A fuzzy Bayesian network approach to improve the quantification of organizational influences in HRA frameworks

Li, Pengcheng; Chen, Guohua; Dai, Lei; Zhang, Li

doi:10.1016/j.ssci.2012.03.017

Cited by 131 publications

(48 citation statements)

References 34 publications

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“…Bayesian modeling is applicable for prediction, risk analysis, diagnosis, monitoring, reliability, and dependency [44] and it can act as a decision support system in government [45][46][47][48]. A Bayesian Network (BN) was developed to analyze the probabilistic causal relationship between the DPS components and actualize the study models.…”

Section: Modeling Toolmentioning

confidence: 99%

Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China

Seydehmet

Nurmemet

et al. 2018

Sustainability

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Significant anthropogenic and biophysical changes have caused fluctuations in the soil salinization area of the Keriya Oasis in China. The Driver-Pressure-State-Impact-Response (DPSIR) sustainability framework and Bayesian networks (BNs) were used to integrate information from anthropogenic and natural systems to model the trend of secondary soil salinization. The developed model predicted that light salinization (vegetation coverage of around 15-20%, soil salt 5-10 g/kg) of the ecotone will increase in the near term but decelerate slightly in the future, and that farmland salinization will decrease in the near term. This trend is expected to accelerate in the future. Both trends are attributed to decreased water logging, increased groundwater exploitation, and decreased ratio of evaporation/precipitation. In contrast, severe salinization (vegetation coverage of around 2%, soil salt ≥20 g/kg) of the ecotone will increase in the near term. This trend will accelerate in the future because decreased river flow will reduce the flushing of severely salinized soil crust. Anthropogenic factors have negative impacts and natural causes have positive impacts on light salinization of ecotones. In situations involving severe farmland salinization, anthropogenic factors have persistent negative impacts.

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Section: Modeling Toolmentioning

confidence: 99%

Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China

Seydehmet

Nurmemet

et al. 2018

Sustainability

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show abstract

“…Compared with actual engineering data, this data collection process and use of subjective data are more likely to cause random error and deviation, leading to inaccurate results. Li et al [20] stated that the lack of an accurate and comprehensive database is the main difficulty in the study of human error. In their study, the fuzzy Bayesian method was introduced to deal with a variable value, but still the basic data were the scores given by experts who estimated the conditional probability for each causal relationship, affecting the reliability of the results.…”

Section: Importance Of Workplace Environment In Effectivementioning

confidence: 99%

“…us, if a particular industry wants to understand its specific problems, it must collect data from scratch [34]. (2) Over-reliance on expert judgment: In the existing HRA method, the evaluation of factors that influence human errors (performance-shaping factors (PSFs)) and the probability of occurrence rely mainly on the subjective judgment of experts, which can easily cause a large random error [20,31,35,36] Despite these gaps, Liao et al [37] had proposed a human error model based on the CREAM that includes more variables that capture the characteristics of construction projects.…”

Section: Challenges Of Human Reliabilitymentioning

confidence: 99%

Estimating the Influence of Improper Workplace Environment on Human Error: Posterior Predictive Analysis

Liu

Shi

et al. 2018

Advances in Civil Engineering

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A model for identifying, analyzing, and quantifying the mechanisms for the influence of improper workplace environment on human error in elevator installation is proposed in this study. By combining a modification of a human error model with realworld inspection data collected by an elevator installation company, the influence paths of improper workplace environment on the conditional probability of human error were quantified using a Bayesian network parameter-learning estimation method and posterior predictive simulation. Under the condition of an improper workplace environment, the probability of human error increased by 80% of its original value, a factor much higher than that resulting from improper management. e most probable influence was found to be improper workmanship and changes in the information required by the worker, thus triggering cognitive failure and consequent unsafe actions by workers. e proposed methodology (posterior predictive simulation) provides a new approach in construction studies for quantifying the probabilistic levels of various causal paths, and the results show the key mechanism for the influence of improper workplace environment on human error using real-world mechanical installation data.

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“…i.e. : (Baraldi et al, 2009, Mosleh et al, 2012, Li et al, 2012, Groth and Swiler, 2013, Ekanem and Mosleh, 2014, Mkrtchyan et al, 2015. Causal traceability is a major need of HRA methods.…”

Section: A Concept For Model Quantificationmentioning

confidence: 99%

Framework for a Bayesian Network version of IDHEAS

Zwirglmaier¹,

Groth²,

Straub³

2015

Safety and Reliability of Complex Engineered Systems

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A fuzzy Bayesian network approach to improve the quantification of organizational influences in HRA frameworks

Cited by 131 publications

References 34 publications

Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China

Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China

Estimating the Influence of Improper Workplace Environment on Human Error: Posterior Predictive Analysis

Framework for a Bayesian Network version of IDHEAS

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