scholarly journals Interval Nonprobabilistic Reliability Analysis for Ancient Landslide considering Strain-Softening Behavior: A Case Study

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zilong Zhou ◽  
Chenglong Lin ◽  
Xin Cai ◽  
Riyan Lan
Keyword(s):  
Reliability Analysis ◽  
Strain Softening ◽  
Constitutive Relations ◽  
Failure Surface ◽  
Normal Space ◽  
Probability Density Distribution ◽  
Probability Method ◽  
Uncertain Parameters ◽  
Ancient Landslide ◽  
Softening Behavior

Uncertainties in geotechnical parameters significantly affect the stability evaluation of an ancient landslide, especially when considering the strain-softening behavior. Due to the great difficulty in obtaining the probability density distribution of geoparameters, an interval nonprobability reliability analysis framework combined with numerical strain-softening constitutive relations was established in this paper. Interval variables were defined as the uncertain parameters in the strain-softening model. The interval nonprobabilistic reliability was defined as the minimum distance from the origin point to the failure surface in the standard normal space, which is the key index for describing the ability of a system to tolerate the variation of uncertain parameters. The proposed method was used to evaluate the reliability of Baishi ancient landslide. The parameter sensitivity analysis was also conducted. Through the proposed method, it is considered that Baishi ancient landslide is safe and stable, and the strain threshold kr is the dominant parameter. The results calculated by the proposed method agree well with the actual situation. This indicates the proposed method is more applicable than the traditional probability method when the data are scare.

scholarly journals Experimental and Theoretical Investigations of the Constitutive Relations of Artificial Frozen Silty Clay

Materials ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3159
Author(s):  
Zhiming Li ◽  
Jian Chen ◽  
Chaojun Mao
Keyword(s):  
Water Content ◽  
Strain Softening ◽  
Constitutive Relations ◽  
Random Variable ◽  
Confining Pressure ◽  
Silty Clay ◽  
Softening Behavior ◽  
Theoretical Investigations ◽  
Confining Pressures ◽  
Strength And Deformation

The strength and deformation characteristics of artificial frozen soils are quite sensitive to temperature, confining pressure, and water content. To investigate these effects, a series of triaxial compressive tests on frozen Harbin silty clay were conducted at temperatures of −5 °C, −10 °C, and −15 °C under different confining pressures and water contents. From the stress–strain curves under lower water content and confining pressure, strain–softening behavior was observed. The modified Duncan–Chang (MDC) model was employed to describe the constitutive relations of artificial frozen silty clay while considering the strain–softening effects. After introducing statistical damage (SD) theory, an SD constitutive model with the failure strain as a random variable was proposed, which is able to overcome the drawbacks of the MDC model. The predicted SD model results are found to be consistent with the experimental results.

A Hybrid Reliability Approach Based on Probability and Interval for Uncertain Structures

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
C. Jiang ◽  
X. Han ◽  
W. X. Li ◽  
J. Liu ◽  
Z. Zhang
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Random Variables ◽  
Normal Space ◽  
Uncertain Parameters ◽  
Limited Information ◽  
Reliability Models ◽  
Distribution Parameters ◽  
Hybrid Reliability ◽  
Monotonicity Analysis

Traditional reliability analysis generally uses probability approach to quantify the uncertainty, while it needs a great amount of information to construct precise distributions of the uncertain parameters. In this paper, a new reliability analysis technique is developed based on a hybrid uncertain model, which can deal with problems with limited information. All uncertain parameters are treated as random variables, while some of their distribution parameters are not given precise values but variation intervals. Due to the existence of the interval parameters, a limit-state strip enclosed by two bounding hyper-surfaces will be resulted in the transformed normal space, instead of a single hyper-surface as we usually obtain in conventional reliability analysis. All the limit-state strips are then summarized into two different classes and corresponding reliability analysis models are proposed for them. A monotonicity analysis is carried out for probability transformations of the random variables, through which effects of the interval distribution parameters on the limit state can be well revealed. Based on the monotonicity analysis, two algorithms are then formulated to solve the proposed hybrid reliability models. Three numerical examples are investigated to demonstrate the effectiveness of the present method.

scholarly journals Semi-Supervised Deep Learning for High-Dimensional Uncertainty Quantification

2020 ◽  
Author(s):  
Zequn Wang ◽  
Mingyang Li
Keyword(s):  
Uncertainty Quantification ◽  
Reliability Analysis ◽  
Supervised Learning ◽  
Dimensional Space ◽  
Limit State ◽  
Failure Surface ◽  
Simulation Method ◽  
High Dimensional ◽  
State Function ◽  
Latent Space

Abstract Conventional uncertainty quantification methods usually lacks the capability of dealing with high-dimensional problems due to the curse of dimensionality. This paper presents a semi-supervised learning framework for dimension reduction and reliability analysis. An autoencoder is first adopted for mapping the high-dimensional space into a low-dimensional latent space, which contains a distinguishable failure surface. Then a deep feedforward neural network (DFN) is utilized to learn the mapping relationship and reconstruct the latent space, while the Gaussian process (GP) modeling technique is used to build the surrogate model of the transformed limit state function. During the training process of the DFN, the discrepancy between the actual and reconstructed latent space is minimized through semi-supervised learning for ensuring the accuracy. Both labeled and unlabeled samples are utilized for defining the loss function of the DFN. Evolutionary algorithm is adopted to train the DFN, then the Monte Carlo simulation method is used for uncertainty quantification and reliability analysis based on the proposed framework. The effectiveness is demonstrated through a mathematical example.

