Variability Of Soil Parameters Research Articles

Variation in Soil Parameters in the Thaulla Area of Ulankulama Tank at Anuradhapura, Sri Lanka

Variation in Soil Parameters in the Thaulla Area of Ulankulama Tank at Anuradhapura, Sri Lanka

Reliability analysis of foundation settlement by stochastic response surface and random finite element method

This paper presents a reliability-based analysis of strip footing settlement by Stochastic Finite Element Method (SFEM). The Stochastic Response Surface Method (SRSM) and Random Finite Element Method (RFEM) are used as two formulation of SFEM. The elastic properties of soil are considered as spatial random variables and modeled as cross correlated log–normal random fields. Random field discretization is done by Karhunen–Loeve (K-L) expansion. Two programs were coded by MATLAB so as to take full advantages of its matrix operations and in an illustrative example; it was shown that the results of SRSM are close to RFEM however the consumed time in RFEM is at most 50 times longer than SRSM. Using the faster method, SRSM, it is concluded that considering the spatial variability of soil parameters in stochastic analysis is necessary.

Reliability evaluation of slope considering geological uncertainty and inherent variability of soil parameters

Geological uncertainty is often ignored in slope reliability analysis, even though the inherent variability of soil parameters is considered. This paper aims to propose a method for slope reliability analysis by considering both the inherent variability of soil parameters and geological uncertainty. A coupled Markov chain (CMC) model is used to simulate the geological uncertainty. An implementation procedure for slope reliability considering the aforementioned two types of uncertainty is provided. A slope reliability problem is analyzed to validate the proposed method with the borehole data from Perth, Australia. Different borehole layout schemes are designed to reflect the effect of both number and location of boreholes on slope reliability. Moreover, which type of uncertainty mentioned above has a greater impact on slope reliability is explored. The results indicate that the proposed method can effectively evaluate the slope reliability considering these two types of uncertainty. If only the inherent variability is considered, the accuracy of reliability analysis mainly depends on the used geological profiles. Borehole layout scheme has a significant effect on slope reliability. Both probability of failure (Pf) and mean of factor of safety (FS) of slope do not monotonously vary with an increasing number of boreholes. The boreholes designed in the critical influence zone can provide more information to improve the accuracy of slope reliability. The coefficients of variation (COVs) of shear strength parameters and the discrepancy among the means of shear strengths for different soils play different and major role between the two types of uncertainty in the reliability analysis of soil slope.

Numerical simulation of electro-osmotic consolidation coupling non-linear variation of soil parameters

Electro-osmotic consolidation is an effective method for soft ground improvement. A main limitation of previous numerical models on this technique is the ignorance of the non-linear variation of soil parameters. In the present study, a multi-field numerical model is developed with the consideration of the non-linear variation of soil parameters during electro-osmotic consolidation process. The numerical simulations on an axisymmetric model indicated that the non-linear variation of soil parameters showed remarkable impact on the development of the excess pore water pressure and degree of consolidation. A field experiment with complex geometry, boundary conditions, electrode configuration and voltage application was further simulated with the developed numerical model. The comparison between field and numerical data indicated that the numerical model coupling of the non-linear variation of soil parameters gave more reasonable results. The developed numerical model is capable to analyze engineering cases with complex operating conditions.

Load-settlement Response of a Composite Foundation: A Reliability-Based Design Approach

A composite foundation in the current study is improved by the joint usage of two types of columns. The soil is modeled as a Mohr-Coulomb material, and the stress-strain relationship is simplified as bilinear elastio-plastic with associated flow rule. Explicit expressions of the equivalent modulus of the composite foundation under elastic or/and plastic conditions are obtained using the parametric variational principle. A critical stress is proposed to judge whether plastic state of soils occurs under a prescribed vertical loading. As a result, the equivalent modulus, taking into account the spatial variation of soil parameters, can improve the prediction in settlement of the composite foundation. The geostatistical technique is recommended to evaluate the correlation length in soils along the vertical direction. Considering the relatively complex mathematical formula in the limit state equation, Monte-Carlo simulation is employed to calculate the probability failure. Finally, a reliability-based design methodology is proposed to balance the safety and economy considerations in designing the area replacement ratios of those two types of columns in a composite foundation.

