Soil Water Characteristic Curve Parameters Research Articles

Hydraulic behavior of sandy subgrade under extreme rainfall in Alashan, China

Unsaturated aeolian sand is the main railway/highway sandy subgrade filling material in the desert area of Alashan, Inner Mongolia. In recent years, the extreme climate events frequently happen in Inner Mongolia. Under the extreme rainfall conditions in the desert area, the rainfall infiltration process and the unsaturated state evolution of aeolian sand subgrade is significant to the hydraulic behavior distribution and the deformation development under traffic loading.In this study, the SWCC (soil-water characteristic curve) parameters of aeolian sand was firstly calculated and discussed through van Genuchten model and the linear approach. Then a unified framework was proposed to describe the hydraulic behavior evolution of aeolian sand subgrade under extreme rainfall. Considering the effect of soil properties and rainfall conditions on infiltration process of aeolian sand subgrade, a normalized SWCC model was established. An analytical model of maximum infiltration depth for aeolian sand subgrade affected by extreme rainfall was further developed. Lastly, the numerical simulation results of the maximum infiltration depth for aeolian sand subgrade under an extreme rainfall event verified the correctness of the analytical model. This research can lay a foundation for studying the hydro-mechanical coupling behavior of sandy subgrade in desert areas.

Reliability analysis of compacted embankment with geocomposite under infiltration

Embankment failures can be prevented by introducing geocomposites to act as drains. The effect of the geocomposite layer on the pore pressure distribution and surface displacements of an unsaturated embankment upon infiltration has been studied numerically using deterministic and probabilistic approaches. The inclusion of the geocomposite layer leads to an increase of suction below the interface and a decrease in suction above it by functioning both as a capillary barrier and a drainage layer, thereby reducing the surface displacements upon infiltration. The load in the form of rainfall and the resistance such as suction of the embankment material being variable leads to a variability in the displacements; therefore, reliability analysis has been carried out using hydraulic permeability and soil water characteristic curve (SWCC) parameters as random variables. To assess the probability of failure (Pf), a surrogate model based on augmented radial basis function has been used. Probabilistic analysis revealed that the embankment with geocomposite has less Pf compared to the embankment without geocomposite considering the rainfall infiltration. Moreover, sensitivity analysis predicted that SWCC parameters influence the Pf of embankment containing geosynthetics under infiltration to a larger extent.

Estimation of Hydraulic Parameters from the Soil Water Characteristic Curve

Soil water characteristic curve (SWCC) is one of the most essential hydraulic properties that play fundamental role in various environmental issues and water management. SWCC gives important information for water movement, soil behavior, infiltration, and drainage mechanism, affecting the water circle and the aquifer recharge. Since most of the world’s freshwater withdrawals go for irrigation uses, decoding SWCC is beneficial, as it affects water saving through irrigation planning. Estimation of crucial parameters, such as field capacity (FC) and permanent wilting point (PWP) is the key solution for water saving. Modelling of the SWCC and hydraulic parameters estimation are of great importance, since the laboratory experimental procedures and the experiments in the field are often time-consuming processes. In the present study, the SWCC along with FC and PWP of two soil types were obtained via specific experimental procedures in the laboratory. In order to simulate the SWCC and estimate FC and PWP, the experimental data were approximated with van Genuchten’s model. Results showed that using SWCC to estimate FC gives excellent results, while the method rationally overestimates the PWP. Hence, the presented method leads to estimation of crucial hydraulic parameters that can be used in irrigation planning and water saving practices.

Optimum Design of Unsaturated Finite Clayey Slopes Using Second-Order Reliability Method

This paper discusses the second-order reliability-based design optimization (SORBDO) of unsaturated finite slopes. An efficient search algorithm is proposed to locate the most probable slip surfaces using the Morgenstern–Price method of slices. The variability of fitting parameters of the soil water characteristic curve (SWCC) and shear strength parameters have been taken into account for evaluating the reliability of unsaturated finite slopes. Six unsaturated shear strength (USS) models are considered for the analysis of unsaturated soils. The most conservative model for the optimum design of finite slopes is recommended. Moreover, the results indicate that the mean and standard deviation of the fitting parameters of SWCC have a significant effect on the critical centers, critical slip surfaces, and reliability index of the unsaturated finite slopes. The results demonstrate a significant change in the critical centers associated with critical slip surfaces due to the change in the mean and coefficient of variation of the fitting parameters of SWCC. Statistical analysis of the experimental data shows that a positive correlation exists between fitting parameters of SWCC related to air entry value (af) and residual water content (mf). Negative correlations are found between the fitting parameters of SWCC related to air entry value (af) and slope (nf). Similarly, the SWCC fitting parameters related to residual water content (mf) and slope (nf) have negative correlations for clayey soils. It is observed that the correlated value of the reliability index is overestimated with reference to the uncorrelated value for clayey soil slopes. The proposed design charts are beneficial for engineers and practitioners for the sustainable design of unsaturated finite slopes. Overall, the developed rigorous SORBDO framework offers a more rational approach for assessing critical failure surfaces and critical centers, considering the interdependency of random variables.