scholarly journals Semianalytical Solution for Large Deformation of Salt Cavern with Strain-Softening Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Lina Ran ◽  
Huabin Zhang ◽  
Qingqing Zhang
Keyword(s):  
Plastic Zone ◽  
Large Deformation ◽  
Strain Softening ◽  
Structural Characteristics ◽  
Strength Criterion ◽  
Geological Strength Index ◽  
Salt Rock ◽  
Salt Cavern ◽  
Softening Behavior ◽  
The Impact

A semianalytical solution of stress and displacement in the strain-softening and plastic flow zones of a salt cavern is presented. The solution is derived by adopting the large deformation theory, considering the nonlinear Hoek–Brown (H-B) strength criterion. The Romberg method is used to carry out numerical calculation, and then, the large deformation law of displacement is analyzed. The results are compared with those obtained by former numerical methods, and the solutions are validated. The results indicate that the displacement of the plastic zone decreases with the increase in distance away from the salt cavern. Similarly, it decreases with an increase in the geological strength index or running pressure, with the running pressure having a more significant effect on the displacement. It increases with the dilation angle, and the impact degree gradually increases. Compared with the softening parameter, h, of the plastic zone, the flow parameter, f, has little impact on the displacement. The displacement of the plastic zone obviously increased when considering the strain-softening of salt rock. When considering the shear dilation and softening behaviors of salt rock, the analytical solution obtained by employing the experiential regression Hoek–Brown (H-B) criterion, which considers many factors such as the structural characteristics of the salt formation and the rock mass quality, is safer and closer to the actual situation. This study can provide reference for many applications, including but not confined to analyzing the deformation of the surrounding rock of an underground salt cavern storage facility during construction.

A comparison of strain-rate enhancement approaches for concrete material subjected to high strain-rate

2017 ◽  
Vol 8 (2) ◽  
pp. 155-176 ◽  
Author(s):  
Xiangzhen Kong ◽  
Qin Fang ◽  
Hao Wu ◽  
Jian Hong
Keyword(s):  
High Strain Rate ◽  
Strain Rate ◽  
Strain Softening ◽  
High Strain ◽  
Consistency Condition ◽  
Rate Data ◽  
Concrete Material ◽  
Rate Enhancement ◽  
Softening Behavior ◽  
Dynamic Loadings

High strain-rate induced from intense dynamic loadings will cause an obvious enhancement of concrete material frequently used in civil and defense engineering, which plays an important role in correct numerical simulations of concrete members subjected to intense dynamic loadings. In this article, the existing three strain-rate enhancement approaches for concrete material are compared by three aspects, that is, flexibility of fitting data, consistency condition, and time-dependent behavior. The so-called “overstress approach” is found to be not flexible for fitting high strain-rate data and unable to well predict the strain-softening behavior but can capture the inherent viscidity of concrete material. The “consistency approach” can describe the strain-softening behavior and the inherent viscidity but may be inconvenient and time-consuming when fitting high strain-rate data. The “simplified approach” widely used in commercial concrete material models can describe the strain-softening behavior and fit high strain-rate data by a more convenient and direct way but cannot capture the inherent viscidity of concrete material. Examples of uniaxial stress including loading and unloading under constant and varying strain-rates are presented to demonstrate the above-mentioned findings, in which the updating algorithm of dynamic stress is presented in detail.

Progressive failures in eastern Canadian and Scandinavian sensitive clays

2011 ◽  
Vol 48 (11) ◽  
pp. 1696-1712 ◽  
Author(s):  
Ariane Locat ◽  
Serge Leroueil ◽  
Stig Bernander ◽  
Denis Demers ◽  
Hans Petter Jostad ◽  
...  
Keyword(s):  
Failure Mechanism ◽  
Strain Softening ◽  
Progressive Failure ◽  
Earth Pressure ◽  
Failure Surface ◽  
Eastern Canada ◽  
Stress Strain ◽  
Softening Behaviour ◽  
Strain Behaviour

Observations from past events are used to show that the concept of progressive failure may explain translational progressive landslides and spreads — large landslides occurring in sensitive clays. During progressive failure, the strain-softening behaviour of the soil causes unstable forces to propagate a failure surface further in the slope. Translational progressive landslides generally take place in long, gently inclined slopes. Instability in a steeper upslope area is followed by redistribution of stress, which increases earth pressure further downslope. Passive failure may therefore occur in less-inclined ground, heaving the soil. Spreads are usually trigged by erosion of a deposit having a higher angle near the toe. Instability starts near the toe of the slope and propagates into the deposit, reducing earth pressure. This may lead to the formation of an active failure with dislocation of the deposit into horsts and grabens. The failure mechanism of both types of landslides is controlled by the stresses in the slope and the stress–strain behaviour of the soil. The mechanism presented explains the sensitivity of a slope to minor disturbances and the resulting high retrogressions observed for such landslides in Scandinavia and eastern Canada.

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