Bellows expansion joints enhance the displacement performance of piping systems owing to their unique geometrical features. However, structural uncertainties such as wall thinning in convolutions, a byproduct of the manufacturing process, can impair their structural integrity. This study addresses such issues by conducting a global sensitivity analysis to assess the impact of these uncertainties on the performance of bellows expansion joints under monotonic loading. Global sensitivity analysis, which examines main and nth order interaction effects, is computationally expensive. To mitigate this, we employed a surrogate model-based approach using an artificial neural network. This model demonstrated robust prediction capabilities, as evidenced by metrics such as the coefficient of determination. The sensitivity indices of the main effect for the 2-ply and 3-ply bellows at the sixth convolution were 0.3340 and 0.3233, respectively. The sensitivity index of the sixth convolution was larger than that of other convolutions because the maximum deformation of the bellows expansion joint under monotonic bending load occurs around it. Interestingly, the sensitivity index for the interaction effect was negligible (0.01%) compared to the main effect, suggesting minimal activity between uncertainty factors across convolutions. Notably, bellows expansion joints under repetitive loading exhibit more complex behaviors, with the initial leakage typically occurring at the convolution. Therefore, future studies should focus on the structural uncertainties of bellows expansion joints under cyclic loading and employ a surrogate model for comprehensive global sensitivity analysis.

This paper describes the design of H-infinity controller for robust control of a DC motor system. The suggested controller can ensure robustness against disturbance and model uncertainty by minimizing H-infinity norm of the transfer function from exogenous input to performance output and applying the small gain theorem. In particular, the controller was designed to reduce the effects of disturbance and model uncertainty simultaneously by formalizing these problems as a mixed sensitivity problem. The validity of the proposed controller was demonstrated by computer simulations and real experiments. Moreover, the effectiveness of the proposed controller was confirmed by comparing its performance with PI controller, which was tested under the same experimental condition as the H-infinity controller.

콘크리트충전강관(Concrete Filled Steel Tube, CFST) 기둥 설계 시, 강관의 국부좌굴을 방지하기 위하여 강관두께 t에 대한 기둥외경 D의 크기를 제한하고 있다. 각각의 설계시방서에서 각기 다른 최대 D/t 값을 제안하고 있으며, 강재의 항복응력 fy, 또는 fy와 강재의 탄성계수 E의 식으로 표현된다. fy와 E의 불확실성을 고려할 경우, 최대 D/t 계산식을 포함한 한계 상태함수(limit state function)를 구성하여 CFST 단면의 국부좌굴에 대한 신뢰성지수(reliability index) β를 산정할 수 있다. 결정된 β는 사용된 최대 D/t 계산식에 따라 달라질 것이다. 이러한 신뢰성해석(reliability analysis) 결과의 가변성(variability)은 한계상태함수에 포함되는 전산모델 선택의 모호함(ambiguity)에서 기인한 것으로 모델링불확실성(modelling uncertainty)이라 한다. 모델링불확실성은 정보적불확실성(epistemic uncertainty)의 하나로 알려진 불명확성(non-specificity)으로 고려할 수 있으며, 불명확성은 가능성분포함수(possibility distribution function)를 사용하여 모델링할 수 있다. 본 연구에서는 다른 3개의 최대 D/t 계산식을 사용하여 주어진 CFST 단면의 국부좌굴에 대한 신뢰성해석을 수행하고 각각의 신뢰성지수를 계산할 것이다. 불명확한 신뢰성지수들은 가능성분포함수를 사용하여 모델링되고, 이로부터 확신정도(degree of confirmation)를 측정할 것이다. 확신정도는 신뢰성지수가 감소하면 증가한다. 결과적으로, 확신정도에 따라 재구성된 신뢰성지수들을 얻을 수 있으며, 허용 확신정도와 함께 CFST 단면의 국부좌굴에 대한 신뢰성지수의 결정이 가능하게 된다.

교량의 손상추정을 위한 구조계 규명기법은 신호취득시스템 및 정보처리기술의 발전과 함께 최근에 많은 연구개발이 이루어지고 있다. 신경망기법이나 유전자 알고리즘과 같은 소프트컴퓨팅 기법은 뛰어난 패턴인식성능 때문에 손상추정 문제에 활발히 활용되고 있다. 본 연구에서는 모드계수를 활용한 신경망기법기반 손상추정을 수행하였으며, 신경망을 훈련시키기 위한 훈련패턴을 생성하는 해석모델에서의 불확실성을 효과적으로 고려할 수 있는 방법을 제시하였다. 해석모델의 불확실성 대하여 민감하지 않은 입력자료인 손상 전 후의 모드형상의 차 또는 모드형상의 비를 신경망의 입력자료로 활용하였다. 단 순보와 다주형교량에 대한 수치예제를 통하여 본 연구에서 제시한 기법의 타당성 및 적용성을 검증하였다.

