Prediction Of Hydraulic Conductivity Research Articles

An Appraisal on the Interpolation Methods Used for Predicting Spatial Variability of Field Hydraulic Conductivity

Knowledge of spatial variability of soil hydraulic conductivity is a key parameter for hydrological modeling. Spatial variability of soil hydraulic conductivity, Ks is attributed to soil heterogeneities associated with texture/structure, different initial conditions, meteorological changes, and clogging. The spatial variability is quantified by employing interpolation methods to point measurements performed in the field. There are not many studies reported in the literature that deals with the critical evaluation of the relative performance of different interpolation methods for predicting spatial variability of hydraulic conductivity. The primary objective of this study is to perform a critical evaluation of five interpolation methods, namely Kriging, Inverse Distance Weighted, Natural Neighbor, Spline and Trend for the spatial prediction of hydraulic conductivity. The accuracy of different methods was assessed by comparing the predicted values with the measured hydraulic conductivity of selected locations. It was noted that the Kriging method with exponential model gave a better spatial prediction as compared to other methods. The spatial variability of Ks was found to be in the same pattern as that of the percentage variation of the sand fraction for both the sites investigated in this study. It was further noted that the prediction of Ks was found to be more precise for those stations with a higher percentage of sand. A sudden transition of soil type from sand to silt was found to influence the accuracy of spatial prediction.

A coupled CFD-DEM simulation of upward seepage flow in coarse sands

The suction anchor becomes more popular for offshore oil and gas industry in deeper water. For suction anchor–soil interaction, the prediction of hydraulic conductivity of porous materials is a long-standing problem in offshore engineering. To investigate the hydraulic characteristics, an upward seepage flow through saturated sands is considered in this study. A numerical approach, which is able to describe the fluid–particle interaction at particle scale, has been employed to analyse fluid flow in sands. This approach is constructed by adopting a coupled discrete element method and computational fluid dynamic approach (CFD-DEM numerical model). The coupled CFD-DEM approach is first benchmarked by a classic geomechanics problem where analytical solutions are available, and then employed to investigate the characteristics of upward seepage flow in coarse sand columns. Through numerical modelling, the predicted relation between hydraulic gradient and flow velocity is obtained and it is compared with the classical analytical correlation. The effect of several bulk and micromechanical parameters including packing porosity, particle size combination and inter-particle rolling resistance on the flow characteristics is numerically examined. The results show that the particle polydispersity and packing porosity have significant effect on the hydraulic conductivity in the seepage flow. The introduction of inter-particle rolling resistance can change initial packing structure of particle assembly in some extent rather than the hydraulic conductivity from the particle shape effect perspective. A further development of numerical model, in which the effect of non-spherical particles on the seepage flow, will be carried out later.

Method for Quick Prediction of Hydraulic Conductivity and Soil-Water Retention of Unsaturated Soils

Hydraulic conductivity and soil-water retention are two critical soil properties describing the fluid flow in unsaturated soils. Existing experimental procedures tend to be time consuming and labor intensive. This paper describes a heuristic approach that combines a limited number of experimental measurements with a computational model with random finite element to significantly accelerate the process. A microstructure-based model is established to describe unsaturated soils with distribution of phases based on their respective volumetric contents. The model is converted into a finite element model, in which the intrinsic hydraulic properties of each phase (soil particle, water, and air) are applied based on the microscopic structures. The bulk hydraulic properties are then determined based on discharge rate using Darcy’s law. The intrinsic permeability of each phase of soil is first calibrated from soil measured under dry and saturated conditions, which is then used to predict the hydraulic conductivities at different extents of saturation. The results match the experimental data closely. Mualem’s equation is applied to fit the pore size parameter based on the hydraulic conductivity. From these, the soil-water characteristic curve is predicted from van Genuchten’s equation. The simulation results are compared with the experimental results from documented studies, and excellent agreements were observed. Overall, this study provides a new modeling-based approach to predict the hydraulic conductivity function and soil-water characteristic curve of unsaturated soils based on measurement at complete dry or completely saturated conditions. An efficient way to measure these critical unsaturated soil properties will be of benefit in introducing unsaturated soil mechanics into engineering practice.

Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network

Knowledge of hydraulic properties is necessary for hydrological studies, artificial recharge of the aquifer, watershed management and agriculture system. The major objective of this study was to develop a fuzzy logic and artificial neural network (ANN) based models for estimating the unsaturated hydraulic conductivity of soil (Ku). A mini disk infiltrometer, being handy used for determining infiltration characteristics. In this study mini Disk Infiltrometer (Decagon Devices, Inc.) at a suction head varying from 1 to 6 cm was used for determining Ku of sandy soil. All the measurements have been done on predetermined initial condition of different proportions of rice husk ash and fly ash mixed with sand. For modeling randomely selected 70% data was applied for training and residual 30% for the test. Comparison of results show that the prediction with ANN approach works well with correlation of coefficient value of 0.8662 (root mean square error 4.5607 cm/h).

The hydraulic conductivity of sediments: A pore size perspective

This article presents an analysis of previously published hydraulic conductivity data for a wide range of sediments. All soils exhibit a prevalent power trend between the hydraulic conductivity and void ratio. Data trends span 12 orders of magnitude in hydraulic conductivity and collapse onto a single narrow trend when the hydraulic conductivity data are plotted versus the mean pore size, estimated using void ratio and specific surface area measurements. The sensitivity of hydraulic conductivity to changes in the void ratio is higher than the theoretical value due to two concurrent phenomena: 1) percolating large pores are responsible for most of the flow, and 2) the larger pores close first during compaction. The prediction of hydraulic conductivity based on macroscale index parameters in this and similar previous studies has reached an asymptote in the range of kmeas/5≤kpredict≤5kmeas. The remaining uncertainty underscores the important role of underlying sediment characteristics such as pore size distribution, shape, and connectivity that are not measured with index properties. Furthermore, the anisotropy in hydraulic conductivity cannot be recovered from scalar parameters such as index properties. Overall, results highlight the robustness of the physics inspired data scrutiny based Hagen–Poiseuille and Kozeny-Carman analyses.

Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS)

This paper aims to predict the unsaturated hydraulic conductivity of soil using Adaptive Neuro- fuzzy inference system (ANFIS), Multi-Linear Regression (MLR), and artificial neural network (ANN). Laboratory experiments carried out on 46 samples of sand, rice husk ash and fly ash (FA) mixture. Out of 46 data-set for modeling of unsaturated hydraulic conductivity 32 random data used for training and remaining 14 to the test. The results suggest improved performance by Gaussian membership function than triangular and generalized bell-shaped membership-based ANFIS. MLR is better than ANN and Gaussian membership function-based ANFIS for unsaturated hydraulic conductivity.

Forensic Excavation of Rock Masses: A Technique to Investigate Discontinuity Persistence

True persistence of rock discontinuities (areas with insignificant tensile strength) is an important factor controlling the engineering behaviour of fractured rock masses, but is extremely difficult to quantify using current geological survey methodologies, even where there is good rock exposure. Trace length as measured in the field or using remote measurement devices is actually only broadly indicative of persistence for rock engineering practice and numerical modelling. Visible traces of discontinuities are treated as if they were open fractures within rock mass classifications, despite many such traces being non-persistent and actually retaining considerable strength. The common assumption of 100% persistence, based on trace length, is generally extremely conservative in terms of strength and stiffness, but not always so and may lead to a wrong prediction of failure mechanism or of excavatability. Assuming full persistence would give hopelessly incorrect predictions of hydraulic conductivity. A new technique termed forensic excavation of rock masses is introduced, as a procedure for directly investigating discontinuity persistence. This technique involves non-explosive excavation of rock masses by injecting an expansive chemical splitter along incipient discontinuities. On expansion, the splitter causes the incipient traces to open as true joints. Experiments are described in which near-planar rock discontinuities, through siltstone and sandstone, were opened up by injecting the splitter into holes drilled along the lines of visible traces of the discontinuities in the laboratory and in the field. Once exposed the surfaces were examined to investigate the pre-existing persistence characteristics of the incipient discontinuities. One conclusion from this study is that visible trace length of a discontinuity can be a poor indicator of true persistence (defined for a fracture area with negligible tensile strength). An observation from this series of experiments was that freshly failed surfaces through pre-existing rock bridges were relatively rough compared to sections of pre-existing weaker areas of geologically developed (though still incipient) discontinuities. Fractographic features such as hackle and rib marks were typical of the freshly broken rock bridges, whereas opened-up areas of incipient discontinuity were smoother. Schmidt hammer rebound values were generally higher for the rock bridge areas, probably reflecting their lower degree of chemical and physical weathering.

