• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2401-2406
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• 2015

Pipelines are used by large heavy industries to deliver various types of fluids. Since this is important to maintain the performance of large systems, it is necessary to accurately predict remaining life of the corroded pipeline. However, predicting the remaining life is difficult due to uncertainties in the associated variables, such as geometries, material properties, corrosion rate, etc. In this paper, a statistical method for predicting corrosion remaining life is proposed using Bayesian inference. To accomplish this, pipeline failure probability was calculated using prior information about pipeline failure pressure according to elapsed time, and the given experimental data based on Bayes' rule. The corrosion remaining life was calculated as the elapsed time with 10 % failure probability. Using 10 and 50 samples generated from random variables affecting the corrosion of the pipe, the pipeline failure probability was estimated, after which the estimated remaining useful life was compared with the assumed true remaining useful life.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.4
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• pp.383-389
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• 2011

In this paper, a statistical calibration method is proposed in order to identify the variability of complex modulus for a rubber material due to operational temperature and experimental/model errors. To describe temperature- and frequency-dependent material properties, a fractional derivative model and a shift factor relationship are used. A likelihood function is defined as a product of the probability density functions where experimental values lie on the model. The variation of the fractional derivative model parameters is obtained by maximizing the likelihood function. Using the proposed method, the variability of a synthetic rubber material is estimated and applied to a rubber mount problem. The dynamic characteristics of the rubber mount are calculated using a finite element model of which material properties are sampled from Monte Carlo simulation. The calculated dynamic stiffnesses show very large variation.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.271-280
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• 2002

A probabilistic approach to identify the effects of uncertainties of soil strength parameters on searching a critical slip surface with the lowest reliability is introduced. In general construction field, it is impossible for the engineer to always gather a variety of statistical information of soil strength parameters for which lots of laboratory and in-situ soil testing are required and to use it with enough statistical knowledge. Thus, in order that the engineer may easily understand the probabilistic concept for the slope stability analysis, this study proposes a combined procedure to incorporate the engineering probabilistic tools into the existing deterministic slope stability analysis methods. Using UTEXAS 3, a slope stability analysis computer program developed by U.S. Army Corps of Engineers (U.S. COE), this study provides the results of this probabilistic slope stability analysis in terms of probability of failure or reliability index. This probabilistic method f3r slope stability analysis appears to yield more comprehensive results of slope reliability than does existing deterministic methods with safety factors alone.

• Journal of the Korean Society of Tobacco Science
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• v.27 no.1 s.53
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• pp.107-114
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• 2005

The uncertainty of measurement in quantitative analysis of ammonia by continuous-flow analysis method was evaluated following internationally accepted guidelines. The sources of uncertainty associated with the analysis of ammonia were the weighing of sample, the preparation of extracting solution, the addition of extracting solution into the sample, the reproducibility of analysis and the determination of water content in tobacco, etc. In calculating uncertainties, Type A of uncertainty was evaluated by the statistical analysis of a series of observation, and Type B by the information based on supplier's catalogue and/or certificated of calibration. It was shown that the main source of uncertainty was caused by the volume measurement of 1 mL and 2 mL, the purity of ammonia reference material in the preparation of standard solution, the reproducibility of analysis and the determination of water content of tobacco. The uncertainty in the addition of extraction solution, the sample weighing, the volume measurement of 50 mL and 100 mL, and the calibration curve of standard solution contributed relatively little to the overall uncertainty. The expanded uncertainty of ammonia determination in burley tobacco at $95\%$ level of confidence was $0.00997\%$.

• Journal of Welding and Joining
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• v.30 no.1
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• pp.78-84
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• 2012

Due to the inherent dimensional uncertainty, tolerances accumulate in the final assembly. Tolerance accumulation has serious effect on the performance of ECV assembly. This paper proposes a method of tolerance accumulation analysis using Monte Carlo simulation, which includes welding process in assemble process. This method can predict the final tolerance distributions of the completed assembly with the prescribed statistical tolerance distribution of each part to be assembled. With the inclusion of welding, another dimensional uncertainties due to partial melting is to be accounted as well. Partial melting of projection height was included in the tolerance propagation analysis. Verification of the proposed method was performed by making use of Monte Carlo simulation. Monte Carlo simulation results showed promising results in that we can predict the final tolerance distributions in advance before actual assembly process of precision machinery.