Soil Probabilistic Slope Stability Analysis Using Stochastic Finite Difference Method

The paper contrasts results obtained by the partially factored limit state design method and a more advanced Random Finite Difference Method (RFDM) in a benchmark problem of slope stability analysis with variable undrained shear strength. Local Average Subdivision method was used to simulate the non-Gaussian random variables. The key difference between the methods is that RFDM takes into account spatial variability of soil parameters allowing slope failure to occur naturally along the path of least resistance. The probabilistic method leads to predictions of the "probability of slope failure" as opposed to the more traditional "factor of safety" measure of slope safety in the limit state design method; however, they give significant different results depending on the level of the variability. Analyses conducted using Monte Carlo Simulation show that the same partial factor can have very different levels of risk depending on the degree of uncertainty of the mean value of the soil shear strength. Calibration studies show the partial factor necessary to achieve target probability values.

Evaluation and improvement of the default soil hydraulic parameters for the Noah Land Surface Model

Soil hydraulic parameters used in estimating soil water and energy fluxes are included in a lookup table in the Noah Land Surface Model (LSM). The purpose of the study was to examine the Noah default soil hydraulic parameters and compare them to soil measurements across Texas, USA. These default soil parameters were compared to measured soil properties from a soil database including 6749 soil samples located within and around Texas. Mean differences between measured and default soil parameters were tested using a t-test (α=0.05). To assess the proposed changes to default soil parameters, water retention curves were created using updated parameters and compared to measured soil moisture content at field capacity (θfc) and permanent wilting point (θwp). Spatial trends across major land resource areas in Texas were also demonstrated for water retention parameters. Our findings indicate that 95% of the default soil parameters were significantly different from the region-specific measured values. Measured soil water content at air-dry, θwp, and θfc are better replacements than default values. Consequent changes in θwp and θfc yielded a 35 to 76% decrease in plant available water compared to default. Updated water retention curves showed improved agreement between estimated soil water and measured values in 95% of cases. For three texture classes, the standard deviations of parameters for water retention parameters ranged 30% for the slope of the water retention curve and 65% for saturated hydraulic conductivity. These results indicate the importance of accounting for spatial variability of soil parameters rather than combining these parameters into texture classes alone. The revised parameter table improves modeling of soil hydraulic properties. Ultimately spatially distributed databases of hydraulic soil parameters will better capture variability and spatial structure of soil processes modeled by Noah LSM.

Multifractal analysis in soil properties: Spatial signal versus mass distribution

The spatial variability of soil properties is a constant expected factor that must be considered in soil studies. This variability is composed of “functional” variations and random fluctuations or noise. Multifractal formalism is suitable for variables with self-similar distributions on a spatial domain. Multifractal analysis can provide insight into the spatial variability of soil parameters. In soil science, it has been quite popular to characterize the scaling property of a variable measured along a transect as a mass distribution of a statistical measure on a length domain of the studied transect. The analysed variable is divided into a number of self-similar segments, and the partition function and mass function are estimated. Based on these estimations, the multifractal spectrum (MFS) is calculated. Another technique that can be applied focuses on the variations of a measure by analysing the absolute differences in the soil property values at different scales, such as the Generalized Structure Function (GSF) and the Universal Multifractal Model (UMM). The aim of this study was to compare both types of multifractal methods on a set of soil physical properties measured on a common 1024m transect across arable fields at Silsoe in Bedfordshire, East-Central England. The studied properties were total porosity (Porosity), gravimetric water content (GWC) and nitrous oxide flux (N2O flux). The results showed that when using both methods, the N2O flux exhibits a distinctive multifractal character, and weak multifractal characters are detected in the GWC and Porosity cases. Additionally, several parameters were calculated and discussed.Finally, the relationship between the mass exponent function (τ(q)) and the GSF (ζ(q)) found in the literature, was positively verified for the three variables. On the contrary, the relationship between ζ(q) and the scaling exponent function based on UMM (K(q)) showed discrepancies in N2O flux and GWC for q values higher than 3. This is the first time that these comparisons have been made on soil property data.