Determination of the saturated film conductivity to improve the EMFX model in describing the soil hydraulic properties over the entire moisture range

Difficulty in measuring hydraulic conductivity, particularly under dry conditions, calls for methods of predicting the conductivity from easily obtained soil properties. As a complement to the recently published EMFX model, a method based on two specific suction conditions is proposed to estimate saturated film conductivity from the soil water retention curve. This method reduces one fitting parameter in the previous EMFX model, making it possible to predict the hydraulic conductivity from the soil water retention curve over the complete moisture range. Model performance is evaluated with published data of soils in a broad texture range from sand to clay. The testing results indicate that 1) the modified EMFX model (namely the EMFX-K model), incorporating both capillary and adsorption forces, provides good agreement with the conductivity data over the entire moisture range; 2) a value of 0.5 for the tortuosity factor in the EMFX-K model as that in the Mualem’s model gives comparable estimation of the relative conductivity associated with the capillary force; and 3) a value of −1.0×10−20J for the Hamaker constant, rather than the commonly used value of −6.0×10−20J, appears to be more appropriate to represent solely the effect of the van der Waals forces and to predict the film conductivity. In comparison with the commonly used van Genuchten-Mualem model, the EMFX-K model significantly improves the prediction of hydraulic conductivity under dry conditions. The sensitivity analysis result suggests that the uncertainty in the film thickness estimation is important in explaining the model underestimation of hydraulic conductivity for the soils with fine texture, in addition to the uncertainties from the measurements and the model structure. High quality data that cover the complete moisture range for a variety of soil textures are required to further test the method.

Prediction of hydraulic conductivity for soil–bentonite mixture

Owing to its low hydraulic conductivity, soil and bentonite mixture is applied as a liner material. However, the experimental determination of hydraulic conductivity, which is controlled by various physical, chemical and mineralogical factors, requires an expensive and time-consuming setup. In the present work, multigene symbolic, genetic programming was used to model functional relationships for hydraulic conductivity. The developed model was able to generalize highly nonlinear variations in data as well as predict system behavior from experimental observations. It was found that the predictions obtained from developed model agree well with experimental observations.

Hydraulic conductivity prediction based on grain-size distribution using M5 model tree

ABSTRACTThe hydraulic conductivity, Ks, is one of the most important hydraulic properties which controls the water and solute movement into the soil. It is measured on soil specimens in the laboratory. On the other hand, sometimes it is obtained by tests carried out in the field by a number of researchers. Therefore, several experimental formulas have developed to predict it. Recently, soft computing tools have been used to evaluate the hydraulic conductivity. However, these tools are not as transparent as empirical formulas. In this study, another soft computing approach, i.e. model trees, have been used for predicting the hydraulic conductivity. The main advantage of model trees is that, unlike the other data learning tools, they are easier to use and represent understandable mathematical rules more clearly. In this paper, a new formula that includes some parameters is derived to estimate the hydraulic conductivity. To develop the new formulas, experimental data sets of hydraulic conductivity were used. A comparison is made between the estimated hydraulic conductivity by this new formula and formulas given by other’s researches.