Variation Mechanism and Prediction of Soil–Water Characteristic Curve Parameters of Low-Liquid-Limit Silty Clay under Freeze–Thaw Cycles

The soil–water characteristic curve (SWCC) is a key input for the numerical simulation of geotechnical engineering. It contains the basic information needed to describe the mechanical behavior of unsaturated soil. In order to study the variation of the SWCC characteristics and its mechanism after the freeze–thaw (F–T) process of low-liquid-limit silty clay in seasonally frozen regions, the SWCC of the soil samples subjected to zero, one, three, five, and seven F–T cycles at three dry densities were measured; then the microstructure was scanned by scanning electron microscopy (SEM) to analyze the relationship between the characteristic parameters of the SWCC and the microstructure after F–T cycles. Finally, according to the mathematical law of characteristic parameters, the prediction equation for the parameters of the SWCC considering the F–T cycles was established, which has a suitable fitting effect with the experimental data. The result shows that with the increase in F–T cycles, the air-entry value (AEV) and residual saturation (RS) decrease gradually, while the saturated water content and the moisture-losing rate of the transition section slightly increased. According to the microstructure analysis, it is due to the F–T process that the compacted soil sample has cracks, the entire plate structure is destroyed, and thus the arrangement between the soil particles becomes looser, and the porosity and average pore size increase. This study can provide data support and references for the design of low-liquid-limit silty clay foundation engineering in seasonally freezing regions.

Global Mapping of Soil Water Characteristics Parameters— Fusing Curated Data with Machine Learning and Environmental Covariates

Hydrological and climatic modeling of near-surface water and energy fluxes is critically dependent on the availability of soil hydraulic parameters. Key among these parameters is the soil water characteristic curve (SWCC), a function relating soil water content (θ) to matric potential (ψ). The direct measurement of SWCC is laborious, hence, reported values of SWCC are spatially sparse and usually have only a small number of data pairs (θ, ψ) per sample. Pedotransfer function (PTF) models have been used to correlate SWCC with basic soil properties, but evidence suggests that SWCC is also shaped by vegetation-promoted soil structure and climate-modified clay minerals. To capture these effects in their spatial context, a machine learning framework (denoted as Covariate-based GeoTransfer Functions, CoGTFs) was trained using (a) a novel and comprehensive global dataset of SWCC parameters and (b) global maps of environmental covariates and soil properties at 1 km spatial resolution. Two CoGTF models were developed: one model (CoGTF-1) was based on predicted soil covariates because measured soil data are not generally available, and the other (CoGTF-2) used measured soil properties to model SWCC parameters. The spatial cross-validation of CoGTF-1 resulted, for the predicted van Genuchten SWCC parameters, in concordance correlation coefficients (CCC) of 0.321–0.565. To validate the resulting global maps of SWCC parameters and to compare the CoGTF framework to two pedotransfer functions from the literature, the predicted water contents at 0.1 m, 3.3 m, and 150 m matric potential were evaluated. The accuracy metrics for CoGTF were considerably better than PTF-based maps.

Correction to: Evolution of hydro‑mechanical behaviours and its influence on slope stability for a post‑earthquake landslide: implications for prolonged landslide activity

