Cohesive Slopes Research Articles

Seismic-induced surficial failure of cohesive slopes using three-dimensional limit analysis: A case study of the Wangjiayan landslide in Beichuan, China

Seismic-induced surficial failure of cohesive slopes using three-dimensional limit analysis: A case study of the Wangjiayan landslide in Beichuan, China

Effect of the pile diameter and slope on the undrained lateral response of the Large–Diameter pile

A new p–y curve of the lateral static loading of large diameter piles in cohesive slopes is proposed in this paper, which considers both the size effect and slope inclination. A series of pile size and slope inclination varied three–dimensional finite difference analyses are used to study the behavior of large–diameter pile foundations under lateral loading on cohesive sloping ground. Based on the results of numerical calculations, the pile–soil failure mechanism is discussed for large–diameter piles. Combined with the failure mechanism, nonlinear analytical formulations are derived for the ultimate lateral load and initial stiffness of hyperbolic p–y curves at different depths, which considers both the size effect and slope inclination. The proposed p–y curve is compiled into a program for parametric analysis of pile displacement and bending moment. To verify the proposed p–y curve, it is compared with large–scale foundation lateral loading field test and centrifuge test data to obtain a remarkable result.

Slope reliability analysis using Bayesian optimized convolutional neural networks

PurposeThis paper aims to develop an effective framework which combines Bayesian optimized convolutional neural networks (BOCNN) with Monte Carlo simulation for slope reliability analysis.Design/methodology/approachThe Bayesian optimization technique is firstly used to find the optimal structure of CNN based on the empirical CNN model established in a trial and error manner. The proposed methodology is illustrated through a two-layered soil slope and a cohesive slope with spatially variable soils at different scales of fluctuation.FindingsThe size of training data suite, T, has a significant influence on the performance of trained CNN. In general, a trained CNN with larger T tends to have higher coefficient of determination (R2) and smaller root mean square error (RMSE). The artificial neural networks (ANN) and response surface method (RSM) can provide comparable results to CNN models for the slope reliability where only two random variables are involved whereas a significant discrepancy between the slope failure probability (Pf) by RSM and that predicted by CNN has been observed for slope with spatially variable soils. The RSM cannot fully capture the complicated relationship between the factor of safety (FS) and spatially variable soils in an effective and efficient manner. The trained CNN at a smaller the scale of fluctuation (λ) exhibits a fairly good performance in predicting the Pf for spatially variable soils at higher λ with a maximum percentage error not more than 10%. The BOCNN has a larger R2 and a smaller RMSE than empirical CNN and it can provide results fairly equivalent to a direct Monte Carlo Simulation and therefore serves a promising tool for slope reliability analysis within spatially variable soils.Practical implicationsA geotechnical engineer could use the proposed method to perform slope reliability analysis.Originality/valueSlope reliability can be efficiently and accurately analyzed by the proposed framework.

Efficient reliability‐based design of slope angles in spatially variable soils with field data

AbstractDeterministic single factor of safety method cannot explicitly account for the influences of various sources of uncertainties (e.g., spatial variability of geomaterials, measurement and transformation uncertainties) in stability design of slopes. Many probabilistic methods have been applied to the reliability‐based design (RBD) of slopes, but they typically require performing numerous deterministic slope stability analyses. In this paper, an efficient reliability‐based design method for spatially varying slopes based on field data is proposed. Here, the RBD of a slope angle is concerned. Reliability‐based design is implemented using an inverse First Order Reliability Method (inverse FORM ‐ IFORM). A sandy slope and a cohesive slope are investigated as examples, respectively, to illustrate the proposed method. The results indicate that the proposed method can quickly obtain rational design schemes of slope angles accounting for the spatial variability of soil properties, measurement and transformation uncertainties based on the field data. It can act as a practical and effective tool for the RBD of slopes in two‐dimensional spatially variable soils. Additionally, it is found that the random field mesh size affects the RBD results significantly, while the probability distribution and horizontal autocorrelation of soil parameters have slight influences on the RBD results.

