Risk assessment of slope failure by representative slip surfaces and response surface function

2015 ◽  
Vol 20 (5) ◽  
pp. 1783-1792 ◽  
Author(s):  
Liang Li ◽  
Xuesong Chu
Keyword(s):  
Risk Assessment ◽  
Response Surface ◽  
Slope Failure ◽  
Slip Surfaces ◽  
Response Surface Function

scholarly journals SLOPE FAILURE RISK ASSESSMENT MODELING USING TOPOGRAPHIC DATA AND NUMERICAL CALCULATION OF SOIL CONSERVATION BY TREE ROOT SYSTEMS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 217-222
Author(s):  
T. Mori ◽  
T. Sugiyama ◽  
I. Hosooka ◽  
M. Nakata ◽  
K. Okano ◽  
...  
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Slope Failure ◽  
Forest Type ◽  
Heavy Rain ◽  
Root Systems ◽  
Topographic Data ◽  
Relative Risk Assessment ◽  
External Forces ◽  
Heavy Rains

<p><strong>Abstract.</strong> In Japan, the frequency of sudden heavy rain events has recently increased, causing slope failures that in turn increase rates of damage to transit infrastructure such as railways and roads. To reduce this damage, there is a need to identify locations near railroad tracks that are at risk of slope failure. Thus, an assessment that predicts whether or not damage will occur due to external forces such as heavy rains is required, rather than a simple relative risk assessment based on identifying locations similar to those damaged in previous events. In this study, we developed a method for time series stability assessment of slopes during heavy rains using digital topographic data. This method uses topographic data to estimate topsoil thickness, which contributes to stability, and soil strength, which is affected by the root systems of vegetation on slopes. Using differences in these parameters between tree species and forest type, we were able to calculate slope stability and simulate slope failure during rainfall. The simulations allowed us to evaluate locations along routes where previous failures occurred, and to identify at-risk locations that have not yet experienced slope failure. This approach will improve forest management based on risk assessments for intensifying heavy rains.</p>

Simulation of risk of progressive slope failure

1992 ◽  
Vol 29 (1) ◽  
pp. 94-102 ◽  
Author(s):  
R. N. Chowdhury
Keyword(s):  
Shear Strength ◽  
Slope Failure ◽  
Direct Evidence ◽  
Slip Surface ◽  
Progressive Failure ◽  
Residual Shear Strength ◽  
Slip Surfaces ◽  
Surface Measurements ◽  
Risk Simulation ◽  
Deterministic And Probabilistic Approaches

Understanding of progressive failure of slopes is of immense interest to geotechnical engineers and others concerned with the occurrence of landslides. One important aspect of research is the development of relevant analytical and numerical techniques. Both deterministic and probabilistic approaches can be used to study the development of progressive failure, provided valid geomechanics models form the basis of such studies. In this paper the risk of failure is simulated within a probabilistic framework. Of particular interest is the increase in the probability of failure, as overstress (and consequent localized failure) is considered to have actually occurred over an increasing proportion of a slip surface within the slope. The perception or interpretation of local failure is often based on observational data from surface measurements and subsurface instrumentation. Knowledge of spatial progression of failure may similarly be based on indirect and direct evidence. In the proposed simulation process the peak and residual shear strength of the slope material are regarded as one-dimensional random fields, and therefore spatial variability of each parameter is taken into consideration. Key words : analysis, clays, failure, shear strength, slopes, stability, landslides, probabilistic analysis, reliability analysis, progressive failure, slip surfaces, risk simulation, statistical analysis.

scholarly journals Kinematic reliability and sensitivity analysis of the modified Delta parallel mechanism

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141875910 ◽  
Author(s):  
Dongtao Xu
Keyword(s):  
Sensitivity Analysis ◽  
Response Surface ◽  
Parallel Mechanism ◽  
Functional Relation ◽  
Structural Response ◽  
Quadratic Function ◽  
Reliability Sensitivity ◽  
Set Up ◽  
Response Surface Function ◽  
Input Error

In order to improve the kinematic reliability, it is crucial to find out the influence of each error source on the kinematic reliability of the mechanism. Reliability sensitivity analysis is used to find the changing rate in the probability of reliability in relation to the changes in distribution parameters. Based on the structural response surface function method, the functional relation between the kinematic reliability of a modified Delta parallel mechanism and the original input-error vectors is described using the quadratic function with cross terms. Moreover, the partial derivatives of the functional relation with respect to the means and variances of the original input errors are derived, which can efficiently evaluate kinematic reliability sensitivity of the mechanism. The advantages of this method are as follows: First, the response surface function, which can be easily set up by the position-error model of the mechanism, is convenient for calculating the variance, partial derivative, and reliability sensitivity. Second, in this case (unlike in the traditional error-mapping model), although the input-error values are unknown, pseudorandom variables used as random input-error sources can be generated by MATLAB software. Furthermore, the kinematic reliability of the mechanism can be assessed using the Monte Carlo method.

scholarly journals A strategy for GIS-based 3-D slope stability modelling over large areas

