INVESTIGATING THE SLOPE STABILITY BASED ON UNCERTAINTY BY USING FUZZY POSSIBILITY THEORY

2014 ◽  
Vol 59 (1) ◽  
pp. 179-188 ◽  
Author(s):  
Navid Hosseini ◽  
Mehran Gholinejad
Keyword(s):  
Slope Stability ◽  
Stability Condition ◽  
Estimation Method ◽  
Possibility Theory ◽  
Statistical Characteristics ◽  
Deterministic Analysis ◽  
Performance Function ◽  
Realistic Interpretation ◽  
Input Parameters ◽  
Grade Of Membership

Abstract The main purpose of this paper is to investigate the slope stability condition by using fuzzy estimation method based on fuzzy possibility theory. Due to use of this theory, the inaccuracy, ambiguity and uncertainty in input parameters are considered and therefore, the calculated factor of safety (FOS) is highly reliable. In this research, first, the input parameters of slope stability analysis, based on statistical characteristics and grade of membership concept, as a fuzzy numbers are defined. Then the performance function of slope behavior is defined and by using the fuzzy parameters, the FOS is calculated. In next step, by using the several α - cut, the calculated FOS is defined as a fuzzy form and subsequently, the slope stability condition based on fuzzy presentation of FOS is evaluated. The results show that, although based on deterministic analysis the studied slope is stable but based on fuzzy interpretation of FOS, the slope stability condition is scare. The fuzzy analysis of slope stability condition, by applying the uncertainty in calculating the FOS and defining the grade of membership for each unknown input parameters in model, a more realistic interpretation of slope stability condition is provided. In addition, the fuzzy presentation of the FOS, allowing more accurate judgments about slope stability condition.

Resonance Analysis of Train–Track–Bridge Interaction Systems with Correlated Uncertainties

2019 ◽  
Vol 20 (01) ◽  
pp. 2050008 ◽  
Author(s):  
Lifeng Xin ◽  
Xiaozhen Li ◽  
Jiaxin Zhang ◽  
Yan Zhu ◽  
Lin Xiao
Keyword(s):  
Probability Distributions ◽  
Three Dimensional ◽  
Estimation Method ◽  
Interaction Model ◽  
Point Estimation ◽  
Statistical Characteristics ◽  
Resonance Analysis ◽  
Track Bridge ◽  
Point Estimation Method ◽  
Nataf Transformation

Over the last decades, the resonance-related dynamics for bridge systems subjected to a moving train has been researched and discussed from mechanics, physics and mathematics. In the current work, new perspectives of train-induced resonance analysis are investigated through introducing random propagation process into the train–bridge dynamic interactions. Besides, the Nataf-transformation-based point estimation method is applied to generate pseudorandom variables following arbitrarily correlated probability distributions. A three-dimensional (3D) nonlinear train-ballasted track–bridge interaction model founded on fundamental physical and mechanical principles is employed to convey and depict train–bridge interactions with random properties considered. After that, extensive applications are illustrated in detail for revealing the statistical characteristics of the so-called “random resonance”. Numerical results show that the critical train speeds associated with resonance and cancelation are random in essence owing to the variability of system parameters; the correlation between parameters exerts obvious influences on system dynamic behaviors; the last vehicle of a train will be in more violent vibrations compared to the front vehicles; the influences of track irregularities on the wheel–rail interactions are significantly greater than those of resonance.

Non-Probabilistic Reliability Research on Slope Stability Based on Interval Analysis

2013 ◽  
Vol 838-841 ◽  
pp. 835-839
Author(s):  
Xiao Chun Lu ◽  
Liang Gan
Keyword(s):  
Slope Stability ◽  
Reliability Analysis ◽  
Uniform Distribution ◽  
Safety Factor ◽  
Failure Probability ◽  
Interval Analysis ◽  
Evaluation System ◽  
Safety Evaluation ◽  
Structural Performance ◽  
Performance Function

In this paper, slope stability considering non-probabilistic reliability analysis based on interval analysis was discussed. We can get safety factor, safety factor interval, non-probabilistic reliability by slope stability computation based on interval analysis. Based on the hypothesis that interval variable of structural performance function numerical value obeys uniform distribution, failure probability based on interval analysis was put forward. These form plural evaluation system about slope stability; it perfects safety evaluation for slope stability.

Non-Probabilistic Reliability and Slope Stability Analysis

2014 ◽  
Vol 578-579 ◽  
pp. 1538-1541
Author(s):  
Huan Sheng Mu
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Reliability Index ◽  
Mean Value ◽  
Slope Stability Analysis ◽  
Performance Function ◽  
Interval Variables ◽  
Reliability Method ◽  
The Mean ◽  
The Difference

In the present paper, a non-probabilistic reliability method is proposed for slope stability analysis. Soil properties involved in non-probabilistic reliability analysis are viewed as random variables and represented by interval variables. The performance function for slope stability analysis is expressed as the difference between anti-sliding moment and driving moment. The non-probabilistic reliability index is defined as the ratio of the mean value of performance function to deviate. Three examples have illustrated the simplicity and applicability of the method. This method provides a new means for slope stability analysis.

