Quantitative risk analysis model of integrating fuzzy fault tree with Bayesian Network

2011 ◽  
Author(s):  
Yan Fu Wang ◽  
Min Xie ◽  
Kien Ming Ng ◽  
Yi Fei Meng
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Fault Tree ◽  
Analysis Model ◽  
Quantitative Risk Analysis

Cybersecurity risk analysis model using fault tree analysis and fuzzy decision theory

2018 ◽  
Vol 43 ◽  
pp. 248-260 ◽  
Author(s):  
Ana Paula Henriques de Gusmão ◽  
Maisa Mendonça Silva ◽  
Thiago Poleto ◽  
Lúcio Camara e Silva ◽  
Ana Paula Cabral Seixas Costa
Keyword(s):  
Risk Analysis ◽  
Decision Theory ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Fuzzy Decision ◽  
Analysis Model ◽  
Tree Analysis

scholarly journals Quantitative Risk Analysis of Offshore Fire and Explosion Based on the Analysis of Human and Organizational Factors

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Fu Wang ◽  
Yu Lian Li ◽  
Biao Zhang ◽  
Pei Na Yan ◽  
Li Zhang
Keyword(s):  
Risk Analysis ◽  
Organizational Factors ◽  
Event Tree ◽  
Analysis Framework ◽  
Analysis Model ◽  
Quantitative Risk Analysis ◽  
Dynamic Risk ◽  
Fire And Explosion ◽  
Human And Organizational Factors ◽  
Explosion Risk

A dynamic risk analysis model of offshore fire and explosion is proposed in this paper. It considers the effect of human and organizational factors in a more explicit way than current traditional risk analysis methods. This paper begins with exploring the recent advances on offshore fire and explosion risk analysis theories, followed by briefly introducing the research techniques employed in the proposed hybrid causal logic model which consists of event tree, fault tree, Bayesian network, and system dynamics. Thereafter, it proposes a quantitative risk analysis framework. At last, the applicability of this model to the offshore platform is also discussed. It aims to provide guideline for risk analysis of offshore fire and explosion.

A Development of Hydrologic Dam Risk Analysis Model Using Bayesian Network(BN)

2015 ◽  
Vol 48 (10) ◽  
pp. 781-791 ◽  
Author(s):  
Jin-Young Kim ◽  
◽  
Jin-Guk Kim ◽  
Byoung-Han Choi ◽  
Hyun-Han Kwon
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Analysis Model ◽  
Dam Risk

scholarly journals Risk Control Technology for Water Inrush during the Construction of Deep, Long Tunnels

2019 ◽  
Vol 2019 ◽  
pp. 1-21
Author(s):  
Yiming Liu ◽  
Yuanpu Xia ◽  
Hao Lu ◽  
Ziming Xiong
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Effective Means ◽  
Water Inrush ◽  
Risk Control ◽  
Risk Information ◽  
Economic Losses ◽  
Analysis Model ◽  
Control Scheme ◽  
Changes In Risk

Water inrush is one of the main disasters occurring during tunnel construction in complex geological areas: once it happens, it can cause economic losses, casualties, and delay. Based on risk analysis and management, a risk control scheme is proposed as an effective means to control the risk of such a disaster; however, there are some deficiencies in existing research because the impacts of human factors on the risk of water inrush, dynamic changes in risk information during construction, and the diversity of types of water inrush are neglected. To enrich the research results of water inrush risk control and improve the effect of water inrush risk control, we first use the advantages of Bayesian network to analyse risk events, construct a Bayesian network structure chart of water inrush risk during construction, and propose a fuzzy probability risk analysis model for water inrush. The model can quickly track changes in risk information and diagnose the cause of water inrush disasters while providing an early warning thereof. In addition, considering that the diversity of water inrush types leads to differences in water inrush mechanisms, we believe that the formulation of any water inrush risk control scheme must be combined with water inrush mechanism analysis; therefore, we take a nondefect generated water inrush in front of the tunnel as a representative case and analyse the possible mechanism of water inrush through the stability analysis of the water-resisting strata. Then, based on the results of risk analysis and an analysis of the water inrush mechanism, a reasonable risk control scheme for water inrush is derived.