Determination of Vertical Bearing Capacity of Pile Foundation Systems in Tropical Soils with Uncertain and Highly Variable Properties

AbstractThis study presents an innovative procedure to derive the ultimate load capacity of a pile system by considering the natural (intrinsic) variability of a tropical soil. The tropical soil has high aluminum and iron content due to processes of lixiviation of the upper soil layers. As a consequence, this soil has a porous cemented structure, high void ratio, high permeability, and high variability of the geotechnical parameters in its depth. Soil layers are experimentally characterized via standard characterization, a triaxial test, standard penetration test, and flat dilatometer tests. Also, the load test of the standard groups (PG) and pile plus pile-cap (PC), which had self-drilling piles, were performed at real scale. Afterwards, simulations using a three-dimensional finite-element method (3D FEM) considering variations in soil parameters were performed for a hypothetical piles-plus-pile cap system under vertical loading using the random finite-element method. Mean parameters were verified throug...

Prediction of Soil Liquefaction Using Genetic Programming

In most geotechnical problems, it is too difficult to predict soil and structural behavior accurately, because of the large variation in soil parameters and the assumptions of numerical solutions. But recently many geotechnical problems are solved using Artificial Intelligence (AI) techniques, by presenting new solutions or developing existing ones. Genetic Programming, (GP), is one of the most recently developed (AI) techniques based on Genetic Algorithm (GA) technique. In this research, GP technique is utilized to develop prediction criteria for liquefaction phenomena in cohesivless soils using collected historical records. The liquefaction formula is developed using special software written by the authors in “Visual C ” language. The accuracy of the developed formula was also compared with earlier prediction methods.

Comparison Between Two Methods for Estimating the Vertical Scale of Fluctuation for Modeling Random Geotechnical Problems

Abstract The design process in geotechnical engineering requires the most accurate mapping of soil. The difficulty lies in the spatial variability of soil parameters, which has been a site of investigation of many researches for many years. This study analyses the soil-modeling problem by suggesting two effective methods of acquiring information for modeling that consists of variability from cone penetration test (CPT). The first method has been used in geotechnical engineering, but the second one has not been associated with geotechnics so far. Both methods are applied to a case study in which the parameters of changes are estimated. The knowledge of the variability of parameters allows in a long term more effective estimation, for example, bearing capacity probability of failure.

Pedotransfer in soil physics: trends and outlook — A review —

Parameters governing the retention and movement of water and chemicals in soils are notorious for the difficulties and high labor costs involved in measuring them. Often, there is a need to resort to estimating these parameters from other, more readily available data, using pedotransfer relationships. This work is a mini-review that focuses on trends in pedotransfer development across the World, and considers trends regarding data that are in demand, data we have, and methods to build pedotransfer relationships. Recent hot topics are addressed, including estimating the spatial variability of water contents and soil hydraulic properties, which is needed in sensitivity analysis, evaluation of the model performance, multimodel simulations, data assimilation from soil sensor networks and upscaling using Monte Carlo simulations. Ensembles of pedotransfer functions and temporal stability derived from “big data” as a source of soil parameter variability are also described. Estimating parameter correlation is advocated as the pathway to the improvement of synthetic datasets. Upscaling of pedotransfer relationships is demonstrated for saturated hydraulic conductivity. Pedotransfer at coarse scales requires a different type of input variables as compared with fine scales. Accuracy, reliability, and utility have to be estimated independently. Persistent knowledge gaps in pedotransfer development are outlined, which are related to regional soil degradation, seasonal changes in pedotransfer inputs and outputs, spatial correlations in soil hydraulic properties, and overland flow parameter estimation. Pedotransfer research is an integral part of addressing grand challenges of the twenty-first century, including carbon stock assessments and forecasts, climate change and related hydrological weather extreme event predictions, and deciphering and managing ecosystem services. Overall, pedotransfer functions currently serve as an essential instrument in the science-based toolbox for diagnostics, monitoring, predictions, and management of the changing Earth and soil as a life-supporting Earth system.

