scholarly journals A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks

Modelling ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 240-258
Author(s):  
Nima Khakzad
Keyword(s):  
Risk Assessment ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Synergistic Effects ◽  
Dynamic Bayesian Network ◽  
Event Tree ◽  
Domino Effect ◽  
Reasoning Under Uncertainty ◽  
Event Tree Analysis ◽  
Probabilistic Network

High complexity and growing interdependencies of chemical and process facilities have made them increasingly vulnerable to domino effects. Domino effects, particularly fire dominoes, are spatial-temporal phenomena where not only the location of involved units, but also their temporal entailment in the accident chain matter. Spatial-temporal dependencies and uncertainties prevailing during domino effects, arising mainly from possible synergistic effects and randomness of potential events, restrict the use of conventional risk assessment techniques such as fault tree and event tree. Bayesian networks—a type of probabilistic network for reasoning under uncertainty—have proven to be a reliable and robust technique for the modeling and risk assessment of domino effects. In the present study, applications of Bayesian networks to modeling and safety assessment of domino effects in petroleum tank terminals has been demonstrated via some examples. The tutorial starts by illustrating the inefficacy of event tree analysis in domino effect modeling and then discusses the capabilities of Bayesian network and its derivatives such as dynamic Bayesian network and influence diagram. It is also discussed how noisy OR can be used to significantly reduce the complexity and number of conditional probabilities required for model establishment.

DAM RISK ASSESSMENT USING THE EVENT TREE ANALYSIS AND BAYESIAN NETWORKS

2019 ◽  
Author(s):  
RAFAE ANTONIO BRISEÑO-RAMIRO ◽  
VÍCTOR HUGO ALCOCER-YAMANAKA ◽  
ADRIÁN PEDROZO-ACUÑA ◽  
JOSÉ AGUSTIN BREÑA-NARANJO ◽  
RAMÓN DOMÍNGUEZ-MORA
Keyword(s):  
Risk Assessment ◽  
Bayesian Networks ◽  
Event Tree ◽  
Event Tree Analysis ◽  
Tree Analysis ◽  
Dam Risk

Challenges and Developments in Navigational Risk Assessment With Large Uncertainty

2014 ◽  
Author(s):  
Xinping Yan ◽  
Jinfen Zhang ◽  
Di Zhang ◽  
Carlos Guedes Soares
Keyword(s):  
Risk Assessment ◽  
Safety Assessment ◽  
Oil Spills ◽  
Rule Base ◽  
Event Tree ◽  
Event Tree Analysis ◽  
General Application ◽  
The Past ◽  
Formal Safety Assessment ◽  
Belief Rule Base

Concerns have been raised to navigational safety worldwide because of the increasing throughput and the passing ships during the past decades while maritime accidents such as collisions, groundings, overturns, oil-spills and fires have occurred, causing serious consequences. Formal Safety Assessment (FSA) has been acknowledged to be a framework widely used in maritime risk assessment. Under this framework, this paper discusses certain existing challenges when an effective safety assessment is carried out under a variety of uncertainties. Some theories and methodologies are proposed to overcome the present challenges, e.g., Fault/Event Tree Analysis (FTA/ETA), Evidential Reasoning (ER), Bayesian Belief Network (BBN) and Belief Rule Base (BRB). Subsequently, three typical case studies that have been carried out in the Yangtze River are introduced to illustrate the general application of those approaches. These examples aim to demonstrate how advanced methodologies can facilitate navigational risk assessment under high uncertainties.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

scholarly journals The engineering skills training process modeling using dynamic bayesian nets

2021 ◽  
pp. 87-96
Author(s):  
Andrey Chukhray ◽  
Olena Havrylenko
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Learning Process ◽  
Probabilistic Inference ◽  
Dynamic Bayesian Network ◽  
Computer Training ◽  
Skills Training ◽  
Dynamic Bayesian Networks ◽  
Competence Level ◽  
Intelligent Computer

The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.

scholarly journals Risk assessment of climate change impacts and the post-mining land reclamation on the example of the “Brzeziny” Natural Aggregate Mine

2018 ◽  
Vol 50 (1) ◽  
pp. 43-54
Author(s):  
Katarzyna Pawełczyk
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Land Reclamation ◽  
Climate Change Impacts ◽  
Mining Area ◽  
Event Tree ◽  
Event Tree Analysis ◽  
Analytic Hierarchy ◽  
Adaptation Measures ◽  
Optimal Method

Abstract Mitigation of climate change and adaptation to its effects manifested in increasingly extreme meteorological phenomena are one of the most important contemporary global challenges. In the face of new hazards, numerous measures are taken to adapt environmental components and ventures to climate change, such as the reclamation of degraded areas – recognized as a key adaptation and mitigation action. The success and the property of selecting these measures, including reclamation, requires a detailed recognition of the risk of occurrence of various hazards and of the severity of their consequences in a given area. The study assessed the risk of the climate change impacts on the post-mining area and based on its results an optimal method of reclamation of the “Brzeziny” gravel pit was proposed, aimed at the maximum adaptation of the area to the occurrence of potential climate events. The risk analysis was based on elements of the common risk assessment methodology (CRAM) and enriched with elements of the analytic hierarchy process method (AHP). Moreover, the event tree analysis (ETA) logic technique was used to assess the proposed adaptation measures at the reclamation stage.