There has been considerable research on the prolonged effects and evolution of post-earthquake landslides, as they are of significance for disaster relief and rebuilding plans in earthquake-impacted regions. There is a need to better quantify these prolonged effects and evolution based on the hydro-mechanical mechanisms of post-earthquake landslides. This study investigated the evolution of the hydro-mechanical behaviour and parameters of a single landslide by integrating experimental and theoretical analyses. Analyses were undertaken using a time series of in situ monitoring data over five hydrological years on a single landslide site in the Baisha River basin of Dujiangyan, in the Wenchuan earthquake region, China. Results indicated a linear increase in the angle of internal friction under prolonged effects, while soil cohesion decreased non-linearly, gradually approaching a lower limit. The saturated hydraulic conductivity ( $${K}_{s}$$ ) increased, and the soil residual water content ( $${\theta }_{r}$$ ) evolution curve conformed to the Boltzmann function distribution model. The soil–water characteristic curve parameters gradually increased. Based on the hydro-mechanical parameter evolution, the spatiotemporal decay of the landslide over the next decade under extreme rainfall conditions was predicted using the rainfall slope stability models and TRIGRS. This paper proposed a mechanistic explanation for the prolonged effects of a post-earthquake landslide in response to rainfall. Furthermore, it provided a reliable theoretical basis for the early warning and prevention of such landslides.

Variability Characterization of SWCC for Clay and Silt and Its Application to Infinite Slope Reliability

A novel statistical framework was developed to quantify the variability of the fitting parameters of the soil–water characteristic curve (SWCC). Reliable estimation of the mean, standard deviation, and associated probability density function (PDF) of the fitting parameters of SWCC is an important tool for addressing reliability-based designs in unsaturated soil mechanics. The assumption of either Gaussian or lognormal distributions may not be valid for representing a high degree of variability associated with fitting parameters. This study aimed to provide the most appropriate continuous PDFs by optimizing the mean and standard deviation such that errors associated with quantile, percentile, and cumulative distribution function (CDF) were as low as possible. A total of 261 and 111 sample points were collected from the most comprehensive experimental works on the fitting parameters of SWCC for clayey and silty soils, respectively. Optimum distributions suitable to the model fitting parameters of SWCC are highly dependent on the type of soil. The most appropriate PDFs for representing the fitting parameters af, nf, and mf of Fredlund and Xing’s model were gamma, Weibull, and inverse Gaussian distributions, respectively. Similarly, fitting parameters af, nf, and mf for silty soils were represented by inverse Gaussian, Gumbel maximum, and Gumbel maximum distributions, respectively. This study found that the selection of inappropriate PDFs overestimated the probability of failure of unsaturated infinite slopes considerably, by 99.49% and 99.76%, respectively, for clayey and silty soils. Recommended mean, coefficient of variation (COV), and PDF are useful in the reliability-based design of unsaturated soil slopes and in judging the performance of existing infinite and finite slopes.

Predicting and Optimizing the Soil–Water Characteristic Curve Parameters with Limited Data using the Performance Guided Jaya Algorithm

The soil water characteristic curve (SWCC) is a fundamental tool for studying the intrinsic mechanical properties of unsaturated soils. The data for SWCCs must be obtained via laboratory tests, which require considerable time and labour. Therefore, in practice, empirical models are often developed with a limited number of laboratory data samples to produce SWCC curves. However, empirical models fitted with limited data often lead to inaccurate modelling of SWCCs. This research aims to propose a data-driven optimization framework to accurately and effectively fit empirical SWCC models with limited amounts of laboratory data. First, the van Genuchten model is adopted as the empirical model to construct the SWCCs. Second, the performance-guided Jaya algorithm (PGJAYA) is introduced to optimize the parameters of the van Genuchten model and increase the fitting accuracy. In the experiments, two cases are investigated, one with limited data and another with abundant data, for four types of soils, i.e., sand, silt, clay and loess. To demonstrate the accuracy and effectiveness of the proposed approach, a comparison with other benchmark optimization algorithms is performed. With limited data, the PGJAYA algorithm outperforms the other optimization algorithms tested. Furthermore, the PGJAYA algorithm produces the smallest fitting error for the SWCCs in the case with full data. Overall, the PGJAYA algorithm is an effective and accurate optimizer with respect to the parameter fitting tasks based on empirical SWCC models.

Estimation of unsaturated hydraulic conductivity function: implication of low to high suction measurements

Establishment of the relationship between soil suction and water content, commonly termed as soil–water characteristic curve (SWCC), is of prime importance in the field of unsaturated soil mechanics. There are several instruments available that can be used to measure the SWCC of soil, but every suction measuring device has its own limitations in terms of its suction measurement range. Therefore, the preciseness of the estimated unsaturated soil properties largely depends on the range of suction measurements and the type of instruments used. The primary objective of this study is to quantify the error that can occur during the estimation of unsaturated hydraulic conductivity function (UHCF) from SWCC for low plastic soils. Experiments were performed to investigate the influence of different suction measurement devices on the estimated UHCF for four different soils with varying clay content. A dew point potentiometer (WP4) and a miniature tensiometer (T5) have been used in this study for the suction measurement. The SWCC of the selected soils were predicted mathematically using a commonly used pedo-transfer function (PTF). The experimental results clearly indicated that the sole use of WP4 overestimated the SWCC parameters, as well as UHCF (overestimation in the conductivity value is in order of 104 times). Rather, a combination of T5 and WP4 data, within their accurate range, provides a more precise estimation of UHCF. Further, the accuracy of the PTF was found very effective for low plastic soils with a relatively low percentage of clay (% clay < 10), in the absence of any experimental data.

