• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.981-981
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• 2012

최근 지구환경 변화에 따른 기후변화의 영향으로 자연재해의 형태는 점차 대형화, 다양화되고 있으며 극치사상의 발생 빈도가 계속해서 증가하고 있는 추세이다. 특히 도시하천의 경우 인구와 재산이 밀집해 있어 기후변화에 따른 홍수위험 및 취약성이 클 것으로 사료된다. 본 연구에서는 기후 변화에 따른 홍수위험 및 취약성 분석을 위하여 위험도 기반 불확실성을 다루는 수단으로 UQR-MCS (Upper Quartile Range-Monte Carlo Simulation)을 적용하였으며, 다양한 형태의 확률 분포로부터 특정변량(variable)의 확률분포 Quartile을 모의하였다. 또한 기후변화에 따른 도시하천의 홍수위험 및 취약성 평가를 위하여 도시하천에 적합한 홍수위험 및 취약성평가 지수(FVI: flood vulnerability index)를 산정하였으며, 홍수취약성지수는 기후변화(Climate change)와 도시화(Urbanization), 제방월류위험(Overtopping risk) 및 홍수범람 면적(Flood area) 등의 지표를 사용하였다. 각각의 지표는 엔트로피(Entropy) 기법을 적용하여 가중치를 부여하였으며, 표준화과정을 통한 일반화된 지표 값을 산정하였다. 우이천 유역의 기후변화에 따른 홍수위험 및 취약성 지표값은 KMA RCM A1B 시나리오자료를 바탕으로 추정한 미래 확률강수량과 각 인자별 재현기간에 따른 수문변량의 변화를 통하여 산정하였다. 본 연구의 결과는 향후 도시하천의 기후변화에 따른 홍수위험도분석 및 취약성 평가, 극치 수문사상에 대한 신뢰성 있는 분석과 더불어 예상치 못할 이상홍수에 대비한 하천방재 연구에 도움이 되리라 사료된다.

• Journal of the Korea Concrete Institute
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• v.21 no.6
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• pp.681-688
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• 2009

The railroad bridges have been usually experienced by vibration and impact in service state. With this reason, it is important that the effective strengthening capacity should be considered to resist the kind of service loading. In this study, NSM strengthening technique is recommended for the concrete railroad bridge because of its better effective resistance for dynamic loading condition and strengthening cost than the conventional externally bonded strengthening using fiber sheet. However, to widely apply NSM method for the concrete railroad bridge, it needs that the strengthening ratio has to be reasonably evaluated with geometrical and material uncertainties, especially for the concrete bridge under long-term service state without the apparent design history and detail information such as concrete compressive strength, reinforcing ratio, railroad characteristics. The purpose of this study is to propose the critical strengthening ratio of CFRP plate for the targeted concrete railroad bridge with uncertainties of deterioration of the structures. To do this, Monte Carlo Simulation (MCS) for geometrical and material uncertainties have been applied so that this approach may bring the reasonable strengthening ratio of CFRP plate considering probabilistic uncertainties for the targeted concrete railroad bridge. Finally, the critical strengthening ratio of NSM strengthened by CFRP plate is calculated by using the limit state function based on the target reliability index of 3.5.

• Journal of Korean Society of Steel Construction
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• v.20 no.1
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• pp.21-31
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• 2008

response surface method (RSM) is widely used to evaluate th e extremely smal probability of ocurence or toanalyze the reliability of very complicated structures. Althoug h Monte-Carlo Simulation (MCS) technique can evaluate any system, the procesing time of MCS dependson the reciprocal num ber of the probability of failure. The stochastic finite element method could solve thislimitation. However, it is limit ed to the specific program, in which the mean and coeficient o f random variables are programed by a perturbation or by a weigh ted integral method. Therefore, it is not aplicable when erequisite programing. In a few number of stage analyses, RSM can construct a regresion model from the response of the c omplicated structural system, thus, saving time and efort significantly. However, the acuracy of RSM depends on the dist ance of the axial points and on the linearity of the limit stat e functions. To improve the convergence in exact solution regardl es of the linearity limit of state functions, an improved adaptive response surface method is developed. The analyzed res ults have ben verified using linear and quadratic forms of response surface functions in two examples. As a result, the be st combination of the improved RSM techniques is determined and programed in a numerical code. The developed linear adapti ve weighted response surface method (LAW-RSM) shows the closest converged reliability indices, compared with quadratic form or non-adaptive or non-weighted RSMs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.884-891
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• 2020

The collapse of bridges can cause massive casualties and economic losses. Therefore, it is thus essential to evaluate the structural safety of bridges. For this task, structural reliability analysis, considering various bridge-related uncertainty factors, is often used. This paper proposes a new computational platform to perform structural reliability analysis for bridges and evaluate their structural safety under various loading conditions. For this purpose, a software package of reliability analysis, Finite Element Reliability Using MATLAB (FERUM), was integrated with MIDAS/CIVIL, which is a widely-used commercial software package specialized for bridges. Furthermore, a graphical user interface (GUI) control module has been added to FERUM to overcome the limitations of software operation. In this study, the proposed platform was applied to a simple frame structure, and the analysis results of the FORM (First-Order Reliability Method) and MCS (Monte Carlo simulation), which are representative reliability analysis methods, were compared. The proposed platform was verified by confirming that the calculated failure probability difference was less than 5%. In addition, the structural safety of a pre-stressed concrete (PSC) bridge was evaluated considering the KL-510 vehicle model. The proposed new structural reliability analysis platform is expected to enable an effective reliability-based safety evaluation of bridges.