Prediction of Unsaturated Hydraulic Conductivity of Agricultural Soils Using Artificial Neural Network and c#

Prediction of Unsaturated Hydraulic Conductivity of Agricultural Soils Using Artificial Neural Network and c#

Investigation on hydraulic properties of compacted GMZ bentonite used as buffer/backfill material

During the past decades, GMZ bentonite has been widely investigated for its use as buffer/backfill materials in China. Based on a comprehensive review of the former studies, achievements on experimental and theoretic works on the hydraulic aspects of compacted GMZ bentonite with consideration of temperature effects are presented in this paper. Water retention property of compacted GMZ bentonite depends on constraint conditions. Temperature effects on water-retention depend on constraint conditions and suction. The hysteresis behaviour is not obvious. Based on the test results, a revised water retention model was developed for considering the temperature effect. The saturated hydraulic conductivity of the densely compacted GMZ bentonite increases as dry density and temperature increases. A revised model, which considers temperature influence on water viscosity and the effective flow cross-sectional area of porous channels, for prediction of saturated hydraulic conductivity have been developed and verified. The unsaturated hydraulic conductivity of confined densely compacted GMZ bentonite samples decreases first and then increases with suction decrease from an initial value of 80 MPa to zero. With consideration of temperature effects and microstructure changes, a revised model for prediction of unsaturated hydraulic conductivity of compacted GMZ01 bentonite was proposed.

Prediction of unsaturated soil hydraulic conductivity using air permeability: Regression approach

A close correlation between water conductivity (<italic>K(θ)</italic>) and air permeability (<italic>K</italic><sub><italic>a</italic></sub>), measured at various water contents, is expected due to tight dependence of water filled porosity to air filled porosity of soils. Finding such a relation will greatly facilitate the prediction of unsaturated water conductivity (<italic>K(θ)</italic>). So, the purpose of the current investigation was to find out if a reliable relation or function between the two permeabilities can be established. In this regard, <italic>K(θ)</italic> and <italic>K</italic><sub><italic>a</italic></sub> were measured by pressure plate outflow and variable head methods, respectively, at the range of 0 to -100 kPa matric potential (<italic>ψ</italic><sub><italic>m</italic></sub>). A linear regression function between relative water conductivity (<italic>K</italic><sub><italic>r</italic></sub>(<italic>θ</italic>)) and <italic>K</italic><sub><italic>a</italic></sub> (<italic>LogK</italic><sub><italic>r</italic></sub> (<italic>θ</italic>)=<italic>a</italic>+<italic>bLogK</italic><sub><italic>a</italic></sub>) with the correlation coefficient (<italic>R</italic>) from 0.884 to 0.999 were established for the 22 examined soils. The overall <italic>R</italic> for 128 data pairs (<italic>K</italic><sub><italic>r</italic></sub>(<italic>θ</italic>) and <italic>K</italic><sub><italic>a</italic></sub>) became 0.821 (being significant at <italic>P</italic><0.01) with the slope (<italic>b</italic>) of -2.54 and intercept (<italic>a</italic>) of -10.93. For the comparison propose <italic>K</italic><sub><italic>r</italic></sub>(<italic>θ</italic>) were also predicted from RETC using experimental SMC data and van Genuchten and Brooks-Corey models. The reliability of the <italic>K</italic><sub><italic>r</italic></sub>(<italic>θ</italic>) prediction from <italic>K</italic><sub><italic>a</italic></sub> based on root mean square error (RMSE), geometric mean error ratio (GMER), and geometric standard deviation of error ratio (GSDER) criteria became considerable greater than those predicted from the two mentioned models.