Bayesian Updating of Soil–Water Character Curve Parameters Based on the Monitor Data of a Large-Scale Landslide Model Experiment

It is important to determine the soil–water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data was developed to investigate the uncertainty of the SWCC parameters in this study. The objectives of this research were to quantify the uncertainty embedded within the SWCC and determine the critical factors affecting an unsaturated soil landslide under hydrodynamic conditions. For this purpose, a large-scale landslide experiment was conducted, and the monitored water content data were collected. Steady-state seepage analysis was carried out using the finite element method (FEM) to simulate the slope behavior during water level change. In the proposed framework, the parameters of the SWCC model were treated as random variables and parameter uncertainties were evaluated using the Bayesian approach based on the Markov chain Monte Carlo (MCMC) method. Observed data from large-scale landslide experiments were used to calculate the posterior information of SWCC parameters. Then, 95% confidence intervals for the model parameters of the SWCC were derived. The results show that the Bayesian updating method is feasible for the monitoring of data of large-scale landslide model experiments. The establishment of an artificial neural network (ANN) surrogate model in the Bayesian updating process can greatly improve the efficiency of Bayesian model updating.

Soil water characteristic curve test and saturated-unsaturated seepage analysis in Jiangcungou municipal solid waste landfill, China

In this study, an analysis of municipal solid waste (MSW) is undertaken at the Jiangcungou landfill in Xi’an, China. Using waste specimens gathered from the landfill, the soil water characteristic curve (SWCC) is determined at the laboratory, and the effects of landfill age and dry density on the SWCC parameters are analyzed. Leachate level trends during the landfill lifecycle are examined by performing saturated-unsaturated seepage analysis with the consideration of precipitation, as well as the inner source of water. These results are then summarized into three primary points. First, the shallow waste in the Jiangcungou landfill lost its water relatively quickly at small negative pressure heads, while the deep waste lost its water much more gradually. In addition, the deeper waste exhibited a lower volumetric water content when the matrix suction was less than 1.4kPa, but the result was opposite when the matrix suction was more than 2.0kPa. Second, the saturated volumetric water content and steepness of the SWCC decreased as the dry density increased, and the residual volumetric water contents increased and the inverse of the air-entry pressure decreased with increasing landfill age. Third, layers three, five and six demonstrated the highest leachate levels in the eighth landfill stage especially layer five, and these merit the greatest attention in monitoring and construction. These findings may provide a basis for the management of the Jiangcungou landfill.

Spatial variability of the parameters of soil-water characteristic curves in gravel-mulched fields

Abstract Knowledge of the soil-water characteristic curve (SWCC) and its spatial variability is essential for many agricultural, environmental, and engineering applications. We analyzed the spatial variability of the parameters of SWCC in gravel-mulched fields using classical statistics and geostatistical methods. Soil samples were collected from the layer in 64 evenly distributed 1 × 1 m quadrats 4 m apart, center to center. SWCC in the gravel-mulched fields could be fitted well by both the van Genuchten and Brooks–Corey models, but the fit was better with the van Genuchten model. The type of fitting three parameters was tested. The model parameters θs and n of each type of soil were weakly variable, and α was moderately variable. The results indicate that the gravel-mulched field has better water retention, and the water retention effect of the new gravel-mulched fields is most obvious. The spatial variation of the parameters in SWCC can therefore be used to infer soil hydraulic properties, which is important for simplifying the calculation of SWCC and quantitatively determining the retention of soil water and for managing the capacity of soil to retain water in gravel-mulched fields in arid regions.

