Soil Spatial Variability Research Articles

The effects of nonlinear soil spatial variability on the nonlinear beam response


 
 
 
 The nonlinear analysis of a beam resting on nonlinear random tensionless soil was studied with the aim of quantifying the influence of the spatial variability of the tension soil characteristics on the behavior of the beam and illustrating the importance of the geometric nonlinear analysis of a beam. The soil–structure interaction mechanism is taken into account where the soil is modeled as nonlinear. Due to large deflections and moderate rotations of the beam, the Von-Kàrman type nonlinearity based on the finite element formulation of nonlinear beam response is adopted and the interfacial shearing resistance along the beam is taken into account. To assess the probabilistic nonlinear behavior of the beam resting on nonlinear random soil, a Monte Carlo approach was used and the response of the beam was statistically analyzed for different beam and soil parameters. The equation of motion is solved using the Newton-Raphson iterative method which is implemented by using a Matlab software. The results indicate that the probabilistic approach of the soil and the geometric nonlinearity of the beam serve a major role in the evaluations of the beam response (of the real response of the buried beam).
 
 
 

Temperature monitoring in levees for detection of seepage

Improving knowledge of existing levees through investigation and monitoring is an important step in evaluating their safety and that of the surrounding area. Nevertheless, these activities are complex due to the considerable levee length and the high spatial variability of soil composing the body and foundation, especially when paleo-rivers are present. In order to investigate the reliability of new advanced techniques proposed for characterizing the soil stratigraphy and the seepage condition within the levee foundation, a new test site was realized along the Adige River in Bolzano Province (Italy). Here, five boreholes, drilled in a 20-m-side square area straddling the embankment, host four different types of monitoring equipment, among which some are Distributed Fiber Optical Sensors (DFOS), here used for detecting the temperature variations along the well. The present paper focuses on the critical analysis of the preliminary results obtained with DFOS and their comparison with data obtained using traditional pressure and temperature probes. The monitoring data collected in the field during the passage of a flood that occurred on 5th August 2021 are used to better understand the hydraulic behavior and the safety conditions of the levee but also to fully assess the reliability and potential of DFOS.

Spatial variability of soil resistance penetration in forest areas converted for agricultural uses in the Amazon, Brazil

The knowledge of the spatial variability of soil attributes has been characterized as an important step for an adequate management to be carried out. Thus, the aim of this study was to evaluate spatial variability of soil resistance to penetration in areas under forest and converted to agricultural uses in the Amazon, Brazil. The study was carried out in Typic Hapludultsin the San Francisco Settlement located in the municipality of Canutama, Amazonas, Brazil. Four areas were selected. The collections proceeded at the crossing points of the grids, under the layers of 0.00-0.05; 0.05-0.10; and 0.10-0.20 m, making a total of 80 sample points in each area. Physical soil analyzes were carried out, with the criterion of determining the texture, soil resistance penetration and soil moisture. The variation in soil moisture content remained constant in all evaluated layers. The PR and soil moisture showed a spatial dependence structure that made it possible to analyze the influence on the different conditions of use and evaluated layers, since the scope of spatial dependence was preponderant to evaluate the scales of spatial variation, mainly in verifying the possible effects and factors that most affect these variables analyzed.

Obtaining Spatial Variations in Cabernet Sauvignon (Vitis vinifera L.) Wine Flavonoid Composition and Aromatic Profiles by Studying Long-Term Plant Water Status in Hyper-Arid Seasons

The spatial variability in vineyard soil might negatively affect wine composition, leading to inhomogeneous flavonoid composition and aromatic profiles. In this study, we investigated the spatial variability in wine chemical composition in a Cabernet Sauvignon (Vitis vinifera L.) vineyard in 2019 and 2020. Because of the tight relationships with soil profiles, mid-day stem water potential integrals (Ψstem Int) were used to delineate the vineyard into two zones, including Zone 1 with relatively higher water stress and Zone 2 with relatively lower water stress. Wine from Zone 2 generally had more anthocyanins in 2019. In 2020, Zone 1 had more anthocyanins and flavonols. Zone 2 had more proanthocyanidin extension and terminal subunits as well as total proanthocyanidins in 2020. According to the Principal Component Analyses (PCA) for berry and wine chemical composition, the two zones were significantly different in the studied wine aromatic compounds. In conclusion, this study provides evidence of the possibility of managing the spatial variability of both wine flavonoid composition and aromatic profiles through connecting vineyard soil variability to grapevine season-long water status.