Improving prediction of hydraulic conductivity by constraining capillary bundle models to a maximum pore size

The prediction of unsaturated hydraulic conductivity from the soil water retention curve by pore-bundle models is a cost-effective and widely applied technique. One problem for conductivity predictions from retention functions with continuous derivatives, i.e. continuous water capacity functions, is that the hydraulic conductivity curve exhibits a sharp drop close to water saturation if the pore-size distribution is wide. So far this artifact has been ignored or removed by introducing an explicit air-entry value into the capillary saturation function. However, this correction leads to a retention function which is not continuously differentiable. We present a new parameterization of the hydraulic properties which uses the original saturation function (e.g. of van Genuchten) and introduces a maximum pore radius only in the pore-bundle model. In contrast to models using an explicit air entry, the resulting conductivity function is smooth and increases monotonically close to saturation. The model concept can easily be applied to any combination of retention curve and pore-bundle model. We derive closed-form expressions for the unimodal and multimodal van Genuchten–Mualem models and apply the model concept to curve fitting and inverse modeling of a transient outflow experiment. Since the new model retains the smoothness and continuous differentiability of the retention model and eliminates the sharp drop in conductivity close to saturation, the resulting hydraulic functions are physically more reasonable and ideal for numerical simulations with the Richards equation or multiphase flow models.

Sample dimensions effect on prediction of soil water retention curve and saturated hydraulic conductivity

Soil water retention curve (SWRC) and saturated hydraulic conductivity (SHC) are key hydraulic properties for unsaturated zone hydrology and groundwater. In particular, SWRC provides useful information on entry pore-size distribution, and SHC is required for flow and transport modeling in the hydrologic cycle. Not only the SWRC and SHC measurements are time-consuming, but also scale dependent. This means as soil column volume increases, variability of the SWRC and SHC decreases. Although prediction of the SWRC and SHC from available parameters, such as textural data, organic matter, and bulk density have been under investigation for decades, up to now no research has focused on the effect of measurement scale on the soil hydraulic properties pedotransfer functions development. In the literature, several data mining approaches have been applied, such as multiple linear regression, artificial neural networks, group method of data handling. However, in this study we develop pedotransfer functions using a novel approach called contrast pattern aided regression (CPXR) and compare it with the multiple linear regression method. For this purpose, two databases including 210 and 213 soil samples are collected to develop and evaluate pedotransfer functions for the SWRC and SHC, respectively, from the UNSODA database. The 10-fold cross-validation method is applied to evaluate the accuracy and reliability of the proposed regression-based models. Our results show that including measurement scale parameters, such as sample internal diameter and length could substantially improve the accuracy of the SWRC and SHC pedotransfer functions developed using the CPXR method, while this is not the case when MLR is used. Moreover, the CPXR method yields remarkably more accurate soil water retention curve and saturated hydraulic conductivity predictions than the MLR approach.

Considering Surface Roughness Effects in a Triangular Pore Space Model for Unsaturated Hydraulic Conductivity

Quantifying the unsaturated hydraulic conductivity of a porous medium has been a great interest in the fields of hydrology, environmental engineering, and petroleum engineering. Previous research has shown that rough surfaces enhance liquid retention and conductance of flow in the form of liquid film. We present a pore‐scale‐based water retention and hydraulic conductivity model considering surface roughness effects. In the proposed model, a porous medium is simplified as a bundle of statistically distributed capillaries with triangular cross‐sections. Surface roughness effects are characterized by a roughness factor, which accounts for increased film thickness under relatively wet conditions due to capillary effects and increased film area under relatively dry conditions. The model significantly improved the prediction of hydraulic conductivity across the entire range of matric potentials for the illustrative soils compared with the van Genuchten–Mualem model (VGM), while maintaining the same number of adjustable parameters. The improved performance of the proposed model demonstrates the advantage of incorporating surface roughness in the pore‐scale‐based models. Furthermore, sandy soils and loams showed distinct roughness factors and pore‐size distribution functions. Sandy soils tended to have smaller roughness factors and greater mean pore sizes than loams.