Benefit of Measurements and Structural Reliability Analysis for Wellhead Fatigue

2013 ◽  
Author(s):  
Torfinn Hørte ◽  
Massimiliano Russo ◽  
Michael Macke ◽  
Lorents Reinås
Keyword(s):  
Fatigue Life ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Structural Reliability ◽  
Offshore Structures ◽  
Life Extension ◽  
Worst Case ◽  
Deterministic Analysis ◽  
Input Parameters ◽  
Measured Response

Structural Reliability Analysis (SRA) methods have been applied to marine and offshore structures for decades. SRA has proven useful in life extension exercises and inspection planning of existing offshore structures. It is also a useful tool in code development, where the reliability level provided by the code is calculated by SRA and calibrated to a target failure probability. The current analysis methods for wellhead fatigue are associated with high sensitivity to variations in some input parameters. Some of these input parameters are difficult to assess, and sensitivity screening is often needed and the worst case is then typically used as a basis for the analysis. The degree of conservatism becomes difficult to quantify, and it is therefore equally difficult to find justification to avoid worst case assumptions. By applying SRA to the problem of wellhead fatigue, the input parameters are accounted for with their associated uncertainty given by probability distributions. In performing SRA all uncertainties are considered simultaneously, and the probability of fatigue failure is estimated and the conservatism is thereby quantified. In addition SRA also provides so-called uncertainty importance factors. These represent a relative quantification of which input parameter uncertainties contribute the most to the overall failure probability, and may serve well as guidance on where possible effort to reduce the uncertainty preferably should be made. For instance, instrumentation may be used to measure the actual structural response and thus eliminate the uncertainty that is associated with response calculations. Clearly measurements obtained from an instrumented system will have its own uncertainty. Other options could be to perform specific fatigue capacity testing or pay increased attention to logging of critical operational parameters such as the cement level in the annulus between the conductor and surface casing. This article deals with the use of measurements for fatigue life estimation. Continuous measurements of the BOP motion during the drilling operations have been obtained for a subsea well in the North Sea. These measurements are used both in conventional (deterministic) analysis and in SRA (probabilistic analysis) for fatigue in the wellhead system. From the deterministic analysis improved fatigue life results are obtained if the measured response replaces the response obtained by analysis. Furthermore, SRA is used to evaluate the appropriate magnitude of the design fatigue factor when fatigue analysis is based on measured response. It is believed that the benefit from measurements and SRA serve as an improved input to the decision making process in the event of life extension of existing subsea wells.

Video Call Traffic Identification Based on Bayesian Model

2013 ◽  
Vol 765-767 ◽  
pp. 1307-1311
Author(s):  
Ying Hou ◽  
Hai Huang ◽  
Kai Wang ◽  
Yu Hang Zhu
Keyword(s):  
Computational Complexity ◽  
Probability Density ◽  
Estimation Method ◽  
Statistical Characteristics ◽  
Probability Density Estimation ◽  
Video Traffic ◽  
Traffic Identification ◽  
Large Samples ◽  
Estimate Method ◽  
High Computational Complexity

This paper proposes Bayesian statistical method to identify the video traffic by the symmetrical features and coding statistical characteristics of video calls. According to the problem of high computational complexity of the non-parametric probability density estimate method in the condition of large samples, we propose grid probability density estimation method of gird division to reduce the computational complexity. We present identification results. The experimental results indicate that that this method can effectively detect video call traffic.

scholarly journals Vulnerability Analysis to Drought Based on Remote Sensing Indexes

2020 ◽  
Vol 17 (20) ◽  
pp. 7660
Author(s):  
Huicong Jia ◽  
Fang Chen ◽  
Jing Zhang ◽  
Enyu Du
Keyword(s):  
Growth Rate ◽  
Vegetation Index ◽  
Estimation Method ◽  
Cumulative Distribution ◽  
Statistical Characteristics ◽  
Cumulative Probability ◽  
Drought Risk ◽  
Variance Ratio ◽  
Vulnerability Curve ◽  
Disaster Losses

A vulnerability curve is an important tool for the rapid assessment of drought losses, and it can provide a scientific basis for drought risk prevention and post-disaster relief. Those populations with difficulty in accessing drinking water because of drought (hereon “drought at risk populations”, abbreviated as DRP) were selected as the target of the analysis, which examined factors contributing to their risk status. Here, after the standardization of disaster data from the middle and lower reaches of the Yangtze River in 2013, the parameter estimation method was used to determine the probability distribution of drought perturbations data. The results showed that, at the significant level of α = 0.05, the DRP followed the Weibull distribution, whose parameters were optimal. According to the statistical characteristics of the probability density function and cumulative distribution function, the bulk of the standardized DRP is concentrated in the range of 0 to 0.2, with a cumulative probability of about 75%, of which 17% is the cumulative probability from 0.2 to 0.4, and that greater than 0.4 amounts to only 8%. From the perspective of the vulnerability curve, when the variance ratio of the normalized vegetation index (NDVI) is between 0.65 and 0.85, the DRP will increase at a faster rate; when it is greater than 0.85, the growth rate of DRP will be relatively slow, and the disaster losses will stabilize. When the variance ratio of the enhanced vegetation index (EVI) is between 0.5 and 0.85, the growth rate of DRP accelerates, but when it is greater than 0.85, the disaster losses tend to stabilize. By comparing the coefficient of determination (R2) values fitted for the vulnerability curve, in the same situation, EVI is more suitable to indicate drought vulnerability than NDVI for estimating the DRP.