Influence of frequency characteristics of soil parameters on ground-return transmission line parameters

Electromagnetic transient processes are strongly related to the parameters of transmission lines. With the ground return parameters considered, the frequency dependent parameters of transmission lines are highly dependent on the soil parameters, which include resistivity and dielectric permittivity. In this paper, with the measurement results of frequency variation of soil parameters, the influence of the frequency dependent soil parameters on the frequency dependent characteristics of the transmission line parameters were studied using the complex return plane method. According to the calculation results, the frequency characteristics of soil parameters have obvious influences on the parameters of overhead transmission lines. The influences of the humidity, temperature and particle size of the soil on the ground-return parameters were also evaluated.

Permanent soil monitoring system as a basic tool for protection of soils and sustainable land use in Slovakia

The purpose of soil monitoring system in Slovakia is to better protect the soils with regard to sustainable land use. The main object is the observation of soil parameters indicative of change to the equilibrium of soil system as far as to the irreversible change with possible development of degradation processes in soil. The soil monitoring system in Slovakia has been running since 1993. Its importance consists of providing the information on changing spatial and temporal variations of soil parameters as well as the evolution of soil quality in topsoil and subsoil. The soil monitoring network in Slovakia is constructed using ecological principles, taking into account all main soil types and subtypes, soil organic matter, climatic regions, emission regions, polluted and non-polluted regions as well as various other land uses. The results of soil monitoring of 318 sites on agricultural land in Slovakia have been presented. Soil properties are evaluated according to the main threats to soil relating to European Commission recommendation for European soil monitoring performance as follows: soil erosion, soil compaction, decline in soil organic matter, soil salinization and sodification and soil contamination. The most significant change has been determined in physical properties of soils. The physical degradation was especially manifested in compacted and the eroded soils. On the basis of our results about 40%of agricultural land is potentially affected by soil erosion in Slovakia. In addition, decline in soil organic matter and available nutrients indicate seriousness of soil degradation processes observed during the last monitoring period in Slovakia. Measured data and required outputs are reported to Joint Research Centre (JRC) in Ispra(Italy) and European Environmental Agency (EEA) in Copenhagen (Denmark). Finally, the soil monitoring system thus becomes a basic tool for protection of soils and sustainable land use as well as for the creation of legislation not only in Slovakia, but in EU, too.

Tools for Optimizing Management of a Spatially Variable Organic Field

Geostatistical tools were used to estimate spatial relations between wheat yield and soil parameters under organic farming field conditions. Thematic maps of each factor were created as raster images in R software using kriging. The Geographic Resources Analysis Support System (GRASS) calculated the principal component analysis raster images for soil parameters and yield. The correlation between the raster arising from the PC1 of soil and yield parameters showed high linear correlation (r = 0.75) and explained 48.50% of the data variance. The data show that durum wheat yield is strongly affected by soil parameter variability, and thus, the average production can be substantially lower than its potential. Soil water content was the limiting factor to grain yield and not nitrate as in other similar studies. The use of precision agriculture tools helped reduce the level of complexity between the measured parameters by the grouping of several parameters and demonstrating that precision agriculture tools can be applied in small organic fields, reducing costs and increasing wheat yield. Consequently, site-specific applications could be expected to improve the yield without increasing excessively the cost for farmers and enhance environmental and economic benefits.

Identification of Yield-Limiting Factors in Southeast Kansas Cropping Systems

Crop performance and yield within production fields varies as a function of growing environment and soil properties within the field. Components contributing to yield in corn, wheat, and soybean production were examined through on-farm measurements of soil properties in southeast Kansas. Additional tests in research plots explored components contributing to yield in greater detail. Environmental variability between the 2013 and 2014 growing seasons contributed to differences in yield. Additional variability in soil parameters influenced crop performance, particularly for soils high in clay content.