Comparative Study on Reliability Analysis of Cohesive Soil Slope using Subset Simulation and Other Methods

In geotechnical engineering, uncertainties arise due to variation in loads, soil characteristics, ground stratification and so on. Reliability analysis based on probabilistic approaches is particularly suitable to deal with such uncertainties. In this paper, a newly developed reliability analysis method, namely Subset simulation (SS) method has been implemented to study the stability of a cohesive slope. The results of reliability analysis obtained from SS method are also compared with three other methods, namely First order second moment method (FOSM), First order reliability method (FORM) and Direct Monte-Carlo simulation (MCS) method. The various reliability models have been used in a spreadsheet environment using MS-Excel. The developed spreadsheet-based platform implementing all four methods contains two common models i.e. deterministic model and the uncertainty model. The SS method uses another model called uncertainty propagation using subset simulation (UPSS) in addition to the two above-mentioned models. The factor of safety of the slope is determined using ordinary method of slices under undrained condition. The probability of failure ( and its corresponding reliability index of the proposed slope has been determined using all four methods. A software called Geo-Studio (SLOPE/W) has been used to tally the results of reliability analysis of the slope considered herein. The results obtained from the different methods show that the SS method gives better performance in terms of efficiency and resolution especially at low failure probability i.e., . Also, the SS method helps in identifying the significant depth where the most probable critical slip surface is located.

Mass transport deposits in reflection seismic data offshore Oregon, USA

AbstractSubmarine landslides associated with the Cascadia subduction zone offshore of the Pacific northwest United States and Canada represent significant natural geohazards. Mapping past submarine landslide deposits is critical for understanding present and future slope failure and tsunami hazard potential. We focus on the portion of Cascadia offshore Oregon to map the occurrences of submarine landslide deposits (mass transport deposits [MTDs]) in the subsurface using recent high‐resolution reflection seismic data. We identified 133 MTDs and categorized them based on their present morphology inferred from their acoustic characteristics as disintegrative or blocky. Interestingly, nearly 76% of the MTDs are located in the northern Oregon margin and many of these are non‐cohesive disintegrative deposits. MTDs are less common in the southern Oregon margin, however, they were also much larger and more cohesive than those found in the north. The differences are not likely to be related to differences in earthquake intensity but rather sedimentation rates and basin structures. Specifically, the northern Oregon margin is proximal to the sediment‐delivery systems of the Columbia River and has landward verging fold‐and‐thrust structures, whereas the southern Oregon margin is relatively sediment starved and has seaward verging structures resulting in fewer steep ridges. Because of the higher sedimentation rates, the northern Oregon margin may be prone to more frequent and disintegrative types of slope failures. In contrast, the southern margin may have enhanced slope stability due to seismic strengthening and lower sedimentation rates. However, when slope failures do occur in the southern Oregon margin, they tend to be more cohesive and blocky. Therefore, even though there are fewer slope failures in the southern Oregon margin, there is still hazard potential because fast‐moving cohesive slope failures can generate tsunami.

System failure probability analysis of cohesive slope considering the spatial variability of undrained shear strength

System failure probability analysis of cohesive slope considering the spatial variability of undrained shear strength

Reliability analysis of a cohesive slope in a spreadsheet using FOSM, FORM and Monte-Carlo Simulation

In geotechnical engineering uncertainties arises from loads, soil characteristics and their properties, calculation models etc. To minimize these uncertainties in geotechnical problem, various reliability based and (or probabilistic based) approaches have been developed. This abstract presents a MS-Excel spreadsheet environment based practical framework for estimating the reliability index and failure probability of a cohesive finite slope using First-order second moment method, First-order reliability method and Monte Carlo Simulation. The height and the angle of slope is considered to be 5 m and 45º respectively. The inclination of the slope is 2H:1V and the hard stratum is assumed to be present at 15 m below the soil. The values of saturated unit weight and undrained shear strength are assumed to be 18 kN/m3 and 20 kPa respectively. The stability analysis is carried out using Swedish slice method under undrained condition. The excel spreadsheet developed in the study is mainly divided into two forms i.e. deterministic model worksheet for calculating the factor of safety and uncertainty model for generating the random variables of uncertain parameters. The undrained shear strength is considered as an uncertain parameter. The nominal factor of safety value is found out to be 1.248 and the critical slip circle has coordinate (2.6, 8.8), having radius of 16 m. The reliability index is found out using FOSM, FORM and MCS.