2014 ◽  
Vol 7 (4) ◽  
pp. 5407-5445 ◽  
Author(s):  
M. Mergili ◽  
I. Marchesini ◽  
M. Alvioli ◽  
M. Metz ◽  
B. Schneider-Muntau ◽  
...  
Keyword(s):  
Slope Stability ◽  
Landslide Susceptibility ◽  
Slope Failure ◽  
Computing Time ◽  
Central Italy ◽  
Shallow Landslides ◽  
Landslide Susceptibility Mapping ◽  
Deterministic Models ◽  
Average Value ◽  
Slip Surfaces

Abstract. GIS-based deterministic models may be used for landslide susceptibility mapping over large areas. However, such efforts require specific strategies to (i) keep computing time at an acceptable level, and (ii) parameterize the geotechnical data. We test and optimize the performance of the GIS-based, 3-D slope stability model r.slope.stability in terms of computing time and model results. The model was developed as a C- and Python-based raster module of the open source software GRASS GIS and considers the 3-D geometry of the sliding surface. It calculates the factor of safety (FoS) and the probability of slope failure (Pf) for a number of randomly selected potential slip surfaces, ellipsoidal or truncated in shape. Model input consists of a DEM, ranges of geotechnical parameter values derived from laboratory tests, and a range of possible soil depths estimated in the field. Probability density functions are exploited to assign Pf to each ellipsoid. The model calculates for each pixel multiple values of FoS and Pf corresponding to different sliding surfaces. The minimum value of FoS and the maximum value of Pf for each pixel give an estimate of the landslide susceptibility in the study area. Optionally, r.slope.stability is able to split the study area into a defined number of tiles, allowing parallel processing of the model on the given area. Focusing on shallow landslides, we show how multi-core processing allows to reduce computing times by a factor larger than 20 in the study area. We further demonstrate how the number of random slip surfaces and the sampling of parameters influence the average value of Pf and the capacity of r.slope.stability to predict the observed patterns of shallow landslides in the 89.5 km2 Collazzone area in Umbria, central Italy.

Fuzzy optimization design-based multi-level response surface of bogie frame

2019 ◽  
Vol 10 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Pengpeng Zhi ◽  
Yonghua Li ◽  
Bingzhi Chen ◽  
Meng Li ◽  
Guannan Liu
Keyword(s):  
Boundary Conditions ◽  
Response Surface ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Content Type ◽  
Design Variables ◽  
Multi Level ◽  
Response Surface Function

Purpose In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but also reduces the fitting accuracy of the response surface. In addition, the uncertainty of the optimal variables and their boundary conditions makes the optimal solution difficult to obtain. The purpose of this paper is to propose a method of fuzzy optimization design-based multi-level response surface to deal with the problem. Design/methodology/approach The main optimal variables are determined by Monte Carlo simulation, and are classified into four levels according to their sensitivity. The linear membership function and the optimal level cut set method are applied to deal with the uncertainties of optimal variables and their boundary conditions, as well as the non-fuzzy processing is carried out. Based on this, the response surface function of the first-level design variables is established based on the design of experiments. A combinatorial optimization algorithm is developed to compute the optimal solution of the response surface function and bring the optimal solution into the calculation of the next level response surface, and so on. The objective value of the fourth-level response surface is an optimal solution under the optimal design variables combination. Findings The results show that the proposed method is superior to the traditional method in computational efficiency and accuracy, and improves 50.7 and 5.3 percent, respectively. Originality/value Most of the previous work on optimization was based on single-level response surface and single optimization algorithm, without considering the uncertainty of design variables. There are very few studies which discuss the optimization efficiency and accuracy of multiple design variables. This research illustrates the importance of uncertainty factors and hierarchical surrogate models for multi-variable optimization design.

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

2019 ◽  
Vol 37 (3) ◽  
pp. 1093-1108
Author(s):  
Liang Li ◽  
Xuesong Chu ◽  
Guangming Yu
Keyword(s):  
Failure Analysis ◽  
Smoothed Particle Hydrodynamics ◽  
Traditional Method ◽  
Slope Failure ◽  
Slip Surface ◽  
Landslide Risk ◽  
Content Type ◽  
Particle Hydrodynamics ◽  
Smoothed Particle ◽  
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.

Analysis of Slope Failure Mechanisms Considering Micropile-Soil Interaction

2013 ◽  
Vol 535-536 ◽  
pp. 565-568 ◽  
Author(s):  
Hong Jian Liao ◽  
Cheng Lin Tian ◽  
Hang Zhou Li
Keyword(s):  
Large Scale ◽  
Slope Failure ◽  
Slip Surface ◽  
Loess Soil ◽  
Scale Model ◽  
Maximum Shear Strain ◽  
Loess Slope ◽  
Slip Surfaces ◽  
Large Scale Model ◽  
Stress And Deformation

A large scale model test was carried out in loess slope, in which the stress and deformation characteristics of slopes reinforced with different arrangements of micropiles were studied. The mechanism of the micropile-soil interaction and the reinforcement effect of micropiles in loess slope were analysed. Based on the scale of in-situ loess slope and the physical mechanics parameters of loess soil, a numerical model was established by using finite difference method. For a reasonable arrangement of micropiles in step-shaped slope, the critical slip surfaces were determined considering the influence of slope inclination, ratio of step height and loading position. The micropiles were arranged in the step-shaped slope based on the critical slip surface, and the relationship between the ultimate bearing capacity of slope and shear strength parameters of loess soil was studied. The maximum shear strain of micropile-soil and moment of micropiles were calculated, and then the mechanism of the micropile-soil interaction was analysed.

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