Application of Bayesian Networks in Dynamic Event Tree Analysis

2011 ◽  
Vol 6 (7) ◽  
pp. 340-348 ◽  
Author(s):  
Zhongbao Zhou ◽  
Xuan Zeng ◽  
Haitao Li ◽  
Siya Lui ◽  
Chaoqun Ma
Keyword(s):  
Bayesian Networks ◽  
Event Tree ◽  
Event Tree Analysis ◽  
Tree Analysis ◽  
Dynamic Event

Bayesian mutual information reliability model for fire risk assessment of high-rise buildings

2020 ◽  
pp. 002072091989419
Author(s):  
Jingjing Pei ◽  
Guantao Wang
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Quantitative Assessment ◽  
Fire Risk ◽  
Event Tree ◽  
Bayesian Network Model ◽  
Tree Model ◽  
High Rise ◽  
Fire Risk Assessment ◽  
Information Reliability

The Bayesian network method is introduced into the process of fire risk quantitative assessment. The event tree model is established, and the Bayesian network model is transformed from the event tree model based on the typical fire scenarios in high-rise space. A Bayesian fire risk assessment algorithm for high-rise buildings based on mutual information reliability is proposed. Bayesian network is modified considering the influence of uncertainties. Finally, the modified Bayesian network model is used to calculate the probability of fire developing to different stages, and the estimated value of property loss is used to express the severity of the accident and calculate the fire risk value. The results show that the existence of uncertainties has a significant impact on the results of risk assessment; the quantitative assessment method based on Bayesian network is better than the ETA method based on event tree analysis in dealing with uncertainties and is more suitable for high-rise space fire risk assessment.

scholarly journals Study on risk assessment of expressway nighttime maintenance construction: A Dynamic Bayesian Network model

2021 ◽  
Vol 257 ◽  
pp. 02047
Author(s):  
Zhen Tian ◽  
Jinhua Fan ◽  
Qianqian Chen ◽  
Huaichen Hu ◽  
Yanyang Shen
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Bayesian Network ◽  
Traffic Accidents ◽  
Dynamic Bayesian Network ◽  
Extended Model ◽  
Time Varying ◽  
Bayesian Network Model ◽  
Change Curve ◽  
Or Gate

There are many risk factors and large uncertainties in expressway nighttime maintenance construction(ENMC), and the state of risk factors will change dynamically with time. In this study, a Dynamic Bayesian Network (DBN) model was proposed to investigate the dynamic characteristics of the time-varying probability of traffic accidents during expressway maintenance at night. Combined with Leaky Noisy-or gate extended model, the calculation method of conditional probability is determined . By setting evidences for DBN reasoning, the time series change curve of the probability of traffic accidents and other risk factors are obtained. The results show that DBN can be applied to risk assessment of ENMC.

scholarly journals Methodology for tunnel risk assessment using fault and event tree analysis

2021 ◽  
Author(s):  
Zlatko Zafirovski ◽  
Vasko Gacevski ◽  
Zoran Krakutovski ◽  
Slobodan Ognjenovic ◽  
Ivona Nedevska
Keyword(s):  
Risk Assessment ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Economic Consequences ◽  
Event Tree ◽  
Event Tree Analysis ◽  
Tree Analysis ◽  
Geological Conditions ◽  
Hazard And Risk ◽  
Complex Solutions

The intense demand and construction of tunnels is accompanied by uncertainties. The reason for appearance of uncertainties are the complex solutions and conditions for these structures. Location and dimensions are becoming more challenging, and the construction is predicted in complexed geological conditions, leading to application of new approaches, methodologies and technologies by the engineers. Most of the uncertainties and unwanted events in tunnelling occur in the construction phase, which generally leads to economic consequences and time losses. For easier handling of the uncertainties, they should be anticipated and studied within a separate part of each project. One of the newer approaches to dealing with uncertainties is hazard and risk assessment and defining ways to deal with them i.e. management. Hazards and risks can be analysed qualitatively and quantitatively. The quantitative analysis, examines the causes and consequences in more detail way and gives explanation of the dependencies. With the quantitative approach, a more valuable information for decision-making can be provided. There are various models and methods used for the quantification of hazards and risks. This paper presents a methodology in which the fault tree analysis and event tree analysis are used in combination to obtain quantitative results. The fault tree analysis is used for assessment of various hazards and the different ways and reasons that cause them. The event tree analysis is a method for assessing the possible scenarios, which follow after a certain hazard i.e. the consequences that may occur in the project. These trees represent graphic models combined with a mathematical (probabilistic) model, which give the probability of occurrence of the risks.

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