Spatial–Temporal Variation of Soil Sliding Probability in Cohesive Slopes with Spatially Variable Soils

Abstract Landslide is a common and severe geohazard that may pose significant risk to adjacent structures such as buildings and buried pipelines. Identification of the failure slip surface is criti...

Parallel Process Latent Growth Modeling of the Relationships Between Social Cohesion and Mental Health in Later Life

Previous literature suggests that social factors (e.g., social cohesion, social support) are protective predictors of mental health problems. However, there might be a reciprocal relationship between social factors and mental health and the relationship changes over time. Therefore, this study examined the longitudinal relationship between community social cohesion and mental health using a latent growth curve model with 8 waves of the National Health and Aging Trends Study (NHATS; 2011-2018), a nationally representative panel study of Medicare beneficiaries in the United States. Social cohesion measured the perceived level of mutual trust by three items (score range: 0-6) and mental health was measured by PHQ-4 (score range: 0-12). The final model including covariates (age, gender, functional disabilities) fit the data well: χ2=1036.383, p<.001; RMSEA=.037; CFI=.960; and SRMR=.070. Initial level of social cohesion was negatively associated with initial level of mental health problem (β=−.23, p< .001), suggesting that higher levels of social cohesion was associated with lower levels of mental health problems. The covariance between social cohesion slope and mental health slope was significant (β=−.16, p< .01), suggesting an increase in social cohesion was associated with a decrease in mental health problems over time. Functional disabilities significantly influenced mental health over time, while functional disabilities did not influence social cohesion consistently. This study adds to the growing literature on the ways mental health status and social connection have reciprocal relationships over time. Therefore, mental health status in later life could be decreased by improving social cohesion and connectedness with the community.

Nonlinear Shear-Strength Reduction Technique for Stability Analysis of Uniform Cohesive Slopes with a General Nonlinear Failure Criterion

Abstract This paper develops a nonlinear shear-strength reduction technique for analyzing the stability of uniform slopes with the general nonlinear failure criterion. To incorporate the nonlinear ...

The upper bound shakedown analysis of strip footing on cohesive slopes under repeated vertical–horizontal loads

Many foundations, such as those of industrial equipment, offshore structures and wind turbines, have to operate under repeated loads. Shakedown analysis is a powerful method that yields a straightforward calculation of a load limit below which the foundation will be stable under repeated loading. In this article, using the upper bound shakedown theorem by means of displacement finite element method and nonlinear programming, the shakedown limit of strip footing on cohesive slopes under simultaneous horizontal and vertical repeated loads is investigated. The results are presented in the form of dimensionless charts. The effect of the cohesion and unit weight of the soil, the distance of the footing from the crest of the slope, and the angle of the slope, on the shakedown failure envelope, is studied.

Comparative study of material point method and upper bound limit analysis in slope stability analysis

Abstract The upper bound limit analysis (UBLA) is one of the key research directions in geotechnical engineering and is widely used in engineering practice. UBLA assumes that the slip surface with the minimum factor of safety (FSmin) is the critical slip surface, and then applies it to slope stability analysis. However, the hypothesis of UBLA has not been systematically verified, which may be due to the fact that the traditional numerical method is difficult to simulate the large deformation. In this study, in order to systematically verify the assumption of UBLA, material point method (MPM), which is suitable to simulate the large deformation of continuous media, is used to simulate the whole process of the slope failure, including the large-scale transportation and deposition of soil mass after slope failure. And a series of comparative studies are conducted on the stability of cohesive slopes using UBLA and MPM. The proposed study indicated that the slope angle, internal friction angle and cohesion have a remarkable effect on the slip surface of the cohesive slope. Also, for stable slopes, the calculation results of the two are relatively close. However, for unstable slopes, the slider volume determined by the UBLA is much smaller than the slider volume determined by the MPM. In other words, for unstable slopes, the critical slip surface of UBLA is very different from the slip surface when the slope failure occurs, and when the UBLA is applied to the stability analysis of unstable slope, it will lead to extremely unfavorable results.