Stability analysis of a loess landslide considering rainfall patterns and spatial variability of soil

Accurate analysis of the stability of rainfall-induced landslides is an important and urgent task. Under complex geological conditions, traditional stability analysis methods cannot meet stability evaluation requirements. To study the impact of the rainfall pattern on the probability of instability of loess slopes, the intensity-frequency-duration curve and breakdown coefficient methods were introduced to analyse the effects of the rainfall extremum distribution and time scale, and bounded random cascade simulation were used to generate random rainfall patterns (RRPs). The obtained RRPs under several classical rainfall patterns were compared in terms of the safety factor, rainfall threshold (IaS/IaF), and annual failure probability (AFP) of loess slopes. Finally, soil preferential flow and spatial variability combined with the RRPs were introduced to analyse the change in the stability of rainfall-induced loess landslides under different theoretical frameworks. It was revealed that the use of a uniform rainfall pattern produced conservative stability evaluation results. Considering the RRPs, the calculation of IaS/IaF and AFP should include the common characteristics of various rainfall patterns. By comprehensively evaluating slope stability considering soil spatial variability and rainfall pattern variability, this study revealed that neglecting certain factors may result in incorrect slope stability estimates. Incorporating multiple assumptions in failure probability evaluation could provide more comprehensive slope safety assessment.

A GSD‐driven approach to deriving stochastic soil strength parameters under hybrid machine learning models

AbstractThe quantification of soil strength parameters is a crucial prerequisite for constructing physical models related to hydro‐geophysical processes. However, due to ignoring soil spatial variability at different scales, traditional parameter assignment strategies, such as assigning values depending on land use classification or other classification systems, as well as those extrapolation and interpolation methods, are insufficient for physical process modelling. This work addressed this deficiency by proposing a method to derive stochastic soil strength parameters under hybrid machine learning (ML) models, taking into account the grain‐size distribution (GSD) of soil with scaling invariance. The nonlinear connection between GSD parameters (derived from GSD curves, such as μ and Dc), moisture content, and soil shear strength parameters was fitted by the suggested hybrid ML model. An analysis of a case study revealed that: (i) the Multi‐layer Perceptron optimized by the African Vulture Optimization Algorithm (AVOA) algorithm performs the best and can estimate the shear strength parameters of soil mass on vegetated slopes; (ii) all the selected ML models showed significant improvements in predictive performance after optimization with the AVOA, with R2 scores increasing by 24.72% for Support Vector Regressor, 34.04% for eXtreme Gradient Boosting, and 35.53% for Multi‐layer Perceptron; and (iii) soil cohesion has an increasing relationship with the GSD parameter μ, while soil internal friction angle has a negative correlation with the grain‐size parameter Dc. The proposed methodology can give predictions of soil shear strength distribution parameters, providing parameter support for the physical modelling of surface process dynamics.

Reliability analysis of offshore monopile foundations considering multidirectional loading and soil spatial variability

Monopiles are the primary type of foundations for offshore wind turbines. Current monopile design methods do not adequately account for the effects of multidirectional loading and soil spatial variability, which may underestimate the failure probability of the foundations. This study aims to investigate the reliability of offshore monopile foundations considering the effects of multidirectional loading and soil spatial variability. A non-intrusive random finite element method combined with Monte Carlo simulation is employed. The behaviors of monopiles in different spatially variable marine soils and loading paths are simulated. Through the reliability analysis, safety factors (FSdesign) are recommended for different scenarios. The results demonstrate that the combined effects of multidirectional loading and soil spatial variability significantly affect the monopile foundation performance and that ignoring these factors will underestimate the failure probability. If the effect of multidirectional loading is more prominent or the marine soil exhibits stronger spatial variability, a larger FSdesign is required to assure an acceptable failure probability.

Enhancing the accuracy of digital soil mapping using the surface and subsurface soil characteristics as continuous diagnostic layers.

Digital soil mapping relies on relating soils to a particular set of covariates, which capture inherent soil spatial variation. In digital mapping of soil classes, the most commonly used covariates are topographic attributes, RS attributes, and maps, including geology, geomorphology, and land use; in contrast, the subsurface soil characteristics are usually ignored. Therefore, we investigate the possibility of using soil diagnostic characteristics as covariates in a mountainous landscape as the main aim of this study. Conventional covariates (CC) and a combination of soil subsurface covariates with conventional covariates (SCC) were used as covariates, and random forest (RF), Multinomial Logistic Regression (LR), and C5.0 Decision Trees (C5) were used as different machine learning algorithms in digital mapping of soil family classes. Based on the results, the RF model with the SCC dataset had the best performance (KC = 0.85, OA = 90). In all three models, adding soil covariates to the sets of covariates increased the model performance. Soil covariates, slope, and aspect were selected as the principal auxiliary variables in describing the distribution of soil family classes.

