scholarly journals An Improved Monte Carlo Method Based on Neural Network and Fuzziness Analysis: A Case Study of the Nanpo Dump of the Chengmenshan Copper Mine

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Feng Gao ◽  
Xiaodong Wu ◽  
LeWen Wu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Membership Function ◽  
Safety Factor ◽  
Reliability Index ◽  
Bootstrap Method ◽  
Probability Of Failure ◽  
Small Samples ◽  
Dump Site ◽  
Mcs Method

The landslide of dump is a man-made geological disaster which will bring great harm to the surrounding people and environment, and probabilistic reliability analysis is commonly used to analyze the probability of slope landslide or whether protective measures should be taken. Monte Carlo simulation is the most commonly used method, but there are some problems, such as low efficiency, statistical ambiguity of small samples, and the fuzzy transition interval of the stability criterion. This paper proposes an improved Monte Carlo method that uses an improved bootstrap method to process small samples of geotechnical data, employs ELM (extreme learning machine) based on PSO (particle swarm optimization) to fit the limit equilibrium method function, and constructs the safety factor membership function of the dump site considering the fuzzy transition interval. This method was applied to an example slope of the dump site in Chengmenshan, Jiangxi. Comparing the analysis result with the result of the traditional MCS (Monte Carlo Search) method, it was found that after adding the safety factor membership function, the result was closer to the actual situation of the dump site, and the probability of failure and reliability index values were closer to those of the dangerous state; after the original function was replaced by the PSO-ELM model, the efficiency of the MCS method was greatly improved while the results maintained high consistency with the original results; the MCS method combined with the bootstrap method not only simulated the fuzzy uncertainty of the original sample statistics and distribution type but also expressed the reliability index and probability of failure as a two-sided confidence interval with a certain confidence level. The above conclusion proves the effectiveness and superiority of this method compared with the original MCS method.

Bootstraps, Een Nieuw Statistisch Alternatief

1988 ◽  
Vol 31 ◽  
pp. 45-56 ◽  
Author(s):  
E.D.J. Schils
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Relative Frequency ◽  
Sampling Error ◽  
Bootstrap Method ◽  
Sampling Theory ◽  
Arithmetic Mean ◽  
Language Loss ◽  
Relative Frequency Distribution ◽  
The Bootstrap Method

The bootstrap is a Monte Carlo method for the approximation of the sampling error of a statistic. The bootstrap method estimates this standard error on the basis of the repeated calculation of the statistic at hand in each of a great number of so-called bootstrap samples, i.e. samples with replacement from a probability distribution which exactly mirrors the empirical relative frequency distribution. The method is useful when there is no analytical sampling theory available for the statistic at hand, or when violation of underlying assumptions precludes the application of an available sampling theory. This paper uses an analytically well-known problem as the context for the presentation of the method, viz. the sampling distribution of the arithmetic mean. The method is then applied to an investigation of language loss using an unfamiliar research design.

scholarly journals Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4965
Author(s):  
Kun Mo Lee ◽  
Min Hyeok Lee ◽  
Jong Seok Lee ◽  
Joo Young Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Emission Factor ◽  
Bootstrap Method ◽  
Ghg Emissions ◽  
Parametric Bootstrap ◽  
Random Variable ◽  
Ghg Emission ◽  
Nonparametric Bootstrap ◽  
Mcs Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

A probability model for fibrous composites with local load sharing

1980 ◽  
Vol 372 (1751) ◽  
pp. 539-553 ◽  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Extreme Value Theory ◽  
Probabilistic Analysis ◽  
Asymptotic Theory ◽  
Probability Model ◽  
Probability Of Failure ◽  
Value Theory ◽  
Fibrous Composites ◽  
The Monte Carlo Method

The chain-of-bundles model for fibrous composites is reviewed, and an approximation to the probability of failure is derived. This leads to formulae for predicting the strength of such a composite. These formulae are developed in the context of an asymptotic theory, and the Monte Carlo method is used to study a specific case in more detail. We also discuss the size effect. The probabilistic analysis relies heavily on extreme value theory, and a brief survey of the relevant parts of that theory is included.

Exit Condition for Probabilistic Assessment Using Monte Carlo Method

2010 ◽  
Vol 10 (1) ◽  
pp. 1-9
Author(s):  
Jakub Valihrach ◽  
Petr Konečný
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimization Design ◽  
Random Variable ◽  
Probability Of Failure ◽  
Probabilistic Assessment ◽  
Theoretical Derivation ◽  
The Monte Carlo Method ◽  
The Relationship

Exit Condition for Probabilistic Assessment Using Monte Carlo Method This paper introduces a condition used to exit a probabilistic assessment using the Monte Carlo simulation, and to evaluate it with regard to the relationship between the computed estimate of the probability of failure and the target design probability. The estimation of probability of failure is treated as a random variable, considering its variance that is dependent on the number of performed Monte Carlo simulation steps. After theoretical derivation of the decision condition, it is tested numerically with regard to its accuracy and computational efficiency. The condition is suitable for optimization design using the Monte Carlo method.