This study analyzed future projections on daily mean values and extremes for temperature and daily precipitation over Seoul metropolitan city using bias-corrected high-resolution multi-regional climate models. The factors of uncertainty for the future projection of climate variables were defined. In the time series analysis of future projections for regional climate models, the average daily temperature and the number of days of the hot day-hot night were predicted to have a stable trend in the RCP2.6 scenario, and showed a tendency to increase continuously in the RCP8.5 scenario. The daily mean precipitation and RX1day (annual daily maximum precipitation) had large annual variabilities in the models. In the estimation of the fraction of total variance, the daily mean temperature was dominated by the internal variability in the early 21st century and the most contributing to the scenario uncertainty in the late 21st century. The daily mean precipitation showed a remarkable contribution from the internal variability over the entire period. The number of days of the hot day-hot night showed a similar contribution pattern to that of the daily mean temperature. For the RX1day, the internal variability dominated over the entire period, and the scenario uncertainty had little contribution. This study will help establish more scientific climate change adaptation policies by providing the uncertainty information for future climate change projection.

In this study, uncertainty ranges for bias-corrected temperature and precipitation in seven metro-cities were estimated using nine GCM-RCM Matrix, and climate changes were predicted based on the corrected temperature and precipitation. During the present climate (1981-2005), both uncertainties for annual temperature and precipitation and differences in regional uncertainties were reduced by bias correction methods. Model’s systematic errors such as cold bias of surface air temperature and underestimated precipitation during the second-Changma period were improved by a bias correction method. Uncertainties of annual variations for bias corrected temperature and precipitation were also decrease. Furthermore, not only mean values but also extreme values were improved by bias correction methods. During the future climate (2021-2050), differences in temperature and precipitation between two RCP scenarios (RCP4.5/8.5) were not quite large. Temperature had an obvious increasing tendency, while future precipitation did not change significantly compared to present one in terms of mean values. Uncertainties for future biascorrected temperature and precipitation were also reduced. In mid-21st centuries, models prospected that mean temperature increased thus lower extremes associated with cold wave decreased and upper extremes associated with heat wave increased. Models also predicted that variations of future precipitation increased thus the frequency and intensity of extreme precipitation increased.

과거에는 생애주기에 기반 유지관리 계획에 대한 인식이 부족하였기 때문에 검측자료의 축적은 이루어졌으나 이러한 검측 자료를 이용한 구성품의 수명예측 및 보수보강 시나리오 선정 등 유지관리 의사결정 지원을 위해 사용되지는 못하였다. 이에 본 연구에서는 자료 분석을 위한 궤도 검측 데이터 필터링 및 정제기법을 개발하고, 검측데이터 분석 기법 적용을 통한 궤도의 성능 평가 지표 결정, 다변수 구간특성 및 환경인자를 고려한 레일 마모 및 궤도 틀림에 대한 민감도 분석, 파형과 파장을 고려한 검측데이터 분석 등을 수행하였으며, 이러한 연구 결과를 기반으로 하여 검측된 레일 마모데이터를 이용한 불확실성 기반 궤도성능 예측모델 개봘과 관련한 연구를 수행하였다.

과거에는 생애주기에 기반 유지관리 계획에 대한 인식이 부족하였기 때문에 검측자료의 축적은 이루어졌으나 이러한 검측 자료를 이용한 구성품의 수명예측 및 보수보강 시나리오 선정 등 유지관리 의사결정 지원을 위해 사용되지는 못하였다. 따라서 축적된 검측 데이터로부터 궤도 구성품의 건전도를 평가할 수 있는 방법을 정립하고 잔존수명을 예측하여 효율적 유지관리를 실현할 수 있는 기법 개발의 필요성이 대두되고 있다. 이에 본 연구에서는 검측된 레일 마모데이터를 이용한 불확실성 기반 궤도성능 예측모델 개봘과 관련한 연구를 수행하였다.

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.