Analysis of tunneling-induced ground movements in spatially variable soil under the influence of existing building

Tunnel construction invariably results in ground movement, which is significantly impacted by the spatial variability of soil properties and proximity of existing buildings. However, few studies have examined the coupled effect of these two factors on tunneling-induced ground movement. This study establishes a stochastic finite element model that incorporates both the spatial variability of soil properties and influence of existing buildings. The model is used to perform a series of random analyses, studying the effects of story number, eccentricity, and building length on ground settlements induced by tunneling in soil with spatial variability of Young’s modulus. A probabilistic analysis is conducted to investigate the uncertainties associated with ground settlements. The results of the analysis indicate that the presence of the building enhances the influence of the spatial variability of soil, leading to an increase in the dispersion of ground settlement. For high-rise buildings, the reduction of building eccentricity and length will increase the uncertainty of surface settlement. Neglecting the influence of the existing building in the probability analysis of surface settlement can result in an overestimation of the failure probability.

Geophysical research on the interplays between soil–rock variability and hydrogeological structures: a case study

Understanding the spatial variations in soil–rock profiles is crucial for identifying weathered rock layers and mapping geological structures, particularly in the complex feldspar-rich granitic terrain of Penang Island, Malaysia. This study employs a combination of electrical resistivity and seismic refraction tomographic techniques, along with borehole lithological data, in three distinct areas on the island. Its goal is to provide insights into surficial and subsurface soil–rock characteristics and water-bearing structures within the North Penang Pluton (including a part of the Sungai Ara axis) for sustainable groundwater development, addressing the needs of the region's growing population. The research identifies three distinct geological units at the near-surface: residual soils, highly to moderately weathered/fractured granitic layers and fresh bedrock, each with its hydrogeological properties. Promisingly, the weathered layers offer significant groundwater development potential, with deep weathered and potentially fractured zones exceeding depths of 30 m in the study area. These findings have substantial implications for sustainable groundwater development, especially in tropical hard-rock terrains. In addition, the study underscores the limitations of small geophysical spacings when probing deeper groundwater resources. This knowledge is essential for informed decision-making in water resource management and infrastructure development, addressing the ongoing global challenge of ensuring access to clean water resources amid population growth.

Stochastic analysis of the seismic performance of sheet-pile walls supporting heterogeneous liquefiable ground

This paper presents the findings of a stochastic analysis conducted to evaluate the effects of the spatial soil variability and temporal base motion variability on the seismic response of sheet pile walls supporting liquefiable ground. A random finite element analysis is used to model the centrifuge experiment performed during the 2020 phase of the Liquefaction Experiments and Analysis Project (LEAP). The relative density of the main soil deposit in the LEAP-2020 experiments ranged from 50 to 75 %. In this study the spatial variability of each centrifuge model was determined from the inflight cone penetration tests (CPT) performed before the shaking phase in each experiment. The vertical spatial correlation length was directly determined from the CPT measurements, while the mean and coefficient of variation of the soil relative density were obtained from an empirical relationship correlating the cone tip resistance with the relative density. The variability observed in the achieved base motions for the LEAP-2020 centrifuge experiments was modeled by generating synthetic base motion time histories that matched the variability observed in the response spectra of the achieved base motions. The results obtained from this analysis shed light on the effects of the base motion variability and soil density on the excess pore pressure development and the triggering of soil liquefaction. It also provides an understanding on the sensitivity of the wall lateral displacement to such variabilities. The observed variability in the simulation response is compared with the variability observed in the centrifuge experiments. This comparison is used to evaluate the capability of the current numerical simulation tools in modeling the variability in the wall response.