Quantitative Risk Analysis of Hydrogen Events at WTP: Part 1 of 2 — The Operational Frequency Analysis Model

2012 ◽  
Author(s):  
C. Ray Lux ◽  
Kevin R. O’Kula ◽  
Michael G. Wentink ◽  
Ryan E. Jones ◽  
Jean E. Collin ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Frequency Analysis ◽  
Chemical Industry ◽  
Fault Tree ◽  
Piping System ◽  
Hanford Site ◽  
Quantitative Risk Analysis ◽  
Piping Systems ◽  
Operational Frequency ◽  
Pocket Formation

A Quantitative Risk Analysis (QRA) model has been developed to determine the frequency and severity (potential combustion loads) of postulated hydrogen event types in piping systems and proposed as a design-informing tool for the U.S. Department of Energy’s (DOE) Hanford Tank Waste Treatment and Immobilization Plant (WTP) at the Hanford Site near Richland, Washington. Specifically, the QRA provides a systematic, comprehensive methodology for assessing hydrogen events, including deflagrations, detonations, and deflagration-to-detonation transition (DDT) types in piping systems containing legacy nuclear waste streams being processed for vitrification. The events considered include normal operations as well as postulated upset conditions as a result of internal and external accidents. The QRA approach incorporates three sequential phases, including Operational Frequency Analysis (OFA), Gas Pocket Formation (GPF) and Event Progression Logic (EPL) models in the form of an integrated logic framework. The WTP piping design will be evaluated on a specific piping route basis using a probabilistic sampling approach, with the QRA providing the quantitative dynamic loads for evaluation according to the frequency and type of hydrogen event. The OFA is based on an industry standard fault tree computer model, CAFTA, and analyzes the frequency of combustible gas pocket formation in a piping system from three primary sources: (1) normal operations; (2) piping system-specific upset conditions affecting transfer operations; and (3) plant-wide initiating events such as fire and seismic accidents. A second output from the OFA is duration time for each event, quantifying the length of time that a gas pocket is likely to develop before the initiating event is terminated, with the information provided directly to QRA event tree models for assessing gas pocket growth. A team of safety, operations and engineering, developed the underlying logic of the fault tree model with the overall modeling approach following applicable nuclear/chemical industry guidance and standards for performing QRA applications. Primary inputs to the OFA module are initiating event, equipment reliability, and human/operator error data and their characteristic distributions, and are drawn from Hanford Site safety documentation, government and commercial sector sources, and related nuclear/chemical industry experience. This paper discusses the overall OFA module, its inputs, the outputs to the GPF and EPL modules, the relative importance of different initiating event conditions, key insights obtained to date, upcoming supporting uncertainty/sensitivity analyses, and summarizes technical peer review assessments.

Quantitative risk analysis of offshore well blowout using bayesian network

2021 ◽  
Vol 135 ◽  
pp. 105080
Author(s):  
Bangtang Yin ◽  
Boyao Li ◽  
Gang Liu ◽  
Zhiyuan Wang ◽  
Baojiang Sun
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Quantitative Risk Analysis

scholarly journals Risk Analysis of Tunnel Water and Mud Inrush Using Interpretive Structural Modeling and Fault Tree

2022 ◽  
Author(s):  
Min Han ◽  
Teng Xia ◽  
Maoxin Su ◽  
Yiguo Xue
Keyword(s):  
Risk Factors ◽  
Risk Analysis ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Structural Modeling ◽  
Geological Structure ◽  
Geological Hazard ◽  
Analysis Model ◽  
Interpretive Structural Modeling ◽  
Water And Mud Inrush

Abstract Water and mud inrush is a common geological hazard in tunnel construction. Risk analysis of tunnel water and mud inrush has always been an important subject. In order to avoid the geological hazard, this paper presents a risk analysis model of tunnel water and mud inrush. The model combines the interpretive structural modeling method (ISM) and fault tree analysis (FTA). Relying on the Qinyu tunnel in the Weiwu expressway project, water and mud inrush risk factors are obtained by using ISM. Fundamental risk factors include formation lithology, attitude of stratum, strata combination, topography and geomorphology, geological structure and weather. ISM core risk factors are used as FTA basic events. Fuzzy importance of FTA basic events is obtained by using fuzzy interval calculation. The results show that geological structure is the primary risk factor causing Qinyu tunnel water and mud inrush. The model achieves qualitative and quantitative analysis of tunnel water and mud inrush. It accurately determines the main factors affecting the tunnel water and mud inrush, which is conducive to accident prevention.

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