An efficient method combining active learning Kriging and Monte Carlo simulation for profust failure probability

2020 ◽  
Vol 387 ◽  
pp. 89-107 ◽  
Author(s):  
Chunyan Ling ◽  
Zhenzhou Lu ◽  
Bo Sun ◽  
Minjie Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Active Learning ◽  
Efficient Method ◽  
Failure Probability

Reliability Estimation of Buried Pipeline Using FORM, SORM and Monte Carlo Simulation

2006 ◽  
Vol 326-328 ◽  
pp. 597-600 ◽  
Author(s):  
Ouk Sub Lee ◽  
Dong Hyeok Kim
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Exposure Time ◽  
Failure Probability ◽  
Reliability Estimation ◽  
Buried Pipeline ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Method ◽  
The Difference

In this paper, the failure probability is estimated by using the FORM (first order reliability method), the SORM (second order reliability method) and the Monte Carlo simulation to evaluate the reliability of the corroded pipeline. It is found that the FORM technique is more effective in estimating the failure probability than the SORM technique for B31G and MB31G models with three different corrosion models. Furthermore, it is noted that the difference between the results of the FORM, the SORM and the Monte Carlo simulation decreases with the increase of the exposure time.

scholarly journals Volume determination of weights in the range from 1 g to 5 kg: a comparison of hydrostatic weighing and double-weighing in air using Monte-Carlo simulation

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 61
Author(s):  
Yi Su ◽  
Kilian Marti ◽  
Christian Wuethrich
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Efficient Method ◽  
Volume Determination ◽  
Correlated Variables ◽  
Hydrostatic Weighing ◽  
Mathematical Equations ◽  
Measurement Uncertainties

We have investigated the two methods, double-weighing in air and hydrostatic weighing, for the determination of the volume of weights in the range from 5 kg down to 1 g. We present the mathematical equations of both methods and show that Monte-Carlo simulation is a suitable way to determine the measurement uncertainties and overcome the difficulties in dealing with correlated variables. We found that the measurement uncertainties of the two methods are comparable and that double-weighing in air is an efficient method for determining the volume of weights below 1 kg.

Monte Carlo simulation in the evaluation of failure probability in waste stabilization ponds

2020 ◽  
Vol 38 ◽  
pp. 101658
Author(s):  
Matheus Sales Alves ◽  
Gustavo Ross Ribeiro Lima ◽  
André Luis Calado Araújo ◽  
Fernando José Araújo da Silva ◽  
Erlon Lopes Pereira
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Failure Probability ◽  
Waste Stabilization Ponds ◽  
Stabilization Ponds ◽  
Waste Stabilization

Reliability Analysis of Uncertain Input Variables for Cracked Structures

2011 ◽  
Vol 52-54 ◽  
pp. 1358-1363 ◽  
Author(s):  
M.R.M. Akramin ◽  
A. Zulkifli ◽  
M. Mazwan Mahat
Keyword(s):  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
Failure Probability ◽  
Probabilistic Analysis ◽  
Research Work ◽  
Adaptive Mesh ◽  
Element Analysis ◽  
Monte Carlo Simulation Technique ◽  
Cracked Structures

Probabilistic analysis aims at providing an assessment of cracked structures and taking relevant uncertainties into account in a rational quantitative manner. The main focus of this research work is on uncertainties aspect which relates with the nature of crack in materials. By using cracked structures modelling, finite element calculation, generation of adaptive mesh, sampling of cracked structure including uncertainties factors and probabilistic analysis using Monte Carlo method, the rigidity of cracked structures is estimated. Assessment of the accuracy in probabilistic structures is essential when limited amount of data is available. The hybrid finite element and probabilistic analysis represents the failure probability of the structures. The probability of failure caused by uncertainties relates to loads and material properties of the structure are estimated using Monte Carlo simulation technique. Numerical examples are presented to show probabilistic analysis based on Monte Carlo simulation provides accurate estimates of failure probability. The comparison shows that the combination between finite element analysis and probabilistic analysis provides a simple and realistic of quantify the failure probability.

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