Risk assessment of the Ship steering gear failures using fuzzy-Bayesian networks

Göksu, Burak; Yüksel, Onur; Şakar, Cenk

doi:10.1016/j.oceaneng.2023.114064

Cited by 30 publications

(2 citation statements)

References 48 publications

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“…These networks are capable of dynamically updating probabilities in response to new information and facilitating causal reasoning within complex risk systems. Such capabilities render BNs invaluable for conducting more precise and dynamic risk assessments in safety evaluation and risk analysis [32,33], with widespread applications across various domains [34][35][36][37]. This approach enhances the flexibility of risk analysis and deepens the understanding of risk levels in complex systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Zhong,

Peng

2024

Buildings

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This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform a probabilistic analysis of quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the “low professional level of designers”, “poor production refinement”, and “poor storage conditions for finished products” have a significant impact on quality risks. This study offers a scientific risk assessment tool designed to address the quality control challenges commonly experienced in the manufacturing of steel structural components. Identifying the critical risk factors that influence quality empowers actual production enterprises to develop risk management strategies and improvement measures in a more focused manner, thereby facilitating more effective resource allocation and risk prevention and control. Consequently, this approach has a significant impact on enhancing the overall production level and quality within the industry.

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Section: Literature Reviewmentioning

confidence: 99%

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Zhong,

Peng

2024

Buildings

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“…In order to validate the developed BN, a method based on three axioms was used. This method was proposed by [59] and widely used by several researchers such as: [60], [61] and [62]. The principle of the three axioms is as follows [63]:…”

Section: F Model Validationmentioning

confidence: 99%

Evaluation of the Accidents Risk Caused by Truck Drivers using a Fuzzy Bayesian Approach

Benallou,

Azmani,

Azmani

2023

IJACSA

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Road accidents cause hundreds of fatalities and injuries each year; due to their size and operating features, heavy trucks typically experience more severe accidents. Many factors are likely to cause such accidents; however, statistics mainly blame human error. This paper analyses the risk of accidents for heavy vehicles, focusing on driver-related factors contributing to accidents. A model is developed to anticipate the probability of an accident by using Bayesian networks (BNs) and fuzzy logic. Three axioms were verified to validate the developed model, and a sensitivity analysis is performed to identify the factors that have the most significant influence over truck accidents. Subsequently, the result provided by the model was exploited to examine the effects of in-vehicle road safety systems in preventing road accidents via an event tree analysis. The results underlined a strong link between the occurrence of accidents and parameters related to the driver, such as alcohol and substance consumption, his driving style, and his reactivity. Similarly, unfavourable working conditions significantly impact the occurrence of accidents since it contributes to fatigue, one of the leading causes of road accidents. Also, the event tree analysis results have highlighted the importance of equipping trucks with these mechanisms.

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Quantitative assessment of CO₂ leakage risk in geologic carbon storage management

Jing,

Li,

Liu

et al. 2024

Greenhouse Gases

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Large‐scale geological storage of carbon dioxide (CO2) is indispensable for mitigating climate change but faces significant challenges, especially in the accurate quantitative assessment of leakage risks to ensure long‐term security. Given these circumstances, this paper proposes an innovative approach for quantitatively assessing CO2 leakage risk to address the previous limitations of limited accuracy and insufficient data. We construct a fault tree and transform it into a Bayesian network–directed acyclic graph, and then use judgment sets along with fuzzy set theory to obtain prior probabilities of root nodes. The feature, event, and process method was utilized to identify key components and subsequently determine the conditional probability table (CPT) of the leaf node. The subjective experience assessments from experts are defuzzified to obtain the CPTs of intermediate nodes. The obtained basic probability parameters are input into the directed acyclic graph to complete the model construction. After calculating the leakage probability using this model, it is combined with the severity of impacts to conduct a comprehensive risk assessment. Furthermore, critical CO2 risk sources can be determined through posterior probability calculations when intermediate nodes are designated as deterministic risk events. The gradual implementation process of the proposed model is demonstrated via a typical case study. The results indicate an overall CO2 leakage probability of 29%, with probabilities of leakage along faults/fractures, caprock, and well identified as 32%, 28%, and 19%, respectively. The project is categorized as a medium‐low risk level. When leakage is confirmed, tectonic movement, thickness, and delamination at interface connections/the presence of cracks are the critical risk sources, and measures to mitigate key risks are outlined. The identified key risk factors conform to empirical evidence and previous research, validating the accuracy of the model. This study is instrumental in CO2 geological storage risk assessment and scalable development program design. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

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Risk assessment of the Ship steering gear failures using fuzzy-Bayesian networks

Cited by 30 publications

References 48 publications

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Evaluation of the Accidents Risk Caused by Truck Drivers using a Fuzzy Bayesian Approach

Quantitative assessment of CO₂ leakage risk in geologic carbon storage management

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Risk assessment of the Ship steering gear failures using fuzzy-Bayesian networks

Cited by 30 publications

References 48 publications

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Evaluation of the Accidents Risk Caused by Truck Drivers using a Fuzzy Bayesian Approach

Quantitative assessment of CO2 leakage risk in geologic carbon storage management

Contact Info

Product

Resources

About

Quantitative assessment of CO₂ leakage risk in geologic carbon storage management