• Corrosion Science and Technology
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• v.12 no.5
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• pp.220-226
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• 2013

The uncertainty in statistical noise resistance measurement was evaluated for a type 316 stainless steel in NaCl solutions at room temperature. Sensitivity coefficients were determined for measurands or variables such as NaCl concentration, pH, solution temperature, surface roughness, inert gas flow rate and bias potential amplitude. The coefficients were larger for the variables such as NaCl concentration, pH, inert gas flow rate and solution temperature, and they were the major factors increasing the combined standard uncertainty of noise resistance. However, the contribution to the uncertainty in noise resistance measurement from the above variables was remarkably low compared to that from repeated measurements of noise resistance, and thus, it is difficult to lower the uncertainty in noise resistance measurement significantly by lowering the uncertainties related with NaCl concentration, pH, inert gas flow rate and solution temperature. In addition, the uncertainty in noise resistance measurement was high amounting to 17.3 % of the mean, indicating that the reliability in measurement of noise resistance is low.

• Steel and Composite Structures
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• v.21 no.1
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• pp.93-107
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• 2016

Seismic fragility analysis is a probabilistic decision-making framework which is widely implemented for evaluating vulnerability of a building under earthquake loading. It requires ingredient named probabilistic model and commonly developed using statistics requiring collecting data in large quantities. Preparation of such a data-base is often costly and time-consuming. Therefore, in this paper, by developing generic seismic drift demand model for regular-multi-story steel moment resisting frames is tried to present a novel application of the probabilistic decision-making analysis to practical purposes. To this end, a demand model which is a linear function of intensity measure in logarithmic space is developed to predict overall maximum inter-story drift. Next, the model is coupled with a set of regression-based equations which are capable of directly estimating unknown statistical characteristics of the model parameters.To explicitly address uncertainties arise from randomness and lack of knowledge, the Bayesian regression inference is employed, when these relations are developed. The developed demand model is then employed in a Seismic Fragility Analysis (SFA) for two designed building. The accuracy of the results is also assessed by comparison with the results directly obtained from Incremental Dynamic analysis.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1907-1916
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• 2020

In this work, the depletion capability implemented in Monte Carlo code MCS is investigated to predict the isotopic compositions of spent nuclear fuel (SNF). By comparison of MCS calculation results to post irradiation examination (PIE) data obtained from one pressurized water reactor (PWR), the validation of this capability is conducted. The depletion analysis is performed with the ENDF/B-VII.1 library and a fuel assembly model. The transmutation equation is solved by the Chebyshev Rational Approximation Method (CRAM) with a depletion chain of 3820 isotopes. 18 actinides and 19 fission products are analyzed in 14 SNF samples. The effect of statistical uncertainties on the calculated number densities is discussed. On average, most of the actinides and fission products analyzed are predicted within ±6% of the experiment. MCS depletion results are also compared to other depletion codes based on publicly reported information in literature. The code-to-code analysis shows comparable accuracy. Overall, it is demonstrated that the depletion capability in MCS can be reliably applied in the prediction of SNF isotopic inventory.

• Steel and Composite Structures
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• v.19 no.1
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• pp.191-208
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• 2015

The sensitivity-based finite element model updating method has received increasing attention in damage detection of structures based on measured modal parameters. Finding an optimization technique with high efficiency and fast convergence is one of the key issues for model updating-based damage detection. A new simple and computationally efficient optimization algorithm is proposed and applied to damage detection by using finite element model updating. The proposed method combines the Gauss-Newton method with region truncation of each iterative step, in which not only the constraints are introduced instead of penalty functions, but also the searching steps are restricted in a controlled region. The developed algorithm is illustrated by a numerically simulated 25-bar truss structure, and the results have been compared and verified with those obtained from the trust region method. In order to investigate the reliability of the proposed method in damage detection of structures, the influence of the uncertainties coming from measured modal parameters on the statistical characteristics of detection result is investigated by Monte-Carlo simulation, and the probability of damage detection is estimated using the probabilistic method.

• Geomechanics and Engineering
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• v.7 no.5
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• pp.525-537
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• 2014

The typical design of ground improvement with prefabricated vertical drains (PVD) and surcharge preloading involves a series of deterministic analyses using averaged or mean soil properties for the various combination of the PVD spacing and surcharge preloading height that would meet the criteria for minimum consolidation time and required degree of consolidation. The optimum design combination is then selected in which the total cost of ground improvement is a minimum. Considering the variability and uncertainties of the soil consolidation parameters, as well as considering the effects of soil disturbance (smear zone) and drain resistance in the analysis, this study presents a stochastic cost optimization of ground improvement with PVD and surcharge preloading. Direct Monte Carlo (MC) simulation and importance sampling (IS) technique is used in the stochastic analysis by limiting the sampled random soil parameters within the range from a minimum to maximum value while considering their statistical distribution. The method has been verified in a case study of PVD improved ground with preloading, in which average results of the stochastic analysis showed a good agreement with field monitoring data.