Rapid estimation of resilient modulus of subgrade soils using performance-related soil properties

ABSTRACT This study aims at developing an efficient and accurate methodology to estimate the resilient modulus of subgrade soils. First, a new resilient modulus model incorporating stress dependence and moisture dependence was proposed. Second, prediction models were developed to conveniently and accurately determine model parameters of SWCC and resilient modulus model. In order to characterise the moisture dependence of subgrade soils, the matric suction was added into the proposed model. The matric suction was measured by the pressure plate test and the soil-water characteristic curve (SWCC) was used to determine the matric suction value at any given moisture contents. In order to develop prediction models for model parameters of SWCC and resilient modulus model, the laboratory experiments and multiple regression analysis were conducted on 22 soil samples. A series of performance-related soil properties were measured and used to develop the coefficients prediction models. The developed coefficients prediction models using the performance-related soil properties have high R-squared values and were validated by comparing the measured and predicted values of resilient modulus. Therefore, when the basic physical properties of soils were obtained, the resilient modulus can be predicted for the subgrade soils at any given matric suctions and stress states.

Reliability analysis of unsaturated soil slope stability under infiltration considering hydraulic and shear strength parameters

In general, soil properties, including shear strength and hydraulic parameters, are characterised as a spatial variability. This paper aims to investigate the effect of spatial variability of the soil properties on slope stability during rainfall infiltration. The effective friction angle, saturated hydraulic conductivity, and soil water characteristic curve parameters of sand are simulated using random field theory. A seepage analysis is conducted using the random finite element method to obtain pore water pressure distribution. A stability analysis is performed to show the variation of safety factors and failure probability. The results show that the random field of the soil-water characteristic curve produces a significant variation of pore water pressure, while the random field of the effective friction angle is the most important parameter for probabilistic stability analysis.

Expedited Soil–Water Characteristic Curve Tests Using Combined Centrifuge and Chilled Mirror Techniques

Abstract A soil–water characteristic curve (SWCC) shows soil’s ability to provide water availability and the rate that this water can be transmitted and replenished. However, the current commonly used method to determine the SWCCs of soils (the axis-translation method) is time consuming. This study combines two relatively new but established methods (centrifuge and chilled mirror psychrometer) to find a rapid and reliable way to obtain the SWCC for a large range of suctions. For comparison, the SWCC was also independently measured using the axis-translation method (Tempe cell and pressure plate). The comparisons show that the SWCC parameters (air-entry values, residual suctions, and slope of SWCC) determined using the combined centrifuge and chilled mirror psychrometer methods agree well with the same SWCC parameters determined using the Tempe cell and pressure plate methods (axis-translation method). At the same time, the time taken was cut down from a few months for the axis-translation method to about two days for the combined centrifuge and chilled mirror psychrometer.

Uncertainty of Soil Water Characteristic Curve Measurements Using an Automated Evaporation Technique

Core Ideas Replicate‐specimen measurement of SWCCs was performed with an automated technique. Inter‐specimen uncertainty for SWCC parameters was quantified. A comparison of automated evaporation technique vs the standard hanging column is presented. Soil water characteristic curve (SWCC) measurements are used to estimate hydrologic parameters for a variety of engineering and agricultural applications. The SWCC parameterization is often based on a single measurement or with little or no consideration and/or quantification of the uncertainty in collection of SWCC data. Uncertainty in the SWCC data stems from random variability in pore geometry associated with irregular granular media. Additionally, a substantial opportunity exists in SWCC measurements for systematic or experimental errors that can increase uncertainty. This study utilized an evaporation method for measuring 10 SWCCs for each of four different test soils. One low‐plasticity and three high‐plasticity silty sand soils were sampled from the Sagehen Creek Experimental Watershed in Sierra County, California. The uncertainty in the SWCC measurements was quantified by the distribution and variability of van Genuchten parameters evaluated via nonlinear regressions on each set of SWCC data. Despite a careful, replicated specimen preparation, variability in the measured SWCC data was indicated by variability of estimates of van Genuchten's fitting parameters α and n and residual volumetric water content θr, suggesting that packing specimens to identical dry densities does not produce identical SWCC measurements for a single soil type. For the soils tested in this study, replicate specimen testing resulted in uncertainty in α between ×/1.2 and ×/1.5 the geometric mean estimate for α, between ±2 and ±6% of the mean estimate for n, and between ±2 and ±6% for θr. Additional testing is required before applying these uncertainties generally to soils of different textural classifications.