Reliability Analysis on Slag Concrete Brick Masonry Compressed Based on Monte-Carlo Method

2014 ◽  
Vol 651-653 ◽  
pp. 1227-1230
Author(s):  
Yi Zheng ◽  
Wei Shi Xiong ◽  
Fei Gao ◽  
Xiao Jun Shen ◽  
Zhao Jun Qiu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reliability Analysis ◽  
Reliability Index ◽  
Brick Masonry ◽  
New Materials ◽  
Clay Brick ◽  
Concrete Masonry ◽  
Dead Loading ◽  
Change Tendency

Slag concrete brick, as a kind of new materials utilizing wastes, is one kind of subsistent for clay brick. However, research on Slag concrete brick is very few, which restrains applying for slag concrete brick. By analyzing the reliability of the compress specimen with slag concrete brick masonry, the resistant statistic parameters are obtained and the computer probabilistic formula is established. Its reliability based on Monte-Carlo Method is calculated by means of Matlab. The result calculated is reasonable and reliable, which meets the requirement in rules. With the increasing of radio of live loading to dead loading, change tendency of reliability index of slag concrete masonry is that both sides are low and center is tall, and maximum appears between 0.5 and 1.0.

BOOTSTRAP MONTE CARLO WITH ADAPTIVE STRATIFICATION FOR POWER ESTIMATION

2002 ◽  
Vol 11 (04) ◽  
pp. 333-350
Author(s):  
HENG-LIANG HUANG ◽  
JING-YANG JOU
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Power Distribution ◽  
Bootstrap Method ◽  
Input Sequence ◽  
Power Estimation ◽  
Coefficient Matrix ◽  
Multiple Regression Method ◽  
Simulation Time ◽  
The Bootstrap Method

Monte Carlo approach for power estimation is based on the assumption that the samples of power are Normally distributed. However, the power distribution of a circuit is not always Normal in the real world. In this paper, the Bootstrap method is adopted to adjust the confidence interval and redeem the deficiency of the conventional Monte Carlo method. Besides, a new input sequence stratification technique for power estimation is proposed. The proposed technique utilizes a multiple regression method to compute the coefficient matrix of the indicator function for stratification. This new stratification technique can adaptively update the coefficient matrix and keep the population of input vectors in a better stratification status. The experimental results demonstrate that the proposed Bootstrap Monte Carlo method with adaptive stratification can effectively reduce the simulation time and meet the user-specified confidence level and error level.

scholarly journals Calculation of Failure Probability of Constantly Loaded Cantilever Beam by Monte Carlo Method

2014 ◽  
Vol 17 (3) ◽  
pp. 80-82
Author(s):  
Dušan Páleš ◽  
Milada Balková ◽  
Ingrid Karandušovská
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Cantilever Beam ◽  
Probability Distributions ◽  
Probabilistic Approach ◽  
Constant Load ◽  
Probability Of Failure ◽  
The Subject ◽  
Individual Variables ◽  
The Mathematical Model

Abstract In this paper, we demonstrate a probabilistic approach to the design of structures on a cantilever beam with constant load. Individual variables in the mathematical model are not represented deterministically by their specifc values but randomly by probability distributions. Normal distribution is used for all random variables. The resulting probability of failure is calculated using a simple Monte Carlo method, for which a brief overview is also provided in this article. Such a probabilistic proposal is the subject matter of newly emerging feld Reliability of Structures.

Practical reliability analysis of slope stability by advanced Monte Carlo simulations in a spreadsheet

2011 ◽  
Vol 48 (1) ◽  
pp. 162-172 ◽  
Author(s):  
Yu Wang ◽  
Zijun Cao ◽  
Siu-Kui Au
Keyword(s):  
Monte Carlo ◽  
Slope Stability ◽  
Spatial Variability ◽  
Soil Properties ◽  
Reliability Analysis ◽  
Slip Surface ◽  
Probability Of Failure ◽  
Critical Slip Surface ◽  
Microsoft Excel ◽  
Mcs Method

This paper develops a Monte Carlo simulation (MCS)-based reliability analysis approach for slope stability problems and utilizes an advanced MCS method called “subset simulation” for improving efficiency and resolution of the MCS at relatively small probability levels. Reliability analysis is operationally decoupled from deterministic slope stability analysis and implemented using a commonly available spreadsheet software, Microsoft Excel. The reliability analysis spreadsheet package is validated through comparison with other reliability analysis methods and commercial software. The spreadsheet package is then used to explore the effect of spatial variability of the soil properties and critical slip surface. It is found that, when spatial variability of soil properties is ignored by assuming perfect correlation, the variance of the factor of safety (FS) is overestimated, which may result in either over (conservative) or under (unconservative) estimation of the probability of failure (Pf = P(FS < 1)). When the spatial variability of soil properties is considered, the critical slip surface varies spatially and such spatial variability should be properly accounted for. Otherwise, the probability of failure can be significantly underestimated and unconservative.

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