• 한국전산구조공학회논문집
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• 제36권4호
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• pp.273-282
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• 2023

원자력발전소 지진 확률론적 안전성 평가인 PSA(Probabilistic Safety Assessment)는 오랜 기간에 걸쳐 확고히 구축되어 왔다. 반면에 다양한 공정 기반의 산업시설물의 경우 화재, 폭발, 확산(유출) 재난에 대해 주로 연구되어 왔으며, 지진에 대해서는 상대적으로 연구가 미미하였다. 하지만, 플랜트 설계 당시와 달리 해당 부지가 지진 영향권에 들어갈 경우 지진 PSA 수행은 필수적이다. 지진 PSA를 수행하기 위해서는 확률론적 지진 재해도 해석(Probabilistic Seismic Hazard Analysis), 사건수목 해석(Event Tree Analysis), 고장수목 해석(Fault Tree Analysis), 취약도 곡선 등을 필요로 한다. 원자력 발전소의 경우 노심 손상 방지라는 최우선 목표에 따라 많은 사고 시나리오 분석을 통해 사건수목이 구축되었지만, 산업시설물의 경우 공정의 다양성과 최우선 손상 방지 핵심설비의 부재로 인해 일반적인 사건수목 구축이 어렵다. 따라서, 본 연구에서는 산업시설물 지진 PSA를 수행하기 위해 고장수목을 바탕으로 확률론적 시각도구인 베이지안 네트워크(Bayesian Network, BN)로 변환하여 리스크를 평가하는 방법을 제안한다. 제안된 방법을 이용하여 임의로 생성된 가스플랜트 Plot Plan에 대해 최종 BN을 구축하고, 다양한 사건 경우에 대한 효용성있는 의사결정과정을 보임으로써 그 우수성을 확인하였다.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.119-130
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• 2023

Among the various elements of probabilistic safety assessment (PSA), human failure events (HFEs) and their dependencies are major contributors to the quantification of risk of a nuclear power plant. Currently, the dependency among HFEs is reflected using a post-processing method in PSA, wherein several drawbacks, such as limited propagation of minimal cutsets through the fault tree and improper truncation of minimal cutsets exist. In this paper, we propose a method to model the HFE dependency directly in a fault tree using the if-then-else logic. The proposed method proved to be equivalent to the conventional post-processing method while addressing the drawbacks of the latter. We also developed a software tool to facilitate the implementation of the proposed method considering the need for modeling the dependency between multiple HFEs. We applied the proposed method to a specific case to demonstrate the drawbacks of the conventional post-processing method and the advantages of the proposed method. When applied appropriately under specific conditions, the direct fault-tree modeling of HFE dependency enhances the accuracy of the risk quantification and facilitates the analysis of minimal cutsets.

• 한국수자원학회논문집
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• 제53권2호
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• pp.121-129
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• 2020

도시화로 인한 불투수층 증가와 하천 주변 개발은 홍수 시 위험에 노출되는 재해요인의 증가뿐 아니라 피해의 파급을 발생시켜 홍수 관리 측면에서 어려움을 낳는다. 홍수 방재대책을 위해서는 도시지역에 분포하는 다양한 지표면 공간특성을 반영하여 침수가 예상되는 지역에 대한 파악이 우선시되어야 한다. 본 연구에서는 도시하천의 홍수 위험지역을 대상으로 확률적 홍수위험 평가가 수행되었다. 홍수와 관련된 지형적 영향요인인 고도, 경사, 유출곡선지수, 하천까지 거리를 예측변수로 하여 하천 주변 침수 예상지역을 설명하기 위해 모형의 학습데이터로 100년 빈도 홍수위험 지도가 사용되었다. 연구 대상 지역은 격자로 변환하여 Bayesian Logistic 회귀분석을 수행하여 각 격자별로 홍수영향요인이 침수 여부를 설명하는 모형을 구축하였다. 최종적으로 모형을 통해 대상 지역 전체에 대하여 침수위험도를 확률적으로 제시하였다.

• Nuclear Engineering and Technology
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• 제19권4호
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• pp.249-265
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• 1987

고준위 방사성폐기물 처분장에 대한 확률론적 위험도 평가를 위해 지금까지 많은 방법들이 제안되어 왔다. 이 계는 많은 불확실성을 갖는 입력 변수들을 갖고 있어서 이 입력변수들에 대해 계산된 위험도 역시 많은 불착실성을 갖는다. 본 논문에서는 이러한 점들을 조직적으로 분석하기 위하여 여러가지 불확실도 및 민감도 분석 방법들이 개발되었고 고준위 폐기물 처분장의 위험도 평가에 적용되었다. 본 논문을 통해 개발된 통계 패키지 SPUSA는 통계적 열여유도 분석, 방사선원 불확실도 분석등 등의 분야에도 사용될 수 있다.

