• 한국콘크리트학회:학술대회논문집
• /
• 한국콘크리트학회 2003년도 봄 학술발표회 논문집
• /
• pp.181-186
• /
• 2003

There are many uncertainties in structural failures or structures, so probabilistic failure cause assessment should be performed in order to consider the uncertainties. However, in many cases of forensic engineering, the failure cause assessments are performed by deterministic approach though number of uncertainties are existed in the failures or structures. Thus, deterministic approach may have possibility for leading to unreasonable and unrealistic failure cause assessment due to ignorance of the uncertainties. Therefore, probabilistic approach is needed to complement the shortcoming of deterministic approach and to perform the more reasonable and realistic failure cause assessment. In this study, reliability-based failure cause assessment (reliability based forensic engineering) is performed, which can incorporate uncertainties in failures and structures. For more practical application, the modified ETA technique is proposed, which automatically generates the defected structural model, performs structural analysis and reliability analysis, and calculates the failure probabilities of the failure events and the occurrence probabilities of the failure scenarios. Also, for more precise reliability analysis, uncertainties are estimated more reasonably by using bayesian approach based on the experimental laboratory testing data in forensic report.

• Computational Structural Engineering : An International Journal
• /
• 제1권2호
• /
• pp.81-87
• /
• 2001

A framework for reliability analysis of structural components and systems under conditions of statistical and model uncertainty is presented. The Bayesian parameter estimation method is used to derive the posterior distribution of model parameters reflecting epistemic uncertainties. Point, predictive and bound estimates of reliability accounting for parameter uncertainties are derived. The bounds estimates explicitly reflect the effect of epistemic uncertainties on the reliability measure. These developments are enhance-ments of second-moment uncertainty analysis methods developed by A. H-S. Ang and others three decades ago.

• 한국환경과학회지
• /
• 제28권1호
• /
• pp.19-35
• /
• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• 자원ㆍ환경경제연구
• /
• 제23권2호
• /
• pp.249-280
• /
• 2014

IPCC는 국가별 온실가스 배출량이 얼마나 확실한 값인가를 보여줄 수 있는 불확도를 함께 보고하도록 규정하고 있다. 그렇지만 한국 정부는 IPCC 기본값을 그대로 적용하고 있는 수준에 불과하며, 그나마도 결측된 값들이 있어서 전체적인 불확도를 산정하지 못한 채 항목별 불확도만을 나열하고 있을 뿐이다. 이에 본 논문에서는 국가 온실가스 배출량의 85.3%를 차지하는 에너지분야를 대상으로 Tier 1 수준의 에러전파방법을 이용해서 온실가스 인벤토리의 불확도를 추정하고 있다. 분석결과 국내 에너지분야 온실가스 배출량의 불확도는 3.4%였으며, 이는 핀란드와 유사한 수치인 것으로 밝혀졌다. 그렇지만 온실가스별로는 이산화탄소의 불확도가 2.7%에 불과했지만, 메탄은 116%, 아산화질소는 473%에 달할 정도로 차이가 큰 것으로 나타났다. 따라서 본 논문에서는 한국 정부가 에너지분야의 불확도를 낮추려면 이산화탄소 보다는 메탄과 아산화질소를 대상으로 활동도뿐만 아니라 배출계수의 개선이 필요하다는 정책적 함의가 제시될 수 있었다. 결론적으로는 IPCC 기본값 대신에 신뢰도 높은 한국 고유의 배출계수를 개발하는 작업이 필요함을 제안하고 있다.

• 분석과학
• /
• 제23권3호
• /
• pp.295-303
• /
• 2010

폐수 시료를 이용한 중금속분석용 수질표준물질을 제조하여, 납, 카드뮴, 크롬, 구리, 아연, 망간, 철 등 중금속 7 종에 대해 인증분석 및 불확도 등을 평가하고, 24 개 환경측정분석기관을 대상으로 실험실간 비교실험을 실시하였다. 폐수표준물질의 인증값 및 확장불확도는 KS A ISO 가이드 35(2005)에 의해 도출하였으며, 균질성에 기인한 표준불확도는 특성값의 0.43~2.67%로 나타났다. 환경측정분석기관간 비교실험에서 모든 항목의 비교실험 결과값이 정규분포를 따랐으며, 로버스트 평균값과 폐수표준물질의 인증값과 비교한 결과, 대부분의 항목에서 로버스트 평균값이 인증값보다 낮게 나타났다.

• 반도체디스플레이기술학회지
• /
• 제22권2호
• /
• pp.104-111
• /
• 2023

The semiconductor industry, which relies on global supply chains, has recently been facing longer lead time for material procurement due to supply chain uncertainties. Moreover, since increasing customer satisfaction and reducing inventory costs are in a trade-off relationship, it is challenging to determine the appropriate safety stock level under demand and lead time uncertainties. In this paper, we propose a framework for determining safety stock levels by utilizing the optimization method to determine the optimal safety stock level. Additionally, we employ a linear regression method to analyze customer satisfaction scores and inventory costs based on variations in lead time and demand. To verify the effectiveness of the proposed framework, we compared safety stock levels obtained by the regression equations with those of the conventional method. The numerical experiments demonstrated that the proposed method successfully reduces inventory costs while maintaining the same level of customer satisfaction when lead time increases.

