C and D). The coefficient of permeability (k) is valued by using the constant head permeability test when the soil samples are arranged in dry national, then distribution the soil within the permeability shape at changed density by using raining soil (at different void ratio), these tests are recurrent at different coefficient of uniformity (CU). The mathematical representation of the coefficient of permeability data are represented by empirical equation. The relapse analysis was performed by using the statistical package and the results of the analysis provide the empirical equation for Baghdad soil. The empirical equation (12) compare with the Poiseuille's equation (11), the results of the empirical equation are conventional as compared to Poiseuille's equation. The results got from the present empirical equation (12) are compared with the field results of the four arbitrary sites which indication a good matching.
Permeability Coefficient of Low Permeable Soils as a Single-Variable Function of Soil Parameter
Water, 2019
Based on the results of experimental studies concerning the filtration coefficient, the Darcianity of the observed flows for eight cohesive soils at four hydraulic gradients was analyzed. It is observed that linear dependence of flow velocity on hydraulic gradient is an approximation only, and it is the worse the more cohesive a given soil is. Despite this, Darcy’s law can be a correct approximation of the empirical relationship between hydraulic gradient and the flow velocity, also in very cohesive soils. A statistical analysis was carried out to identify correlation between soil properties and permeability coefficient. For each soil, 109 parameters were analyzed, among others applying mercury intrusion porosimetry, scanning electron microscopy, dynamic image analysis, and laser diffraction. Ultimately, three single-variable models best fitted to the experimental data were found, using the plasticity index IP as the independent variable, the average pore diameter DP, and the convex...
Spatial Variability of Desert Soil in Najaf Governorate, Iraq Using Geostatistics
Plant Archives, 2021
The study area was chosen within the administrative boundaries of Najaf governorate , in the western part (the western plateau), and between two longitude 44°2'32.99''-44° 13'31.41" East and two showrooms 31° 59'15.94"-31° 53'1.65" North, for the purposes of studying variations of soil, As the area of the study area 19776 ha, After Identifying 57 locations and two depths 0-25 cm and 25-50 cm by (Al Augar), Its coordinates are determined by means of a device GPS. Spatial variability were studied horizontally for morphological and physical and chemical characteristics of the study area soils and applied to the methods of advanced statistics, which included of geostatistics. The results of the Geostatistics indicated that the most variability morphological Properties are texture, followed by the property of structure, then consistency, and finally the Properties of color. Morphological Properties were less variations than the rest of the Properties. The results of Kriging indicated that the range for the spatial variability of morphological Properties ranged between 3011-4257 meters as it was the lowest value for texture and the highest value for the color Property of surface horizons. As for the subsurface horizons the range between 3065-4306 meters the lowest value for texture and the highest value for the color Property. As for the range of the spatial variability of the physical Properties ranged between 813-4083meters, The lowest value was for the ratio of gravel and the highest value for the bulk density of surface horizons. The subsurface horizons were with a range of 896-4111 meters. The range of the spatial variability of the chemical properties ranged between 850-4560 meters the lowest value for EC and the highest value for PH for surface horizons as for subsurface horizons it ranged between 854-5138 meters the lowest value for ESP and the highest value for pH. As for the values of the coefficient of variation for the properties of the soil ranged from 2.6-124.70% and 1.20-143.10% for the surface and subsurface horizons respectively, And it was the lowest value of pH and the highest value of the percentage of gravel, It is noted that the smaller the coefficient of variation the greater the values of the range. The most appropriate statistical models when using Geostatistics were the model Spherical followed by Circular in the rate of 56.25and 43.75 % respectively they were most appropriate to most of the soil Properties, An Exponential model in the rate of 3.13% of describing variability of silt separation applied to subsurface horizons as it gave a good representation of the Semivariogram. The number of samples in the Geostatistics of morphological, physical and chemical properties ranged from 3-19 samples, while in the random case 1-387 samples.