Influence of Confining Pressure on Water Retention Characteristics of Compacted Soil

Sustainability of earth structures is one of the major concerns in the field of geotechnical engineering. Recurring evaluation and maintenance related to earth structures can often lead to faulty decisions due to errors in analysis. A prominent such concern is related to compacted soils in earth structures which are unsaturated in nature and subjected to stress variations in field. Under the effect of stress state, the key parameters to assess the strength and deformation behavior of unsaturated soils might undergo various changes. In general, key parameters are obtained from soil water characteristic curve (SWCC). In the present study, an effort has been made to understand the influence of confining pressure on the SWCC of compacted soil through experimental and analytical studies. The SWCC of compacted soil at zero net confinement was obtained using tensiometers (HYPROP) and dew point potentiometer (WP4C) while for various net confining pressures, modified triaxial apparatus (using axis translation technique) is used. The measured soil suction-water content data was fitted with five most widely used SWCC models available in literature. The key parameters of SWCC fitted with least square regression technique was compared for all the five models to assess the effectiveness of the models in predicting the SWCC correctly over the entire suction range. Results indicate that there is a disparity in the obtained key parameters from various SWCC models. The influence of stress state on the SWCC obtained experimentally is compared with the analytical solution accounting the influence of stress state.

Using adaptive neuro-fuzzy inference system for modeling unsaturated soils shear strength

Determining shear strength of unsaturated soils is generally more complex, time-consuming, and costly than that of saturated soils. In spite of numerous studies conducted on unsaturated test methods, there is still a critical need for the existing studies to be applied while extending the researches on the subject matter of shear strength determination in unsaturated soils. This is primarily in order to save time and money spent on laboratory tests. For this purpose, several empirical approaches to unsaturated soils shear strength testing have been proposed by different researchers. Being based on shear strength and soil matric suction parameters, these empirical methods can be divided into linear and nonlinear variants. Nonlinear empirical methods are usually based on saturated soil shear strength parameters and soil–water characteristic curve (SWCC). However, it is still a time-consuming and costly practice to determine SWCC parameters in the laboratory. As such, several correlations are proposed to estimate SWCC parameters; some of these correlations are, however, based on fitting parameters, and this is again a relatively difficult task to undertake. Recently, artificial intelligence has led to applications in solving nonlinear problems and cases where an accurate understanding of the problem is required. In this research, aiming at proposing adaptive neuro-fuzzy inference system (ANFIS) models to predict unsaturated soils shear strength, two clustering approaches are followed, namely fuzzy c-mean clustering and subtractive clustering. ANFIS input parameters were taken to be the same parameters commonly chosen in most empirical models utilized for determining shear strength; these include net normal stress ( $$\sigma _{\mathrm{n}}-u_{\mathrm{a}})$$ , matric suction ( $$\sigma _{\mathrm{a}}-u_{\mathrm{w}})$$ , effective cohesion ( $$c^\prime $$ ), and angle of frictional resistance ( $$\varphi '^\prime $$ ), and also other especial parameters which were taken in empirical models such as Lamborn (A micromechanical approach to modeling partly saturated soils, Texas A&M University, College Station, 1986), Vanapalli et al. (Can Geotech J 33:379–392, 1996), Oberg and Sallfors (ASTM Geotech Test J 20:40–48, 1997), Bao et al. (Keynote lecture, proceedings of the 2nd international conference on unsaturated soils (UNSAT 98), Beijing, China, pp 71–98, 1998) and Khalili and Khabbaz (Geotechnique 48:681–687, 1998) models collected from SoilVision database. Continuing with the research, a couple of comparisons were made on five empirical models along with the two ANFIS models. The results indicated that both ANFIS models were of high capability in terms of shear strength prediction using the chosen inputs so that more satisfying and adequate predictions be provided compared to empirical models results.

Probabilistic model of unsaturated slope stability considering the uncertainties of soil-water characteristic curve

Many soil slopes are unsaturated and failure of them can be a major cause of damage to structures. Apart from soil properties, the Soil-Water Characteristic Curve (SWCC) is the backbone of any unsaturated slope analysis. Uncertainties of these effective parameters of unsaturated slopes cause the probabilistic analysis to be more realistic rather than deterministic. In this research, the stochastic analysis of unsaturated slope stability is carried out based on simplified Bishop’s method. The stochastic parameters are the input parameters of SWCC in addition to effective internal angle of friction, effective cohesion and unit weight of soil. Based on the collected results from hundreds of stochastic analyses, the probability of failure is presented as a three dimensional surface. Finally, probabilistic model is developed to model this surface and evaluate the probability of failure as function of safety factor and its correlation of variation.