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (Fy) and undrained shear strength of the cohesive soil (Cu) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R2) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

Probabilistic bearing capacity of strip footing on reinforced anisotropic soil slope

The probabilistic bearing capacity of a strip footing placed on the edge of a purely cohesive reinforced soil slope is computed by combining lower bound finite element limit analysis technique with random field method and Monte Carlo simulation technique. To simulate actual field condition, anisotropic random field model of undrained soil shear strength is generated by using the Cholesky-Decomposition method. With the inclusion of a single layer of reinforcement, dimensionless bearing capacity factor, N always increases in both deterministic and probabilistic analysis. As the coefficient of variation of the undrained soil shear strength increases, the mean N value in both unreinforced and reinforced slopes reduces for particular values of correlation length in horizontal and vertical directions. For smaller correlation lengths, the mean N value of unreinforced and reinforced slopes is always lower than the deterministic solutions. However, with the increment in the correlation lengths, this difference reduces and at a higher correlation length, both the deterministic and probabilistic mean values become almost equal. Providing reinforcement under footing subjected to eccentric load is found to be an efficient solution. However, both the deterministic and probabilistic bearing capacity for unreinforced and reinforced slopes reduces with the consideration of loading eccentricity.

Static and seismic stability assessment of 3D slopes with cracks

The majority of extant analyses on slope stability assumed a two-dimensional plane-strain condition and/or an intact slope. However, slope failures typically exhibit three-dimensional (3D) characteristics and cracks are commonly present in cohesive slopes. This study develops a framework based on the kinematic limit-analysis method for static and seismic stability evaluation of 3D slopes with cracks. A pseudo-static approach is adopted to represent seismic effects. Two types of cracks are considered: cracks prior to slope failure (open cracks) and cracks forming contemporaneously with slope failure (formation cracks). A 3D horn-like collapse mechanism including a vertical crack is used to represent the slope collapse. A comparison with previous studies is conducted to validate the proposed approach. A series of stability charts is presented for the ease of direct use. Results show that consideration of seismic effects and existence of cracks lead to a significant reduction in slope stability, whereas the inclusion of 3D effects yields a greater safety factor. The influence of cracks becomes more significant for slopes with greater inclination angles and higher levels of soil cohesion. This study considers a more realistic situation, the results of which can serve as a reference tool in the seismic design of slopes.

Cohesive slope failure analysis using methods combining smoothed particle hydrodynamics and response surface function

Purpose The paper aims to construct a method to simulate the relationship between the parameters of soil properties and the area of sliding mass of the true slip surface of a landslide. Design/methodology/approach The smoothed particle hydrodynamics (SPH) algorithm is used to calibrate a response surface function which is adopted to quantify the area of sliding mass of the true slip surface for each failure sample in Monte Carlo simulation. The proposed method is illustrated through a homogeneous and a heterogeneous cohesive soil slope. Findings The comparison of the results between the proposed method and the traditional method using the slip surface with minimum factor of safety (FSmin) to quantify the failure consequence has shown that the landslide risk tends to be attributed to a variety of risk sources, and that the use of a slip surface with FSmin to quantify the consequence of a landslide underestimates the landslide risk value. The difference of the risk value between the proposed method and the traditional method increases dramatically as the uncertainty of soil properties becomes significant. Practical implications A geotechnical engineer could use the proposed method to perform slope failure analysis. Originality/value The failure consequence of a landslide can be rationally predicted using the proposed method.