Modeling and Uncertainty Estimation of Wind Power Curve Based on Recorded Field Data

2014 ◽  
Vol 986-987 ◽  
pp. 694-697 ◽  
Author(s):  
Peng Lin ◽  
Shu Qiang Zhao
Keyword(s):  
Confidence Interval ◽  
Wind Power ◽  
Field Data ◽  
Estimation Method ◽  
Interval Estimation ◽  
Uncertainty Estimation ◽  
Statistical Characteristics ◽  
Power Curve ◽  
Speed Level ◽  
Confidence Interval Estimation

Wind power curve of wind turbine has great importance in the prediction of wind power. The measured wind power curve is drawn by method of bins based on recorded field data; the uncertainty factors of the wind power curve is analyzed, and a non-parametric confidence interval estimation method is proposed based on analyzing the statistical characteristics of the data distribution. By means of the method, a probability density function model for wind power in each wind speed level is established, and the uncertainty estimation confidence interval of wind power curve is obtained on the basis of deterministic estimation. The example analysis proves the efficiency and feasibility of the method proposed in this paper.

GIS-Based Deterministic Analysis of Deep-Seated Slope Stability in a Complex Geological Setting

2015 ◽  
pp. 1437-1440 ◽  
Author(s):  
Martin Mergili ◽  
Ivan Marchesini ◽  
Massimiliano Alvioli ◽  
Mauro Rossi ◽  
Michele Santangelo ◽  
...  
Keyword(s):  
Slope Stability ◽  
Deterministic Analysis ◽  
Geological Setting

scholarly journals A novel length-based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries

2014 ◽  
Vol 72 (1) ◽  
pp. 217-231 ◽  
Author(s):  
Adrian Hordyk ◽  
Kotaro Ono ◽  
Sarah Valencia ◽  
Neil Loneragan ◽  
Jeremy Prince
Keyword(s):  
Empirical Test ◽  
Estimation Method ◽  
Size Composition ◽  
Cost Effective ◽  
Small Scale ◽  
Empirical Estimation ◽  
Input Parameters ◽  
Maximum Likelihood Methods ◽  
Spawning Potential Ratio ◽  
The Difference

Abstract The spawning potential ratio (SPR) is a well-established biological reference point, and estimates of SPR could be used to inform management decisions for data-poor fisheries. Simulations were used to investigate the utility of the length-based model (LB-SPR) developed in Hordyk et al. (2015). Some explorations of the life history ratios to describe length composition, spawning-per-recruit, and the spawning potential ratio. ICES Journal of Marine Science, 72: 204–216.) to estimate the SPR of a stock directly from the size composition of the catch. This was done by (i) testing some of the main assumptions of the LB-SPR model, including recruitment variability and dome-shaped selectivity, (ii) examining the sensitivity of the model to error in the input parameters, and (iii) completing an initial empirical test for the LB-SPR model by applying it to data from a well-studied species. The method uses maximum likelihood methods to find the values of relative fishing mortality (F/M) and selectivity-at-length that minimize the difference between the observed and the expected length composition of the catch, and calculates the resulting SPR. When parameterized with the correct input parameters, the LB-SPR model returned accurate estimates of F/M and SPR. With high variability in annual recruitment, the estimates of SPR became increasingly unreliable. The usefulness of the LB-SPR method was tested empirically by comparing the results predicted by the method with those for a well-described species with known length and age composition data. The results from this comparison suggest that the LB-SPR method has potential to provide a tool for the cost-effective assessment of data-poor fisheries. However, the model is sensitive to non-equilibrium dynamics, and requires accurate estimates of the three parameters (M/k, L∞, and CVL∞). Care must be taken to evaluate the validity of the assumptions and the biological parameters when the model is applied to data-poor fisheries.

A points estimation and series approximation method for uncertainty analysis

2009 ◽  
Vol 223 (9) ◽  
pp. 1997-2007 ◽  
Author(s):  
X F Zhang ◽  
Y E Zhao ◽  
Y M Zhang ◽  
X Z Huang ◽  
H Li
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Function Approximation ◽  
Structural Reliability ◽  
Estimation Method ◽  
Point Estimation ◽  
Performance Function ◽  
Density Function Approximation ◽  
Point Estimation Method

The objective of this article is to present an algorithm for moment evaluation and probability density function approximation of performance function for structural reliability analysis. In doing so, a point estimation method for probability moment of performance function is discussed at first. Based on the coherent relationship between the orthogonal polynomial and probability density function, formulas for point estimation are derived. Vector operators are defined to alleviate computational burden for computer programming. Then, by utilizing C-type Gram—Charlier series expansion method, a procedure for probability density function approximation of the performance function is studied. At last, the accuracy of the proposed method is demonstrated using three numerical examples.

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