• KIEAE Journal
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• v.10 no.3
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• pp.103-110
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• 2010

Carbonation is one of the major deterioration factors for concrete. So. lots of researchers have proposed the equations for determining carbonated depth and the initial time of steel corrosion due to carbonation to predict the service life of concrete structures. However, there are large gaps among the equations for predicting carbonation because each researcher has different considering factors to predict carbonation depth. So, in this study, we calculated the deviations of the proposed equations for carbonation, and we calculated each researcher different corrosion initiation time. However, it has a lot of deviation. Therefore, we evaluated the probability of steel corrosion considering each deviation using MCS, an analysis method based on probability theory. In the results, we have proposed much advanced information for determining service life of reinforced concrete structures due to carbonation.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.411-414
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• 2011

구조물에서의 소음은 일반적으로 볼트, 스냅, 힌지, 용접 등과 같은 체결부에서 발생한다. 이러한 소음을 정확히 예측하기 위해서는 구조물이 갖는 실제적인 동적특성(고유진동수, 주파수응답함수) 해석기술이 필수적으로 선행되어야 한다. 그러나 현재의 동적특성 해석기술은 체결부 특성을 무시하거나 많은 시간을 수반되는 상세모델을 적용하여 해석한다. 본 연구에서는 간단한 요소를 사용하여 체결부 상세모델을 대처할 수 있는 등가모델 개발을 수행하였고 실제모델과의 비교를 통해 타당성을 검증하였다. 또한, Monte-Carlo Simulation(MCS)을 사용하여 구조물의 확률기반 해석을 수행하였다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.11
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• pp.518-524
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• 2005

This paper presents an analytical method for the reliability evaluation of distribution system, including the distributed generations. Unlike the large sized generations of transmission system, the distributed generations have complexities in analyzing and determining the operation. In the process of evaluate reliability, it can be shown that the analytical method is simpler than the Monte-Carlo simulation and the method using Load Duration Curve model is more accurate than that using peak load model. The modeling of distributed generation to analysis distribution system reliability using LDC is proposed in this Paper, and is compared with the MCS method as a result of case studies.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.113-124
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• 2020

Social movements to improve the performance of buildings through remodeling of aging apartment houses are being captured. To this end, the remodeling construction cost analysis, structural analysis, and political institutional review have been conducted to suggest ways to activate the remodeling. However, although the method of analyzing construction cost for remodeling apartment houses is currently being proposed for research purposes, there are limitations in practical application possibilities. Specifically, In order to be used practically, it is applicable to cases that have already been completed or in progress, but cases that will occur in the future are also used for construction cost analysis, so the sustainability of the analysis method is lacking. For the purpose of this, we would like to suggest an automated estimating method. For the sustainability of construction cost estimates, Deep-Learning was introduced in the estimating procedure. Specifically, a method for automatically finding the relationship between design elements, work types, and cost increase factors that can occur in apartment remodeling was presented. In addition, Monte Carlo Simulation was included in the estimation procedure to compensate for the lack of uncertainty, which is the inherent limitation of the Deep Learning-based estimation. In order to present higher accuracy as cases are accumulated, a method of calculating higher accuracy by comparing the estimate result with the existing accumulated data was also suggested. In order to validate the sustainability of the automated estimates proposed in this study, 13 cases of learning procedures and an additional 2 cases of cumulative procedures were performed. As a result, a new construction cost estimating procedure was automatically presented that reflects the characteristics of the two additional projects. In this study, the method of estimate estimate was used using 15 cases, If the cases are accumulated and reflected, the effect of this study is expected to increase.

• Geomechanics and Engineering
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• v.24 no.2
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• pp.167-178
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• 2021

Spatial variability is an inherent uncertainty of soil properties. Current reliability analyses generally incorporate random field theory and Monte Carlo simulation (MCS) when dealing with spatial variability, in which the computational efficiency is a significant challenge. This paper proposes a KL-FORM algorithm to improve the computational efficiency. In the proposed KL-FORM, Karhunen-Loeve (KL) expansion is used for discretizing random fields, and first-order reliability method (FORM) is employed for reliability analysis. The KL expansion and FORM can be used in conjunction, through adopting independent standard normal variables in the discretization of KL expansion as the basic variables in the FORM. To illustrate the effectiveness of this KL-FORM, it is applied to a case study of a strip footing in spatially variable unsaturated soil under rainfall, in which the bearing capacity of the footing is computed by numerical simulation. This case study shows that the KL-FORM is accurate and efficient. The parametric analyses suggest that ignoring the spatial variability of the soil may lead to an underestimation of the reliability index of the footing.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1331-1344
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• 2021

This study is to evaluate the seismic capacity of the fire-damaged cabinet facility in a nuclear power plant (NPP). A prototype of an electrical cabinet is modeled using OpenSees for the numerical simulation. To capture the nonlinear behavior of the cabinet, the constitutive law of the material model under the fire environment is considered. The experimental record from the impact hammer test is extracted trough the frequency-domain decomposition (FDD) method, which is used to verify the effectiveness of the numerical model through modal assurance criteria (MAC). Assuming different temperatures, the nonlinear time history analysis is conducted using a set of fifty earthquakes and the seismic outputs are investigated by the fragility analysis. To get a threshold of intensity measure, the Monte Carlo Simulation (MCS) is adopted for uncertainty reduction purposes. Finally, a capacity estimation model has been proposed through the investigation, which will be helpful for the engineer or NPP operator to evaluate the fire-damaged cabinet strength under seismic excitation. This capacity model is presented in terms of the High Confidence of Low Probability of Failure (HCLPF) point. The results are validated by the proper judgment and can be used to analyze the influences of fire on the electrical cabinet.