Stochastic numerical analyses to investigate the effects of the spatial nonuniformity of offshore ground on the serviceability of monopile foundations

ABSTRACT The evidence of soil nonuniformity has been reported in several site investigation reports and is well-confirmed for any soil in general. However, studies related to the spatial soil variability of offshore ground are somewhat limited, and current design practices for monopile foundations also overlook the uncertainty of the offshore ground. This paper presents the probabilistic evaluation of the serviceability limit state of monopile foundations considering the spatial variability of offshore ground. The nonuniformity of the ground is modeled utilizing the site-specific undrained shear strength (su) as a stochastic variable, using a spatially correlated Gaussian random field. A hardening soil constitutive model is utilized in 3D finite element analysis of offshore ground, with a focus on parameter calibration. The reliability of the serviceability limit state is assessed based on the allowable load on the pile head. The stochastic behavior of the maximum bending moment and maximum shear force along the depth of the monopile is also investigated. Besides, the paper also highlights the importance of incorporating ground nonuniformity during the design process of a monopile foundation.

Damage mechanism and failure risk analysis of offshore pipelines subjected to impact loads from falling object, considering the soil variability

Trench and burial, as a primary and effective protection measurement for offshore pipelines from impact loads, has received much research attention recently. Previous studies were usually performed based on the assumption that the soil material was homogeneous with deterministic mechanical properties. The soil spatial variability, which is demonstrated to have significant influences on the soil capacity in marine geotechnical analysis, has not been included. This study was motivated to investigate the response of the buried pipelines subjected to the impact loads, with special address on the soil variability. Firstly, a three-dimensional random large deformation finite element analysis model was developed, which was implemented by the field variable (FV) technique to map the non-stationary random field (NSRF) into the verified Coupled Eulerian-Lagrangian (CEL) model (Hereafter referred to as FVRCEL). Then the FVRCEL model was integrated with the Monte-Carlo simulation (MCS) to obtain the statistical characteristics of the pipeline structural response. The failure mechanisms of the pipeline in the random soil with different fluctuation scales were investigated, and a parametric study was performed to identify the influential factors. Finally, the failure probability curves and surfaces were presented, providing clues for the pipeline safety design. The results revealed that in general, more than 50 % of the realized NSRF scenarios in the random analysis yielded more severe dent damage than the deterministic result, indicating that the latter would underestimate the damage degree, which was more pronounced when the increasing gradient of soil strength was high. The horizontal fluctuation scale had a remarkable influence on the pipeline damage behaviours and the corresponding statistical characteristics, of which the inner mechanisms were discussed. From the probabilistic perspective, at most an extra failure probability of 75 % would be suffered if the soil variability was ignored.

Bearing capacity of shallow foundations considering geological uncertainty and soil spatial variability

Bearing capacity of shallow foundations considering geological uncertainty and soil spatial variability

Evaluating Spatial Soil Parameters of an Apple Orchard in ‘R’ Software: A Study from Kashmir Valley

Maintaining the ecological balance of the physical and biological environment depends greatly on the spatial variation of soil parameters across a given region and its elucidation using remote sensing and GIS applications. R software was used to apply the geostatistical methods of Ordinary Kriging and Inverse Distance Weightage to the study of the spatial distribution and variability of the soil parameters of an apple orchard in the Valley of Kashmir. Average values for available phosphorus (P), potassium (K), calcium (Ca), available nitrogen (N), and soil organic carbon (OC) were 1.15 percent, 315 percent, 22.5 kg, 221.4 kg, and 250 mg, respectively. Ordinary kriging (OK) and IDW were used to plot the spatial distribution of soil parameters using spherical (pH, OC, and Ca), steradian (EC, N, P, and K), and mean square error (MSE) values. According to the semi-variogram analysis, there was a strong to moderate spatial dependence. The interpolated maps showed various soil distribution patterns for pH (5.4-5.7), EC (0.46-0.56 DSM-1), OC (0.9-1.4 percent), N (296-335) kg ha-1, P (20-25 kg ha-1), K (221-221.8 kg ha-1), and Ca (100-400 mg kg-1) at the regional scale. This study represented a wide range of spatial soil variability. The estimation of MSE for Ordinary Kriging and IDW was used to predict the best model after performing a validation accuracy assessment. The best fit model for interpolation was the standard kriging model with low MSE. The maps of soil spatial distribution created to serve as an effective tool for policymakers and those planning farms.

Laterally loaded pile response in spatially variable soil

The spatial variability of soil has long been recognised, yet there is a dearth of research on the response of piles under lateral loading that considers this variability, especially for the lateral load transfer (P-y) curve in spatially variable clay. To address this gap, the study presents a three-dimensional random finite element analysis utilising a non-stationary random field to model soil variability with a linearly increasing trend. The results reveal that the spatial variability significantly affects the lateral capacity of the pile, emphasising the importance of accounting for this variability in pile design. Additionally, the study finds that while the spatial variability affects the failure mechanism, the mechanism remains comparable to that observed in deterministic analyses. The spatial variability has a substantial effect on ultimate soil resistance pressure, which can be fitted using a normal probability distribution function. An adjust factor is suggested to calculate the characteristic value, and from this characteristic value, the fractiles of ultimate soil resistance pressure can be calculated. This study provides an evaluation method for the spatial variability effect of soil, facilitating a cost-effective design of pile foundation.

Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical engineering. The latter is particularly true for slope stability assessment, where the effects of uncertainty are synthesized in the so-called probability of failure. This probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical viewpoint. In view of this issue, this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional moments. The spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève expansion. Then, failure probabilities are estimated employing maximum entropy distribution with fractional moments. The application of the proposed approach is examined with two examples: a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained slope. The results show that the proposed approach has excellent accuracy and high efficiency, and it can be applied straightforwardly to similar geotechnical engineering problems.

Optimal borehole placement for the design of rectangular shallow foundation systems under undrained soil conditions: A stochastic framework

This contribution proposes a framework to identify optimal borehole configurations for the design of shallow foundation systems under undrained soil conditions. To this end, the minimization of a performance measure defined in terms of the bearing capacity standard deviations is considered. The random failure mechanism method is adopted for random bearing capacity evaluation, thereby enabling explicit treatment of soil spatial variability with tractable numerical efforts. A sampling-based optimization scheme is implemented to account for the non-smooth nature of the resulting objective function. The proposed framework provides non-trivial sensitivity information of the chosen performance measure as a byproduct of the solution process. Further, the method allows assessing the effect of increasing the number of soil soundings into bearing capacity standard deviations. Three cases involving different foundation layouts are studied to illustrate the capabilities of the approach. Numerical results suggest that the herein proposed framework can be potentially adopted as a supportive tool to determine optimal soil sounding strategies for the design of a practical class of civil engineering systems.

Spatial variability of urban forest topsoil properties: towards representative and robust sampling design.

Background: Soil spatial variability is a major concern when deciding how to collect a representative topsoil sample for laboratory analysis. Sampling design to capture site-specific variability is documented in the agricultural literature, but poorly understood for urban forest soils where soils may be characterized by strong horizontal and vertical variability and large temporal anthropogenic disturbances. Methods: This paper evaluates the spatial variability of selected topsoil properties under urban trees to define a statistically robust sampling design that optimizes the number of samples to reliably characterize basal soil respiration (BSR), a property associated with soil health. To provide a reference on variability, two additional soil properties were measured, unrelated to BSR: electrical conductivity (EC) and bulk density (BD). Thirteen sampling sites comprising both park and street trees ( Acer rubrum) were selected in Cambridge, MA, USA. Results: Results indicate street tree topsoil had approximately twice as much variation, requiring more intensive sampling, as did park tree topsoil, even though street trees had smaller soil sampling zones, constricted by tree pits. The variability of BSR was nearly identical to that of EC, and BD results varied least. A large number of samples would be required for acceptable levels of statistical reliability (90% CI - 10% ER) of 44.4, 41.7, and 6.4 for BSR, EC, and BD, respectively, whereas by accepting a lower level of certainty (80% CI - 20% ER) the number of required soil samples was calculated as 6.8, 6.4, and 0.4 for BSR, EC, and BD, respectively. Conclusions: The use of EC testing as a baseline measure to determine spatial variation in the topsoil is proposed, to alleviate the financial implications of more expensive BSR testing. Factors of topsoil disturbance and soil access restrictions at sites with severe root-sidewalk conflicts and the overall generalizability of the results are also discussed.

Predicting subsurface classification in 2D from cone penetration test data

Uncertainty is inevitable in the characterisation of a geotechnical site, especially due to the inherently heterogeneous nature of the ground. In this paper, a method for characterising a subsurface with limited cone penetration test (CPT) data is proposed. The method is based on integrating predictions of CPT parameters with a probabilistic approach for subsoil classification at the CPTs. The predicted stratigraphy is able to capture the spatial variability of soil measured via CPTs and takes account of the uncertainties that arise from transforming CPT measurements into soil units as well as errors in the measurements themselves. The applicability of the proposed method is demonstrated for a site in the Netherlands. The results show that the proposed approach can identify the most likely classification in the domain with good accuracy. Furthermore, the significance of considering the uncertainties in predicting the most likely classification is illustrated via finite element stability analyses of a slope cut-out in the domain.