The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes

In this paper, a neuro particle-based optimization of the artificial neural network (ANN) is investigated for slope stability calculation. The results are also compared to another artificial intelligence technique of a conventional ANN and adaptive neuro-fuzzy inference system (ANFIS) training solutions. The database used with 504 training datasets (e.g., a range of 80%) and testing dataset consists of 126 items (e.g., 20% of the whole dataset). Moreover, variables of the ANN method (for example, nodes number for each hidden layer) and the algorithm of PSO-like swarm size and inertia weight are improved by utilizing a total of 28 (i.e., for the PSO-ANN) trial and error approaches. The key properties were fed as input, which were utilized via the analysis of OptumG2 finite element modelling (FEM), containing undrained cohesion stability of the baseline soil (Cu), angle of the original slope (β), and setback distance ratio (b/B) where the target is selected factor of safety. The estimated data for datasets of ANN, ANFIS, and PSO-ANN models were examined based on three determined statistical indexes. Namely, root mean square error (RMSE) and the coefficient of determination (R2). After accomplishing the analysis of sensitivity, considering 72 trials and errors of the neurons number, the optimized architecture of 4 × 6 × 1 was determined to the structure of the ANN model. As an outcome, the employed methods presented excellent efficiency, but based on the ranking method, the PSO-ANN approach might have slightly better efficiency in comparison to the algorithms of ANN and ANFIS. According to statistics, for the proper structure of PSO-ANN, the indexes of R2 and RMSE values of 0.9996, and 0.0123, as well as 0.9994 and 0.0157, were calculated for the training and testing networks. Nevertheless, having the ANN model with six neurons for each hidden layer was formulized for further practical use. This study demonstrates the efficiency of the proposed neuro model of PSO-ANN in estimating the factor of safety compared to other conventional techniques.

The performance of six neural-evolutionary classification techniques combined with multi-layer perception in two-layered cohesive slope stability analysis and failure recognition

Six population-based hybrid algorithms are applied to train the multilayer perceptron (MLP) to improve the classification accuracy, in the stability assessment. A complex problem of slope stability against failure is designed in Optum G2 software. Considering four key factors of shear strength of clayey soil, slope angle, the ratio of foundation distance from the slope to the foundation length, and the applied surcharge, the stability or failure of the proposed slope are anticipated. The provided data are used to develop the MLP combined with biogeography-based optimization (BBO), ant colony optimization (ACO), genetic algorithm (GA), evolutionary strategy (ES), particle swarm optimization (PSO) and probability-based incremental learning (PBIL). The results revealed that the BBO-MLP with the obtained area under the receiving operating characteristic curve (AUROC) of 0.995 and the classification ratio (CR) of 92.4% is the most accurate model followed by GA-MLP (AUROC = 0.960 and CR = 84.3%), PBIL-MLP (AUROC = 0.948 and CR = 79.3%), ES-MLP (AUROC = 0.879 and CR = 65.7%), PSO-MLP (AUROC = 0.878 and CR = 71.3%), and ACO-MLP (AUROC = 0.798 and CR = 60.7%).

On the need for dependence characterization in random fields: Findings from cone penetration test (CPT) data

Random field theory is widely used to model spatial variability of soil properties. However, random field modeling focuses mainly on the estimate of spatial correlation structure. The dependence structure that is necessary to construct the joint probability distribution over a random field is usually not characterized. The aim of this research is twofold. First, this paper focuses on characterizing the dependence structure underlying a random field based on cone penetration test (CPT) data. The copula approach is adopted to represent dependencies and the best-fit dependence (copulas) are identified from the CPT data. It is found that the nonGaussian dependencies can be a real phenomenon in spatial fluctuation of the soil shear strength parameter. Second, this paper provides formulations for generating random fields with Gaussian or nonGaussian dependencies, and investigates whether the improper use of the dependence structure could lead to significant bias in failure probability. The generated one-dimensional (1-D) and two-dimensional (2-D) random fields of a cohesive slope under different dependencies are compared. Large deviation in probabilistic results implies that the effect of dependencies on failure probability can be nontrivial. Therefore, the complete random field characterization should involve the estimate of both correlation structure and dependence structure.