• 대한안전경영과학회지
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• 제10권4호
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• pp.93-104
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• 2008

A successful fire risk assessment is depends on identification of risk, the analytical process of potential risk, on estimation of likelihood and the width and depth of consequence. Take the influence on enterprise into consideration, Fire risk assessment could carry out along the evaluation of the risk importance, the risk level and the risk acceptance. A large part of the limitation of choosing the risk assessment techniques impose restrictions on expense and time. If it is unnecessary high level risk assessment or Probabilistic Risk Assessment of buildings, in compliance with the Relative Ranking Method, Fire risk indexing and assessing is possible. As working-level technique, AHP method is useful with practical technique.

• 산업경영시스템학회지
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• 제38권1호
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• pp.83-89
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• 2015

Selection of efficient supplier is a very important process as risk or uncertainty of a supply chain and its environment are increasing. Previous deterministic DEA and probabilistic DEAs are very limited to handle various types of risk and uncertainty. In this paper, I propose an improved probabilistic DEA which consists of two steps; Monte Carlo simulation and statistical decision making. The simulation results show that the proposed method is proper to distinguish supplier's performance and provide statistical decision background.

• Wind and Structures
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• 제13권2호
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• pp.191-206
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• 2010

Windborne debris is a major cause of structural damage during severe windstorms and hurricanes owing to its direct impact on building envelopes as well as to the 'chain reaction' failure mechanism it induces by interacting with wind pressure damage. Estimation of debris risk is an important component in evaluating wind damage risk to residential developments. A debris risk model developed by the authors enables one to analytically aggregate damage threats to a building from different types of debris originating from neighboring buildings. This model is extended herein to a general debris risk analysis methodology that is then incorporated into a vulnerability model accounting for the temporal evolution of the interaction between pressure damage and debris damage during storm passage. The current paper (Part I) introduces the debris risk analysis methodology, establishing the mathematical modeling framework. Stochastic models are proposed to estimate the probability distributions of debris trajectory parameters used in the method. It is shown that model statistics can be estimated from available information from wind-tunnel experiments and post-damage surveys. The incorporation of the methodology into vulnerability modeling is described in Part II.

• Earthquakes and Structures
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• 제25권6호
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• Geomechanics and Engineering
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• 제37권6호
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• pp.565-576
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• 2024

In this study, the earthquake risk assessment of single-story RC precast buildings in Turkey was carried out using loss curves. In this regard, Kocaeli, a seismically active city in the Marmara region, and this building class, which is preferred intensively, were considered. Quality and period parameters were defined based on structural and geometric properties. Depending on these parameters, nine main sub-classes were defined to represent the building stock in the region. First, considering the mean fragility curves and four different central damage ratio models, vulnerability curves for each sub-class were computed as a function of spectral acceleration. Then, probabilistic seismic hazard analyses were performed for stiff and soft soil conditions for different earthquake probabilities of exceedance in 50 years. In the last step, 90 loss curves were derived based on vulnerability and hazard results. Within the scope of the study, the comparative parametric evaluations for three different earthquake intensity levels showed that the structural damage ratio values for nine sub-classes changed significantly. In addition, the quality parameter was found to be more effective on a structure's damage state than the period parameter. It is evident that since loss curves allow direct loss ratio calculation for any hazard level without needing seismic hazard and damage analysis, they are considered essential tools in rapid earthquake risk estimation and mitigation initiatives.

• Nuclear Engineering and Technology
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• 제53권2호
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• pp.376-385
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• 2021

Probabilistic risk assessment (PRA) has improved its elemental technologies used for assessing external events since the Fukushima Daiichi Nuclear Power Station Accident in 2011. HRA needs to be improved for analyzing tasks performed under extreme conditions (e.g., different actors responding to external events or performing operations using portable mitigation equipment). To make these improvements, it is essential to understand plant-specific and scenario-specific conditions that affect human performance. The Nuclear Risk Research Center (NRRC) of the Central Research Institute of Electric Power Industry (CRIEPI) has developed an HRA guide that compiles qualitative analysis methods for collecting plant-specific and scenario-specific conditions that affect human performance into "narratives," reflecting the latest research trends, and models for analysis of tasks under extreme conditions.