An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+’

Steijn, W.; Kampen, J.N. Van; Beek, D. van der; Groeneweg, Jop; Gelder, Pieter van

doi:10.1016/j.ssci.2019.104514

Cited by 29 publications

(20 citation statements)

References 22 publications

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“…The merits of nuclear energy have been re-discovered over recent years to address the future global energy needs [1] in an environmentally conscious and resource-sustainable way [2]. Since the nuclear renaissance [3] and the subsequent increase in nuclear newbuild projects [4] globally, new needs for safety assessment [5,6] of the complex nuclear power system has arisen. Securing a very high level of design and operating safety with reduced costs will not only be technically required for the burgeoning nuclear sector, but also from the policy [7] point of view to approach the challenge in addressing the public's negative perception [8] on nuclear power in the post-Fukushima era [9].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Fault-Event Trees Based Probabilistic Safety Analysis of a Boiling Water Nuclear Reactor’s Core Meltdown and Minor Damage Frequencies

2020

Safety

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A systematic probabilistic safety assessment for a boiling water nuclear reactor core is performed using fault trees and event trees analysis models. Based on a survey of the BWR’s safety systems against potential hazards, eight independent failure modes (initiating events) triggered scenarios are modelled and evaluated in the assembled fault-event trees, obtaining the two key outcome probabilities of interest, i.e., complete core meltdown (CCMD) frequency and minor core damage (MCD) frequency. The analysis results indicate that the complete loss of heat sink accounts for the initiating accident most vulnerable to CCMD (with a frequency of 1.8 × 10 − 5 per year), while the large break in the reactor pressure vessel is the least susceptible one (with a frequency of 2.9 × 10 − 12 per year). The quantitative risk assessment and independent review conducted in this case study contributed a reference reliability model for defense-in-depth core optimizations with reduced costs, informing risk-based policy decision making, licensing, and public understanding in nuclear safety systems.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Fault-Event Trees Based Probabilistic Safety Analysis of a Boiling Water Nuclear Reactor’s Core Meltdown and Minor Damage Frequencies

2020

Safety

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“…For the seamless integration of existing qualitative knowledge and quantitative knowledge, they utilized a Bayesian belief network. The resultant model provides an integrated and more accurate estimation of failure probabilities for both technological and human factors as well as the uncertainty surrounding such probability estimates [25]. Golestani et al proposed a methodology for quantifying the effects of harsh environmental conditions on the reliability of human actions in performing complex physical operations.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Human Performance Influencing Factors through LINMAP and Bayesian Belief Networks

et al. 2022

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This study aims to identify and rank the performance influencing factors (PIFs), which cause errors in human operations, by analyzing the failure weights and ranks of the tasks performed by every operator. Assessing these factors can mitigate human errors and improve safety, efficiency, and job satisfaction. The linear programming techniques for multidimensional analysis of preference (LINMAP) and Bayesian belief networks were employed to analyze an aircraft tire manufacturing industry. In this method, all operators of workshops were evaluated. According to the data analysis, each operator's tasks were weighted, and the potential error rate of each task was determined. PIFs for each workshop were then ranked and prioritized so that the most effective factors could easily be distinguished in order to identify the tasks where the operators had the highest rates of failure. The probability of human error was then obtained. In a predictive model, it is possible to determine when an error occurs and which factors are the most effective in its occurrence. This paper proposes an approach to the easy, inexpensive, and rapid classification of PIFs by determining their correlations through conditional possibilities. The proposed approach is capable of classifying not only PIFs but also the PIF-related tasks with the greatest effects.

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“…In high-reliability organizations, the SPAR-H method has been widely used due to its simplicity and practicability. SPAR-H was first used by U.S. nuclear power plants to assess HEPs [ 26 ], and is now also used in the petrochemical industry, such as the chemical plants [ 27 ], oil storage companies [ 28 ], and the deepwater drilling process [ 29 ]. Furthermore, Gould et al (2012) considered that SPAR-H was easier to use in comparison with other human risk analysis (HRA) models, such as HFACS [ 30 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H

Zhang

Meng

Yang

et al. 2022

IJERPH

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Unsafe crew acts (UCAs) related to human errors are the main contributors to maritime accidents. The prediction of unsafe crew acts will provide an early warning for maritime accidents, which is significant to shipping companies. However, there exist gaps between the prediction models developed by researchers and those adopted by practitioners in human risk analysis (HRA) of the maritime industry. In addition, most research regarding human factors of maritime safety has concentrated on hazard identification or accident analysis, but not on early warning of UCAs. This paper proposes a Bayesian network (BN) version of the Standardized Plant Analysis Risk–Human Reliability Analysis (SPAR-H) method to predict the probability of seafarers’ unsafe acts. After the identification of performance-shaping factors (PSFs) that influence seafarers’ unsafe acts during navigation, the developed prediction model, which integrates the practicability of SPAR-H and the forward and backward inference functions of BN, is adopted to evaluate the probabilistic risk of unsafe acts and PSFs. The model can also be used when the available information is insufficient. Case studies demonstrate the practicability of the model in quantitatively predicting unsafe crew acts. The method allows evaluating whether a seafarer is capable of fulfilling their responsibility and providing an early warning for decision-makers, thereby avoiding human errors and sequentially preventing maritime accidents. The method can also be considered as a starting point for applying the efforts of HRA researchers to the real world for practitioners.

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An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+’

Cited by 29 publications

References 22 publications

RETRACTED: Fault-Event Trees Based Probabilistic Safety Analysis of a Boiling Water Nuclear Reactor’s Core Meltdown and Minor Damage Frequencies

RETRACTED: Fault-Event Trees Based Probabilistic Safety Analysis of a Boiling Water Nuclear Reactor’s Core Meltdown and Minor Damage Frequencies

Assessing Human Performance Influencing Factors through LINMAP and Bayesian Belief Networks

A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H

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