Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

Liu, Yang; Ma, Xiaoxue; Qiao, Weiliang; Luo, Huiwen; Ping, He

doi:10.3390/ijerph19010297

Cited by 7 publications

(6 citation statements)

References 58 publications

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“…BN is a model used for characterizing variable dependencies, facilitating quantitative analysis of causal relationships between incident factors and aiding intervention selection ( 24 ). The composition of the BN is divided into a qualitative part and a quantitative part.…”

Section: Methodsmentioning

confidence: 99%

Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network

He,

Peng,

Luo

et al. 2024

Front. Public Health

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ObjectiveThis research investigates the role of human factors of all hierarchical levels in radiotherapy safety incidents and examines their interconnections.MethodsUtilizing the human factor analysis and classification system (HFACS) and Bayesian network (BN) methodologies, we created a BN-HFACS model to comprehensively analyze human factors, integrating the hierarchical structure. We examined 81 radiotherapy incidents from the radiation oncology incident learning system (RO-ILS), conducting a qualitative analysis using HFACS. Subsequently, parametric learning was applied to the derived data, and the prior probabilities of human factors were calculated at each BN-HFACS model level. Finally, a sensitivity analysis was conducted to identify the human factors with the greatest influence on unsafe acts.ResultsThe majority of safety incidents reported on RO-ILS were traced back to the treatment planning phase, with skill errors and habitual violations being the primary unsafe acts causing these incidents. The sensitivity analysis highlighted that the condition of the operators, personnel factors, and environmental factors significantly influenced the occurrence of incidents. Additionally, it underscored the importance of organizational climate and organizational process in triggering unsafe acts.ConclusionOur findings suggest a strong association between upper-level human factors and unsafe acts among radiotherapy incidents in RO-ILS. To enhance radiation therapy safety and reduce incidents, interventions targeting these key factors are recommended.

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Section: Methodsmentioning

confidence: 99%

Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network

He,

Peng,

Luo

et al. 2024

Front. Public Health

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“…For instance, according to the statistics of the China Association of the National Shipbuilding Industry (CANSI), in 2019, the shipbuilding industry and ship repair industry accounted for 65.4% and 4.2% of the overall business income in China, respectively. Compared with the figures of 2015, the proportion of these increased significantly [10]. It is important and urgent to reduce the rate of occupational accidents and accurately and efficiently promote industrial safety management.…”

Section: Introductionmentioning

confidence: 94%

Human-Related Hazardous Events Assessment for Suffocation on Ships by Integrating Bayesian Network and Complex Network

et al. 2022

Self Cite

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To investigate the human-related factors associated with suffocation on ships during docking repair, a comprehensive analysis model composed of a Bayesian network (BN) and a complex network (CN) is proposed in the present study. The principle of event tree analysis (ETA) is firstly applied to identify the hazardous events involved in the accident according to the accident report, based on which the CN would then be developed with the logic relationships among the hazardous events. The improved K-shell decomposition algorithm is utilized to determine the criticality of nodes in the CN, the results of which are then used to develop the BN model within the framework of a human factor analysis classification system (HFACS). Then, the developed BN model can be simulated with the probability distribution of all the nodes within the BN, which are obtained on the basis of node criticality. Finally, the results of the BN simulation are interpreted from the perspectives of a brief analysis, backward analysis and sensitivity analysis. The results are verified with existing studies and the accident investigation report issued by authority, which are presented as evidence to verify the effectiveness of the proposed methodology to evaluate the human-related risk involved in the suffocation on ships. The methodology proposed in this study integrates the advantages of BN and CN to investigate the human-related hazardous events involved in maritime accidents, which can be seen as the main innovation of this work.

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“…In this study, the fault tree method is used to construct a Bayesian network. Through the cause analysis of fault events, the basic events and intermediate events leading to the risk occurrence are found to build the fault tree, and the Bayesian network is generated from the fault tree [30]. Due to the conditional independence between basic events, the joint probability formula can be shown as in Equation (12).…”

Section: Basic Principles Of Bayesian Networkmentioning

confidence: 99%

A Coupled Mathematical Model of the Dissemination Route of Short-Term Fund-Raising Fraud

Shan

Wang

et al. 2022

Mathematics

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To effectively protect citizens’ property from the infringement of fund-raising fraud, it is necessary to investigate the dissemination, identification, and causation of fund-raising fraud. In this study, the Susceptible Infected Recovered (SIR) model, Back-Propagation (BP) neural network, Fault tree, and Bayesian network were used to analyze the dissemination, identification, and causation of fund-raising fraud. Firstly, relevant data about fund-raising fraud were collected from residents in the same area via a questionnaire survey. Secondly, the SIR model was used to simulate the dissemination of victims, susceptibles, alerts, and fraud amount; the BP neural network was used to identify the data of financial fraud and change the accuracy of the number analysis of neurons and hidden layers; the fault-tree model and the Bayesian network model were employed to analyze the causation and importance of basic events. Finally, the security measures of fund-raising fraud were simulated by changing the dissemination parameters. The results show that (1) for the spread of the scam, the scale of the victims expands sharply with the increase of the fraud cycle, and the victims of the final fraud cycle account for 12.5% of people in the region; (2) for the source of infection of the scam, the initial recognition rate of fraud by the BP neural network varies from 90.9% to 93.9%; (3) for the victims of the scam, reducing fraud publicity, improving risk awareness, and strengthening fraud supervision can effectively reduce the probability of fraud; and (4) reducing the fraud rate can reduce the number of victims and delay the outbreak time. Improving the alert rate can reduce victims on a large scale. Strengthening supervision can restrict the scale of victims and prolong the duration of fraud.

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Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

Cited by 7 publications

References 58 publications

Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network

Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network

Human-Related Hazardous Events Assessment for Suffocation on Ships by Integrating Bayesian Network and Complex Network

A Coupled Mathematical Model of the Dissemination Route of Short-Term Fund-Raising Fraud

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