Probability of Failure Assessment of Building Using Traditional and Enhanced Monte Carlo Simulation Techniques

2018 ◽  
pp. 345-347
Author(s):  
Badreddine Chemali ◽  
Boualem Tiliouine
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Of Failure ◽  
Simulation Techniques ◽  
Failure Assessment

scholarly journals Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 472 ◽  
Author(s):  
Amir Mahdiyar ◽  
Danial Jahed Armaghani ◽  
Mohammadreza Koopialipoor ◽  
Ahmadreza Hedayat ◽  
Arham Abdullah ◽  
...  
Keyword(s):  
Gene Expression ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gene Expression Programming ◽  
Sensitivity Analyses ◽  
Accurate Estimation ◽  
Blasting Operation ◽  
Simulation Techniques ◽  
Input Variables ◽  
The Impact

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions.

Advanced Probabilistic Fracture Mechanics Using the R6 Procedure

2010 ◽  
Author(s):  
David W. Beardsmore ◽  
Karen Stone ◽  
Huaguo Teng
Keyword(s):  
Fracture Toughness ◽  
Monte Carlo ◽  
Fracture Mechanics ◽  
Monte Carlo Methods ◽  
Probability Of Failure ◽  
Defect Size ◽  
Probabilistic Fracture Mechanics ◽  
Safety Margins ◽  
Reliability Method ◽  
Failure Assessment

Deterministic Fracture Mechanics (DFM) assessments of structural components (e.g. pressure vessels and piping used in the nuclear industry) containing defects can usually be carried out using the R6 procedure. The aim of such an assessment is to demonstrate that there are sufficient safety margins on the applied loads, defect size and fracture toughness for the safe continual operation of the component. To ensure a conservative assessment is made, a lower-bound fracture toughness, and upper-bound defect sizes and applied loads are used. In some cases, this approach will be too conservative and will provide insufficient safety margins. Probabilistic Fracture Mechanics (PFM) allow a way forward in such cases by allowing for the inherent scatter in material properties, defect size and applied loads explicitly. Basic Monte Carlo Methods (MCM) allow an estimate of the probability of failure to be calculated by carrying out a large number of fracture mechanics assessments, each using a random sample of the different random variables (loads, defect size, fracture toughness etc). The probability of failure is obtained by counting the proportion of simulations which lead to assessment points that lie outside the R6 failure assessment curve. This approach can give good results for probabilities greater than 10−5. However, for smaller probabilities, the calculation may be inefficient and a very large number of assessments may be necessary to obtain an accurate result, which may be prohibitive. Engineering Reliability Methods (ERM), such as the First Order Reliability method (FORM) and the Second Order Reliability Method (SORM), can be used to estimate the probability of failure in such cases, but these methods can be difficult to implement, do not always give the correct result, and are not always robust enough for general use. Advanced Monte Carlo Methods (AMCM) combine the two approaches to provide an accurate and efficient calculation of probability of failure in all cases. These methods aim to carry out Importance Sampling so that only assessment points that lie close to or outside the failure assessment curve are calculated. Two methods are described in this paper: (1) orthogonal sampling, and (2) spherical sampling. The power behind these methods is demonstrated by carrying out calculations of probability of failure for semi-elliptical, surface breaking, circumferential cracks in the inside of a pressure vessel. The results are compared with the results of Basic Monte Carlo and Engineering Reliability calculations. The calculations use the R6 assessment procedure.

Solvation of poly(methyl acrylate) and poly(vinyl acetate) by CO2 studied via atomistic Monte Carlo simulation techniques

e-Polymers ◽  
2004 ◽  
Vol 4 (1) ◽  
Author(s):  
Sabine Beuermann ◽  
Michael Buback ◽  
Marco Drache ◽  
Dorit Nelke ◽  
Gudrun Schmidt-Naake
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Vinyl Acetate ◽  
Methyl Acrylate ◽  
Polymer Chains ◽  
Phase Behaviour ◽  
Specific Interactions ◽  
Model Compounds ◽  
Simulation Techniques ◽  
Solvent Environment

Abstract The differences in solubility of poly(vinyl acetate) (PVAc) and poly(methyl acrylate) (PMA) were addressed by applying atomistic Monte Carlo simulation techniques. Polymer segments consisting of nine monomer units serve as model compounds for polymer chains. As a measure of intermolecular interactions with the solvent environment, cohesion energies of the polymer segments embedded in either the corresponding monomer or in CO2 were calculated. Only in case of PMA segments in CO2 environment, specific interactions between polymer segments were identified. This finding is in agreement with experimental results on phase behaviour and propagation kinetics.

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