SATURATION DEPENDENCE OF TRANSPORT IN POROUS MEDIA PREDICTED BY PERCOLATION AND EFFECTIVE MEDIUM THEORIES

Accurate prediction of the saturation dependence of different modes of transport in porous media, such as those due to conductivity, air permeability, and diffusion, is of broad interest in engineering and natural resources management. Most current predictions use a "bundle of capillary tubes" concept, which, despite its widespread use, is a severely distorted idealization of natural porous media. In contrast, percolation theory provides a reliable and powerful means to model interconnectivity of disordered networks and porous materials. In this study, we invoke scaling concepts from percolation theory and effective medium theory to predict the saturation dependence of modes of transport — hydraulic and electrical conductivity, air permeability, and gas diffusion — in two disturbed soils. Universal scaling from percolation theory predicts the saturation dependence of air permeability and gas diffusion accurately, even when the percolation threshold for airflow is estimated from the porosity. We also find that the non-universal scaling obtained from the critical path analysis (CPA) of percolation theory can make excellent predictions of hydraulic and electrical conductivity under partially saturated conditions.

The use of electrical conductivity measurements in the prediction of hydraulic conductivity of unsaturated soils

Summary Statistical models have been widely used in soil science, hydrogeology and geotechnical engineering to predict the hydraulic conductivity of unsaturated soils. However, no effective method is available yet for the determination of the associated model parameters such as the tortuosity factor q. Considering the analogy between water flow and electrical current flow in a porous medium, in this study, we proposed to improve the predictive capability of statistical models by determining the tortuosity factor q using electrical conductivity (EC) measurements. We first developed a theoretical hydraulic–electrical conductivity (K–EC) relationship for unsaturated soils based on the bundle of capillary tubes model. This K–EC relationship was then used to form a new unsaturated soil EC model, which was verified using published experimental data. The tortuosity factor q can then be determined by fitting the new EC model to soil EC measurements. Experimental data of six soils were used to test the effectiveness of this method and it was shown that the prediction was significantly improved when compared with the one using the commonly suggested value q = 0.5. The associated root-mean-square-deviation (RMSD) between measurements and predictions is only 0.28 when q is obtained by using our proposed method. In contrast, the RMSD is 0.97 when q is simply assumed as 0.5.

Prediction of Hydraulic Conductivity of Soil Bentonite Mixture Using Hybrid-ANN Approach

Due to its low hydraulic conductivity compacted soil-bentonite mixture is widely used as a barrier material at waste disposal site. The experimental determination of hydraulic conductivity of soil-bentonite mixture, which depends on the various physical and chemical and mineralogical factors, requires expensive and time consuming setup. Thus, a hybrid neural network model (combining genetic algorithm with neural network) is presented here as a complementary tool to model hydraulic conductivity of soil-bentonite mixture. The prediction capability of the model has been found to be satisfactory. The developed model yielded correlation coefficients of 0.98 and 0.97 for training and testing data sets, respectively. The proposed model was compared with conventional neural network models by using different statistical indicators such as Nash-Sutcliffe model efficiency and discrepancy ratio with standard deviation. It was found that the predictions obtained from developed model agreed well with experimental observations. Identification of important parameters and ranking their order of influence on hydraulic conductivity has been discussed by using input significance test.

A new equation for the soil water retention curve

SummaryThe soil water retention curve is a fundamental characteristic of unsaturated zone flow and transport properties. Recent studies show that an air‐entry value is needed in a soil water retention equation in order to provide a better prediction of relative hydraulic conductivity. A new equation considering the air‐entry value is proposed to describe the soil water retention curve. The performance of the proposed equation is contrasted with a well‐supported equation by comparing measured and calculated data for 14 soils, representing various soil textures, which range from sandstone to clay. Results show that the proposed equation provides adequate characterization of the soil water retention curves. The equation for predicting relative hydraulic conductivity is derived from the proposed soil water retention equation. An empirical equation for relative hydraulic conductivity is also used. Our results show that the agreement between the predicted and measured relative hydraulic conductivities is improved by the combinations of the proposed equation and the relative hydraulic conductivity equations. The proposed equation is mathematically simple and it can easily be implemented in unsaturated flow and multiphase flow numerical models.