Phytodiversity and Seasonal Variations in the Soil Characteristics of Shrublands of Dachigam National Park, Jammu and Kashmir, India

The panorama of Indian biological resources is much wider making it one of the mega biodiverse (hotspot) centres of the world. Biodiversity is an element of natural resource base which is the main component of sustainability in both production and utilization of the ecosystem services. The present study was carried out on the Shrublands of the Dachigam National Park of Western Himalaya, Kashmir, in the year 2010. The vegetation was analysed for floristic composition, phytosociological aspects and seasonal variations in major soil characteristics of the study area. A seasonal variation in soil parameters was observed with temperature varying from 2 to 21°C, moisture 7.28–32.34%, pH 5.1–7.44, electrical conductivity 150–520 μS/cm, organic carbon 3.27–5.98%, organic matter 5.63–10.30%, total nitrogen 2500–4600 ppm, total phosphorus 757–1226 ppm and total potassium 15, 500-18, 550 ppm. A total of 35 shrub species were recorded belonging to 13 families and 19 generas with maximum richness and density in protected site and least at village site.

Effects of spatial variability of soil properties on the seismic response of an embankment dam

In this paper a numerical procedure for a two-dimensional seismic analysis of an embankment dam is presented taking into account the spatial variability of soil properties. The approach integrates advanced numerical modelling and random field theory into a probabilistic framework. Geotechnical parameters are modelled as anisotropic random fields with different autocorrelation distances in the vertical and horizontal directions. The correlation between cohesion and friction angle values is taken into account with Gaussian cross-correlated random fields using a specific marginal distribution, autocorrelation functions and cross-correlation coefficients. Based on this stochastic representation of the soil domain, fully nonlinear time-history analyses are carried out for several earthquake records of varying intensities. The results of the analyses are used to explicitly derive fragility functions that include the influence of spatial variability of soil parameters and the uncertainty of ground motion.

Scalability of classical terramechanics models for lightweight vehicle applications incorporating stochastic modeling and uncertainty propagation

This paper investigates the validity of commonly used terramechanics models for lightweight vehicle applications while accounting for experimental variability. This is accomplished by cascading uncertainty up to the point of wheel performance. Vehicle-terrain interaction is extremely complex, and thus models and simulation methods for vehicle mobility prediction are largely based on empirical data. Analytical methods are compared to experimental measurements of key operational parameters such as drawbar force, torque, and sinkage. Models of operational parameters ultimately depend on a small set of empirically determined soil parameters, each with an inherent uncertainty due to test variability. The soil parameters associated with normal loads are determined by fitting the dimensionless form of Bekker’s equation to the pressure-sinkage test data. Similarly, the soil parameters associated with shear loads are determined by fitting Janosi-Hanamoto’s equation to the direct shear test data. An uncertainty model is used to propagate the soil parameter variability through to the wheel performance based on Wong and Reece. This commonly used analytical model is shown to be inaccurate as the envelope of model uncertainty does not lie within the experimental measures, suggesting that model improvements are required to accurately predict the performance of lightweight vehicles on deformable terrain.

Spatial Variability of Soil Parameters of the van Genuchten Model at a Regional Scale

Spatial heterogeneity of soil water retention curves (SWRC) plays an important role in modelling the movement of water and solutes in soils. To characterize the spatial variability of SWRC parameters fitted by the van Genuchten (VG) equation, and then identify factors that closely correlated with the VG parameters, we collected undisturbed and disturbed soil samples from the upper 0–5 cm soil layer at 382 sampling sites across the entire Loess Plateau of China (~620 000 km2). We determined the SWRC and six other soil properties as well as nine environmental factors for each site. Results showed that the saturated water content (θs) and a curve‐shape parameter that is related to soil pore size distribution (n) were weakly spatially variable (coefficient of variation (CV) = 15%), and that the residual soil water content (θr) and a scaling parameter that is related to the inverse of the air entry pressure (α) were moderately (CV = 96%) and strongly variable (CV = 136%), respectively. Semivariograms of and log(α) were best‐fitted by an isotropic exponential model, while those of θs and log(n) were fitted by an isotropic spherical model. Fractal analysis indicated that log(α) demonstrated the most short‐range variation, while log(n) reflected the importance of long‐range variation. Bulk density and the contents of clay, silt, sand and soil organic carbon contributed greatly to the variations in the VG parameters (except α). However, the vegetation, topographic and climatic elements as well as land use also usually explained some of the variation. The VG parameters, at the regional scale, demonstrated different characteristics of spatial variation, which comprehensively reflected the combined effects of processes involving the soil, vegetation, topography and climate as well as effects of land use, which is often determined by human activity.