BN Applications in Operational Risk Analysis: Scope, Limitations and Methodological Requirements

Trucco, Paolo; Leva, Maria Chiara

doi:10.5772/38858

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…The reason that Bayesian networks are suitable for marine modeling is because of their ability to utilize expert knowledge when there is a shortage of data. Bayesian networks are a way of expressing domain knowledge, and explicitly include probabilistic dependence and causal relationships between major factors [31]. Bayesian network learning is considered to be evidence-based because the models are constructed from actual accident data [32].…”

Section: Expert Panel Opinionsmentioning

confidence: 99%

Bayesian Network Analysis of Industrial Accident Risk for Fishers on Fishing Vessels Less Than 12 m in Length

Lee,

Kim,

Ryu

et al. 2024

Sustainability

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The Marine Stewardship Council estimates that approximately 38 million people worldwide work in fisheries, and more than one-third of the global population is dependent on aquatic products for protein, highlighting the importance of sustainable fisheries. The FISH Safety Foundation reports that 300 fishers die every day. To achieve sustainable fisheries as a primary industry, the safety of human resources is of the utmost importance. The International Maritime Organization (IMO) and the International Labor Organization (ILO) have made efforts towards this goal, including the issuance of agreements and guidelines to reduce industrial accidents among fishing vessel workers. The criterion for applying these guidelines is usually a total ship length ≥12 m or ≥24 m. However, a vast majority of registered fishing vessels are <12 m long, and the fishers of these vessels suffer substantially more industrial accidents. Thus, we conducted a quantitative analysis of 1093 industrial accidents affecting fishers on fishing vessels <12 m in length, analyzed risk using a Bayesian network analysis (a method proposed by the Formal Safety Assessment of the IMO), and administered a questionnaire survey to a panel of experts in order to ascertain the risk for different types of industrial accidents and propose specific measures to reduce this risk.

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Section: Expert Panel Opinionsmentioning

confidence: 99%

Bayesian Network Analysis of Industrial Accident Risk for Fishers on Fishing Vessels Less Than 12 m in Length

Lee,

Kim,

Ryu

et al. 2024

Sustainability

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show abstract

“…Reliability Engineering and System Safety 177 (2018) [176][177][178][179][180][181][182][183][184][185][186][187][188][189][190] disaster restoration of various infrastructure systems in an effort to estimate the expected restoration time [68,173], and several others have compared the performance of different restoration strategies [41,44]. Other works have tackled the problem of post-disaster restoration strategy planning and optimization, for the purpose of restoring system service in a timely and efficient manner.…”

Section: E Ziomentioning

confidence: 99%

“…• phenomenological/functional methods, based on transfer functions, state dynamic modeling, input-output modeling and control theory, agent-based modeling etc. ; these methods are capable of capturing the dynamics of interrelated operation between elements (hardware, software and human) of a complex system and with the environment, from which the dynamic operation of the system itself emerges [189];…”

Section: E Ziomentioning

confidence: 99%

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The future of risk assessment

Zio

2018

Reliability Engineering & System Safety

282

116

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A B S T R A C TRisk assessment must evolve for addressing the existing and future challenges, and considering the new systems and innovations that have already arrived in our lives and that are coming ahead. In this paper, I swing on the rapid changes and innovations that the World that we live in is experiencing, and analyze them with respect to the challenges that these pose to the field of risk assessment. Digitalization brings opportunities but with it comes also the complexity of cyber-phyiscal systems. Climate change and extreme natural events are increasingly threatening our infrastructures; terrorist and malevolent threats are posing severe concerns for the security of our systems and lives. These sources of hazard are extremely uncertain and, thus, difficult to describe and model quantitatively.Some research and development directions that are emerging are presented and discussed, also considering the ever increasing computational capabilities and data availability. These include the use of simulation for accident scenario identification and exploration, the extension of risk assessment into the framework of resilience and business continuity, the reliance on data for dynamic and condition monitoring-based risk assessment, the safety and security assessment of cyber-physical systems.The paper is not a research work and not exactly a review or a state of the art work, but rather it offers a lookout on risk assessment, open to consideration and discussion, as it cannot pretend to give an absolute point of view nor to be complete in the issues addressed (and the related literature referenced to).

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“…The ability to model dependencies among events with probabilistic data makes BN suitable of risk analysis (Weber et al, 2012). As pointed out by Trucco and Leva (2012), the use of BN in modelling operational risks provides a special advantage over many other approaches, since BN is the knowledge representation of the domain and can explicitly include probabilistic dependence among the main elements and their causal relationships. Meanwhile, the ability of BN to make use of the experts' knowledge is very valuable for maritime risk modelling considering the data scarcity in the maritime domain.…”

Section: Bn Validation Frameworkmentioning

confidence: 99%

Predictive risk modelling with bayesian network for maritime applications

Zhang¹

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BN Applications in Operational Risk Analysis: Scope, Limitations and Methodological Requirements

Cited by 5 publications

References 32 publications

Bayesian Network Analysis of Industrial Accident Risk for Fishers on Fishing Vessels Less Than 12 m in Length

Bayesian Network Analysis of Industrial Accident Risk for Fishers on Fishing Vessels Less Than 12 m in Length

The future of risk assessment

Predictive risk modelling with bayesian network for maritime applications

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