• Earthquakes and Structures
• /
• 제2권1호
• /
• pp.25-42
• /
• 2011

This paper discusses a mechanical model for the vulnerability assessment of old masonry building aggregates that takes into account the uncertainties inherent to the building parameters, to the seismic demand and to the model error. The structural capacity is represented as an analytical function of a selected number of geometrical and mechanical parameters. Applying a suitable procedure for the uncertainty propagation, the statistical moments of the capacity curve are obtained as a function of the statistical moments of the input parameters, showing the role of each one in the overall capacity definition. The seismic demand is represented by response spectra; vulnerability analysis is carried out with respect to a certain number of random limit states. Fragility curves are derived taking into account the uncertainties of each quantity involved.

• Smart Structures and Systems
• /
• 제22권4호
• /
• pp.399-411
• /
• 2018

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

• 대기
• /
• 제22권2호
• /
• pp.149-161
• /
• 2012

Going by the research results of the past, of all the uncertainties resulting from the research on climate change, the uncertainty caused by the climate change scenario has the highest degree of uncertainty. Therefore, depending upon what kind of climate change scenario one adopts, the projection of the water resources in the future will differ significantly. As a matter of principle, it is highly recommended to utilize all the GCM scenarios offered by the IPCC. However, this could be considered to be an impractical alternative if a decision has to be made at an action officer's level. Hence, as an alternative, it is deemed necessary to select several scenarios so as to express the possible number of cases to the maximum extent possible. The objective standards in selecting the climate change scenarios have not been properly established and the scenarios have been selected, either at random or subject to the researcher's discretion. In this research, a new scenario selection process, in which it is possible to have the effect of having utilized all the possible scenarios, with using only a few principal scenarios and maintaining some of the uncertainties, has been suggested. In this research, the use of cluster analysis and the selection of a representative scenario in each cluster have efficiently reduced the number of climate change scenarios. In the cluster analysis method, the K-means clustering method, which takes advantage of the statistical features of scenarios has been employed; in the selection of a representative scenario in each cluster, the selection method was analyzed and reviewed and the PDF method was used to select the best scenarios with the closest simulation accuracy and the principal scenarios that is suggested by this research. In the selection of the best scenarios, it has been shown that the GCM scenario which demonstrated high level of simulation accuracy in the past need not necessarily demonstrate the similarly high level of simulation accuracy in the future and various GCM scenarios were selected for the principal scenarios. Secondly, the "Maximum entropy" which can quantify the uncertainties of the climate change scenario has been used to both quantify and compare the uncertainties associated with all the scenarios, best scenarios and the principal scenarios. Comparison has shown that the principal scenarios do maintain and are able to better explain the uncertainties of all the scenarios than the best scenarios. Therefore, through the scenario selection process, it has been proven that the principal scenarios have the effect of having utilized all the scenarios and retaining the uncertainties associated with the climate change to the maximum extent possible, while reducing the number of scenarios at the same time. Lastly, the climate change scenario most suitable for the climate on the Korean peninsula has been suggested. Through the scenario selection process, of all the scenarios found in the 4th IPCC report, principal climate change scenarios, which are suitable for the Korean peninsula and maintain most of the uncertainties, have been suggested. Therefore, it is assessed that the use of the scenario most suitable for the future projection of water resources on the Korean peninsula will be able to provide the projection of the water resources management that maintains more than 70~80% level of uncertainties of all the scenarios.

• 자원환경지질
• /
• 제41권6호
• /
• pp.763-771
• /
• 2008

불확실성은 사면의 안정성을 해석하는 과정에서 특성자료의 부족이나 지질공학적 특성의 공간적 변동성 등의 원인으로 포함되며 따라서 불확실성으로 인해 변수들의 정확한 값을 획득하기 힘들게 된다 이러한 문제점을 해결하기 위하여 확률론적 해석기법이 활용되어 왔으며 최근에는 퍼지집합이론(fuzzy set theory)을 이용한 해석기법이 활용되고 있다. 특히 확률변수들의 자료 양이 제한적인 경우 변수의 확률특성을 정확하게 파악하기 힘들어 확률론적 해석기법의 활용이 제한적일 수 있으며 이러한 경우 퍼지집합이론은 확률변수의 특성을 효과적으로 표현할 수 있다. 본 연구에서는 암반사면의 안정성 해석과정에서 포함되는 불확실성을 정량화하기 위해 퍼지신뢰도척도(fuzzy reliability measure)를 활용하여 분석을 수행하였으며 특히 암반사면의 안정성에 영향을 미치는 여러 지질공학적 특성중 불연속면의 경사와 내부마찰각을 삼각형 퍼지숫자(fuzzy number)로 해석하였다 이를 위하여 연구대상사면을 선정하여 암반사면에서 발생하는 평면파괴를 대상으로 분석을 수행하였다. 퍼지신뢰도(fuzzy reliability) 해석에서는 퍼지숫자에 대한 퍼지 연산을 통해 퍼지신뢰도 지수(fuzzy reliability index)를 획득하였으며 이러한 결과를 확률론적 해석 결과와 비교하기 위하여 몬테카를로모사기법(Monte Carlo simulation)과 점추정법(point estimate method)을 이용한 확률론적 해석을 수행하였다. 해석결과 불충분한 자료 등으로 인해 불확실성의 정량화가 어려운 경우 퍼지신뢰도 해석을 통해 적절한 퍼지신뢰도 지수와 파괴확률을 획득할 수 있을 것으로 판단된다.